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This article was published as a part of the Data Science Blogathon

Why do we need Deep Learning?

The problem

In traditional programming, the programmer formulates rules and conditions in their code that their program can then use to predict or classify in the correct manner. For example, normally to build a classifier, a programmer comes up with a set of rules, and program those rules. Then when you want to classify things, you give it a piece of data, and rules are used to select the category. This approach might be successful for a variety of problems. But remember NOT ALL! How? Let’s see.

Image classification is one such problem that cannot be resolved using the traditional programming method. Imagine how would you write the code for image classification? How could a programmer write rules and regulations to predict images they have never seen before?

Solution Found! What is it?

Deep learning is the solution to the above problem. It is a very efficient technique in pattern recognition by trial and error. With the help of a deep neural network, we can train the network by providing a huge amount of data and providing feedback on its performance, the network can identify, through a huge amount of iteration, its own set of conditions by which it can act in the correct way.

A SIMPLE MODEL: WITH ONE NEURON

Let’s begin understanding the neural network with the simplest model that can be made. The neuron is usually represented by circles with one arrow pointing outward and one inward.

Here, in the above diagram, ‘m’ is the slope of the line, ‘x’ is the input value and ‘b’ is the constant, for the equation, y = m.x + b.

Note the difference between ‘y_hat’ and ‘y’ variables. ‘y_hat’ is the prediction that we have made using the equation called estimated value whereas ‘y’ is the actual value or true value.

Let’s see how this simple model works! We will start assigning random values to ‘m’ (usually between -1 and 1) and ‘b’, and then calculate ‘y_hat’ with the ‘x’ as input values.

In the above figure, (x, y_hat) is plotted and a regression line is drawn. Since ‘m’ and ‘b’ were given random values, there is supposed to be an error in the estimated value from the true value. Thus, the error is calculated as the average of the square of the difference between the expected value and true value i.e.

Here MSE (mean squared error) is being used but one can also use RMSE i.e. root mean squared error, which is nothing but the root of MSE.

MSE basically indicates how far is the expected value from the target/ true value. If MSE is plotted on a 3D graph, it looks like the image given below:

The aim of any model is to minimize the error. Hence, the minimum of the curve is at the bottom of the curve. In order to reach there, we have to use calculus for that. Gradient Descent is the method for finding out the minimum of this loss curve. It looks like the following graphs:

So how it works? The computer doesn’t have an eyeball like humans which does not have the ability to guestimate the path to reach the target variable from the current position. It calculates the gradient, which is the fancy word to describe multivariate slope which figures out the direction of slope, which is some ratio of the variable ‘b’ to ‘m’.

Now the direction to travel is known. The next step is to determine how far it has to travel to reach the target point. This is known as the ‘learning rate’. Usually, the learning rate is not taken to be a bigger value as it will mean that one is moving away from the target. A too-small learning rate is also a problem since it will take a longer time for computation. There are a few ways to measure the progress of the algorithm. There are epochs which mean a model update with the full dataset. Instead of epochs, nowadays people use a more efficient technique that is batch which means the sample of the full dataset. In either case, the step is either the full dataset or a batch, calculate the gradient, and update the parameters with the gradient and learning rate. Thus, the process is continued till the target value is achieved. This is how gradient descent works!

BUILDING A NETWORK: FROM NEURONS TO NETWORK

in the earlier example, only one input was considered for the neuron. Now, more inputs can be considered for one neuron. From now onwards, instead of having single x input to the neuron with slope ‘m’, multiple inputs (i.e. x1,x2,x3…so on) will be considered with their respective weights (i.e. w1, w2, w3…so on). Not only this, there is one more interesting thing! The output of one neuron can be used as input to another neuron. This is applicable as long as it does not make a loop. When an error is calculated from new weights using gradient descent an error calculated from the later neuron can be used as part of the error of the previous neuron it’s connected to.

Gotcha! Now is the right time to introduce the concept of the Activation Function here.

ACTIVATION FUNCTIONS

There are only three types of activation functions i.e. Linear, ReLU, and Sigmoid.

Linear Activation Function is one of the basic activation functions. This is used widely because it is fast, as it is linear in nature and hence easy to differentiate. The graph and equation of this function look like the images shown below:

One easy way to add non linearity is to feed the equation of a line into another non-linear function. The most popular function is ReLU stands for Rectified Linear Unit. It is one of the fancy ways of saying that whenever the output of the line is negative, it is going to set it to 0. It can be represented as follows:

Another activation function is the Sigmoid function. This is an ‘S’ plot curve that goes from 0 to 1. This is the most prominent function and is widely in Logistic Regression. The result of this function gives the probability which has to lie between 0 to 1.

OVERFITTING

Why shouldn’t the programmer make a big neural network and solve every problem? Can one think of the pitfalls of having a large model with lots of neurons?

One problem is that there will be a lot of wastage of computational resources, and the other could be the longer time to train the model. Ugh! There is one more point.

The problem is related to classical statistics. Let’s say, two models have been built, as shown below:

Which of the two models proves to be the best? One that is located on the left side. Why? look at the RMSE value. The left model has a better RMSE value than the right one. But that is not true! Why? Let’s see.

What if we get new sample data that the model has not already seen by the model? The left model would give a very poor result i.e. the RMSE will be very high as compared to the right side model. This is because the left side model has memorized the points and thus provided excellent RMSE during training. On the other hand, the right-side model is more generalized. Thus, the left hand side model is said to be the overfitted model. Thus, the choice depends on the Data Scientist to choose the type of model suitable with respect to their needs.

Now let’s create one project to understand the concepts much better :

Project Name: American Sign Language Dataset

Objectives:

1. Prepare the image data and use it for training the model

2. Create and compile the basic model for image classification

Project begins:

American Sign Language Dataset is a very common dataset and can be found on Kaggle for practice. It consists of 26 letters out of which the letters ‘j’ and ‘k’ are not considered in the training because they require movement. Their classification is beyond the scope of this guide.

Let’s start with the coding part!

Loading the Data

Reading in the Data: The sign dataset, that has been downloaded from Kaggle, is in the CSV (stands for comma-separated value) format. CSV file has rows and columns which consists of labels mentioned at the top. One can check the difference between the CSV file and XLSX file by opening them in the text (.txt) format on a usual notepad. In the CSV file, the values in a row are separated by commas.

import pandas as pd train_df = pd.read_csv("sign_mnist_train.csv") valid_df = pd.read_csv("sign_mnist_valid.csv") Exploring the Data

Now it’s time to visualize the data. The data can be visualized by using the head method of the pandas data frame. Each row has some integer values which are nothing but the image pixel’s intensity. The data has a column labeled mentioned which refers to the true value of each image.

train_df.head()

Output:

Extracting the Labels

Now, the training and validation labels will be stored in a variable called y_train and y_label variables. The code for their construction can be referred to below:

y_train = train_df[‘label’]

y_valid = valid_df['label'] del train_df['label'] del valid_df['label']

Extracting the Images

Now, previously labels were stored in the variable. Now, the training and validation images dataset will be stored in a variable called x_train and x_valid respectively.

x_train = train_df.values x_valid = valid_df.values

Summarizing the Training and Validation Data

Now, the program has 27,455 images with 784 pixels each for training…

x_train.shape

…as well as their corresponding labels:

y_train.shape

For validation, it has 7,172 images…

x_valid.shape

…and their corresponding labels:

y_valid.shape

Visualizing the Data

To visualize the images, now use the matplotlib library. Here there is no need to worry about the details of this visualization.

Note that data will have to be reshaped from its current 1D shape of 784 pixels to a 2D shape of 28×28 pixels to make sense of the image:

mport matplotlib.pyplot as plt plt.figure(figsize=(40,40)) num_images = 20 for i in range(num_images): row = x_train[i] label = y_train[i] image = row.reshape(28,28) plt.subplot(1, num_images, i+1) plt.title(label, fontdict={'fontsize': 30}) plt.axis('off') plt.imshow(image, cmap='gray') Normalize the Image Data

Deep learning models are much better and very efficient at dealing with floating-point numbers between 0 and 1. The Conversion of integer values to floating-point values between 0 and 1 is called normalization. Normalization is a very essential concept that one should be aware of.

Now it’s time to normalize the image data, meaning that their pixel values, instead of being between 0 and 255 as they are currently:

x_train.min()

x_train.max()

…should be floating-point values between 0 and 1. It is coded as follows:

x_train = x_train / 255 x_valid = x_valid / 255 Categorize the Labels

What is categorical encoding?

Consider the case, if someone asks, what is 7-2? If you said that digit 4 is much closer to the answer than digit 9. Unfortunately, this does not happen in neural networks used for image classification. The neural networks should not have this kind of reasoning and it should clearly make a difference that guessing the image as 4, which is actually 5, is equivalently bad as guessing 9.

We can see that images are labeled between 0 to 9. Since these are numerical values, the model thus created should try to categorize them perfectly into ten categories.

Since it was previously mentioned that labels are values, it is required for them to get converted into categories. For this, category encoding is used, which modifies the data so that each label belongs to its possible category. The keras.utils.to_categorical method will be used to accomplish this.

import tensorflow.keras as keras num_classes = 24 y_train = keras.utils.to_categorical(y_train, num_classes) y_valid = keras.utils.to_categorical(y_valid, num_classes)

Build the Model

The data is all prepared, we have normalized images for training and validation, as well as categorically encoded labels for training and validation.

With the training data that we prepared earlier, it is now time to create the model that will train with the data. This first basic model will be made up of several layers and will be comprised of 3 main parts:

An input layer, the layer which receives all data as input

Several hidden layers, each made up of many neurons. Each neuron will have its weight associated with it such that it will affect the performance and accuracy of the model.

An output layer, which will depict the network’s guess for a given image

The units argument specifies the number of neurons in the layer. The activation function concept has already been taught above.

from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense model = Sequential() model.add(Dense(units = 512, activation='relu', input_shape=(784,))) model.add(Dense(units = 512, activation='relu')) model.add(Dense(units = num_classes, activation='softmax'))

Summarizing the Model

The below code will give the way to summarize the model:

model.summary()

Compiling the Model

Time to compile our model with the same options, using categorical cross-entropy to ensure that we want to fit into one of many categories, and measure the accuracy of our model:

Train the Model

Use the model’s fit method to train it for 20 epochs using the training and validation images and labels created above:

model.fit(x_train, y_train, epochs=20, verbose=1, validation_data=(x_valid, y_valid))

Now one can see the training of the model for 20 epochs. The accuracy turns out to be 97% approximately which is quite good.

So, this was all about Deep learning techniques. I hope you enjoyed the guide and have been able to give a head start with deep learning.

All the best! Stay tuned for more articles and guide on Analytics Vidhya!

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You're reading A Guide On Deep Learning: From Basics To Advanced Concepts

Learning The Basics Of Deep Learning, Chatgpt, And Bard Ai

Introduction

Artificial Intelligence is the ability of a computer to work or think like humans. So many Artificial Intelligence applications have been developed and are available for public use, and chatGPT is a recent one by Open AI.

ChatGPT is an artificial intelligence model that uses the deep model to produce human-like text. It predicts the next word in a text based on the patterns it has learned from a large amount of data during its training process. Bard AI is too AI chatbot launched by google and uses recent work so can work to answer real-time questions.

We will discuss chatGPT and Bard AI and the difference between them.

Learning Objectives

1. Understanding the Deep Learning Model and chatGPT.

2. To understand the difference between chatGPT and Bard.

This article was published as a part of the Data Science Blogathon.

Understanding the Deep Learning Model

Artificial Intelligence is a broad term in today’s world to do everything and behave like a human. When we talk about the algorithm, we are, in other words, talking about a subset of Artificial Intelligence, Machine learning.

Machine learning algorithms look at the past behavior of humans and predict it based on past behavior. When we go further deep, some patterns are adapted or learned themselves when the situation is different. “Deep Learning” further deep algorithms, following the footsteps of neural networks.

“Deep Learning Algorithm” is classified into two Supervised and Unsupervised. “Supervised Learning” is divided into Convolutional Neural Network (CNN) and Recurrent neural networks.

In supervised learning, the data given in input is labeled data. In Unsupervised learning, the data is unlabeled and works by finding patterns and similarities.

Artificial Neural Network (ANN)

Similarly, like a human brain, an input layer, one or more hidden layers, and an output layer make up the node layers of artificial neural networks (ANNs). There is a weight and threshold associated with each artificial neuron or node. When a node’s output exceeds a predetermined threshold, it is activated and sends data to the next layer. Otherwise, no data reaches the next layer.

After an input layer, weights get added. Larger weights contribute more to the output than other inputs. The mass of the input layer gets multiplied, and then the results are added up. After that, the output result is by the activation function, which decides what to do with it. The node is activated if that output exceeds a certain threshold, transmitting data to the next layer. As a result, the input layer of the next layer consists of the output return of the past one and is thus named feed-forward.

Let’s say that three factors influence our decision, and one of the questions is if there is a rainy day tomorrow, and if the answer is Yes, it is one, and if the response is no is 0.

Another question will there be more traffic tomorrow? Yes-1, No -0.

The last question is if the beachside will be good for a picnic. Yes-1, No-0.

We get the following responses.

where

– X1 – 0,

– X2 – 1,

– X3 – 1

Once the input is assigned, we look forward to applying weight. As the day is not rainy, we give the mass as 5. For traffic, we gave it as 2, and for a picnic as 4.

W1 – 5

W2 – 2

W3 – 4

The weight signifies the importance. If the weight is more, it is of the most importance. Now we take the threshold as 3. The bias will be the opposite value of the threshold -3.

y= (5*0)+(1*2)+(1*4)-3 = 3.

Output is more than zero, so the result will be one on activation. Changes in the weights or threshold can result in different returns. Similarly, neural networks make changes depending on the results of past layers.

For example, you want to classify images of cats and dogs.

The image of a cat or dog is the input to the neural network’s first layer.

After that, the input data pass through one or more hidden layers of many neurons. After receiving inputs from the layer before it, each neuron calculates and sends the result to the next layer. When determining which characteristics, the shape of the ears or the patterns, set apart cats from dogs, the neurons in the hidden layers apply weights and biases to the inputs.

The probability distribution of the two possible classes, cat and dog, is the return for final layers, and prediction ranks higher than probability.

Updating weights and biases is termed backpropagation, and it improves at the time in pattern recognition and prediction accuracy.

Facial Recognization by Deep Learning

We will use animal faces to detect digitally based on a convolutional.

from tensorflow.keras.models import Sequential from tensorflow.keras.layers import * from tensorflow.keras.models import Model from tensorflow.keras.applications import InceptionV3 from tensorflow.keras.layers import Dropout, Flatten, Dense, Input from tensorflow.keras.preprocessing.image import ImageDataGenerator import numpy import pandas import matplotlib.pyplot as plt import matplotlib.image as mpimg import pickle from sklearn.model_selection import train_test_split from sklearn.metrics import classification_report import patoolib patoolib.extract_archive('animals.zip') plt.imshow(image) train_data = ImageDataGenerator(rescale = 1./255) test_data = ImageDataGenerator(rescale = 1./255) train_dir= ("C://Users//ss529/Anaconda3//Animals//train") val_dir = ("C://Users//ss529/Anaconda3//Animals//val") train_generator = train_data.flow_from_directory( train_dir, target_size =(150, 150), batch_size = 20, class_mode ='binary') test_generator = test_data.flow_from_directory( val_dir, target_size =(150, 150), batch_size = 20, class_mode ='binary') from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense,Flatten model = Sequential() model.add(Flatten(input_shape=(150, 150,3))) model.add(Dense(4,activation='sigmoid')) model.add(Dense(5,activation='relu')) model.add(Dense(3,activation='softmax')) model.summary() opt = tf.keras.optimizers.Adam(0.001) model.fit(train_generator,epochs=5, validation_data= test_generator) What is ChatGPT?

An up-to-date Artificial Intelligence chatbot, trained by Open AI, developed on Azure that answers your queries, except for mistakes, corrects the code, and can reject unnecessary demands. It depends on a Generative pre-trained transformer equipment GPT 3.5, which uses Artificial or complex work to approach and make out with words.

ChatGPT, which stands for chat-based Generative Pre-trained transformer, is a potent tool that works in different ways to increase output in several distinct areas.

ChatGPT is intelligent to solve simple math problems and answer query-related technical or even some jokes.

For example, the image below shows some funny jokes generated by AI.

In another example, the image below shows to find the area of a triangle with the help of AI.

How to Use ChatGPT?

Here we are going to answer some questions related to chatGPT.

Anyone can use ChatGPT for free. One can sign up and log in using google or email. The free version of ChatGPT is open to the general as of the writing date of February 2023.

“ChatGPT Plus” is a paid subscription plan. It gives priority access to new features, faster response times, and reliable availability when demand is high.

For example, I asked some business and idea tips on Data Science, and here is the response provided by chatGPT in the below image.

Why Should we Use chatGPT?

chatGPT can give you the best services based on how you want to use a chatbot for your benefit.

It can write for your document or reports.

It is possible to save time and allow messages straight given and professionally by using ChatGPT to generate personalized and engaging responses.

It can help generate new business ideas that assist business leaders and entrepreneurs with original and creative concepts for new projects, schemes, and services.

ChatGPT can come in handy for detection and correction in existing code.

Limitations Of ChatGPT

ChatGPT does not so far shows 100% accuracy.

For example, for the question about Male Rao Holkar’s death, the response from chatGPT is not similar to the history.

Edward Tiann, a 22 years old student from Princeton University, developed the GPTZero application that can detect plagiarism with the contents texted by AI. It is so far for educational use, and the beta version is ready for public use.

What is Bard AI?

LaMDA (Language Model for Dialogue Applications) powers Bard, an experimental conversation AI service. To respond to queries in a new and high-quality way, it uses data from the Internet.

How does Bard function?

LaMDA, a large language model created by Google and released in 2023, powers Bard. Bard is made available by Google on a thin-weight version of LaMDA, which requires less computing power to run, allowing it to reach a maximum number of users.

The Difference Between ChatGPT and Bard

Google Bard AI and chatGPT are the chatbots that use AI for a chat.

ChatGPT is available and open to the public. Bard is limited to beta testers and not for public use.

For chatGPT service has paid and free options. Bard service is available for free.

Bard uses the langauge model developed by google in 2023 and that of chatGPT, a pre-trained transformer.

ChatGPT has a GPT -2 Output detector that detects plagiarism, and Bard has not.

ChatGPT will search for texts and sources that did exist in 2023. Bard on recent sources that can fetch more data. The Google search engine will undergo some settings to let Bard AI answer.

Frequently Asked Questions

Q1. What algorithm does the ChatGPT use?

A. ChatGPT is built on the GPT-3.5 architecture, which utilizes a transformer-based deep learning algorithm. The algorithm leverages a large pre-trained language model that learns from vast amounts of text data to generate human-like responses. It employs attention mechanisms to capture contextual relationships between words and generate coherent and contextually relevant responses.

Q2. How is ChatGPT programmed?

A. ChatGPT is programmed using a combination of techniques. It is built upon a deep learning architecture called GPT-3.5, which employs transformer-based models. The programming involves training the model on a massive amount of text data, fine-tuning it for specific tasks, and implementing methods for input processing, context management, and response generation. The underlying programming techniques involve natural language processing, deep learning frameworks, and efficient training and inference pipelines.

Conclusion

ChatGPT is a new chatbot AI that surprised the world with its unique features to answer, solve problems, and detect mistakes.

Some of the key points we learned here

ChatGPT, a new chatbot developed by Open AI, is the new google. For the question’s answers, we usually searched on google to find the answer can be done now on chatGPT, but still, it has less than 100% accuracy.

ChatGPT works on deep learning models.

Brad AI, developed by google in competition with chatGPT, will soon reach the public.

We will use animal faces to detect digitally based on a convolutional.

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A Basic Introduction To Activation Function In Deep Learning

This article was published as a part of the Data Science Blogathon.

Introduction

The activation function is defined as follows: The activation function calculates a weighted total and then adds bias to it to decide whether a neuron should be activated or not. The Activation Function’s goal is to introduce non-linearity into a neuron’s output.

A Neural Network without an activation function is basically a linear regression model in Deep Learning, since these functions perform non-linear computations on the input of a Neural Network, enabling it to learn and do more complex tasks. Therefore, studying the derivatives and application of activation functions, also as analysing the pros and drawbacks of each activation function, is essential for selecting the proper type of activation function that may give non-linearity and accuracy in a particular Neural Network model.

We know that neurons in a Neural Network work following their weight, bias, and activation function. We would change the weights and biases of the neurons in a Neural Network based on the output error. Back-propagation is the term for this process. Because the gradients are supplied simultaneously with the error to update the weights and biases, activation functions, therefore, enable back-propagation.

Table of Contents

Why Do We Need Activation Functions in CNN?

Variants Of Activation Function

Python Code Implementation

Conclusion

Why do we need it?

Non-linear activation functions: Without an activation function, a Neural Network is just a linear regression model. The activation function transforms the input in a non-linear way, allowing it to learn and as well as accomplish more complex tasks.

Mathematical proof:-

The diagram’s elements include:- A hidden layer, i.e. layer 1:- A visible layer, i.e. layer 2:- A visible layer, i.e.

a(1) = z(1)

= W(1)X + b(1) (1)

Here,

Layer 1’s vectorized output is z(1).

W(1) denotes the vectorized weights (w1, w2, w3, and w4) applied to hidden layer neurons, X denotes the vectorized input features (i1 and i2), and b denotes the vectorized bias (b1 and b2).

Any linear function has a(1) being vectorized form.

(Note that the activation function is not taken into consideration here.)

The output layer, or layer 2, is as follows:

Layer 2 input is z(2) = W(2)a(1) + b(2) a(2) = z(2)a(1) + b(2) a(2) = z(2)a(1) + b(2) a(2) = z(2)a(1) + b(2) a(2) = z(2)a(1) + b(2) a(2) (2)

Output layer calculation:

lue of z(1).

(W(2) * [W(1)X + b(1)]) + b = (W(2) * [W(1)X + b(1)]) (2)

[W(2) * W(1)] = z(2) * [W(2)*b(1) + b(2)] + X

Let,

W = [W(2) * W(1)]

b = [W(2)*b(1) + b(2)]

z(2) = W*X + b is the final result.

Which is a linear function once again.

Even after applying a hidden layer, this observation yields a linear function, hence we can deduce that no matter how many hidden layers we add to a Neural Network, all layers will behave the same way because the combination of two linear functions yields a linear function.

1). Linear Function: –

• Equation: The equation for a linear function is y = ax, which is very much similar to the equation for a straight line.

• -inf to +inf range

• Applications: The linear activation function is only used once, in the output layer.

Source: V7labs

• Problems: If we differentiate a linear function to introduce non-linearity, the outcome will no longer be related to the input “x” and the function will become constant, hence our procedure will not show any behaviour.

For example, determining the price of a home is a regression problem. Because the price of an apartment might be a large or little number, we can employ linear activation at the output layer. Even in this case, any non-linear function at the hidden layers of the Neural Network is required.

2) The sigmoid function:

• It’s a function that is being plotted in the form of ‘S’ Shape.

• Formula: A = 1/(1 + e-x)

• Non-linear in nature. The values of X ranges from -2 to 2, but the Y values are highly steep. This indicates that slight changes in x will result in massive changes in Y’s value.

• 0 to 1 value of the range

3). Tanh Function: Tanh function, also identified as Tangent Hyperbolic function, is an activation that almost always works better than sigmoid function. It’s simply a sigmoid function that has been adjusted. Both are related and can be deduced from one another.

• Equation: f(x) = tanh(x) = 2/(1 + e-2x) – 1 OR tanh(x) = 2 * sigmoid(2x) – 1 OR tanh(x) = 2 * sigmoid(2x) – 1

• Range of values: -1 to +1

• Uses:- Usually employed in hidden layers of a Neural Network since its values change from -1 to 1, causing the hidden layer’s mean to be 0 or very close to it, which aids in data centring by bringing the mean close to 0. This makes learning the next layer much more straight.

4). RELU (Rectified linear unit) is the fourth letter in the alphabet. It’s the most used activation method. Hidden layers of neural networks are primarily used.

• Formula: A(x) = maximum (0,x). If x is positive, it returns x; else, it returns 0.

• Value Range: (inf, 0)

• Non-linear in nature, which means simply backpropagating errors and also having the ReLU function activating many layers of neurons.

• Applications: Because it includes fewer mathematical operations, ReLu is less computationally expensive than tanh and sigmoid. Only a few neurons are active at a time, making the network scarce and efficient for computation.

Simply put, the RELU function learns much faster than the sigmoid and Tanh functions.

5). Softmax Function: The softmax function is a type of sigmoid function that comes in handy when dealing with categorization issues.

• Non-linearity in nature

• Uses: Typically utilised when dealing with many classes. The softmax function would divide by the sum of the outputs and squeeze the outp

• Output: The softmax function is best used in the classifier’s output layer, where we’re trying to define the class of each input using probabilities.

Selecting The Right Activation Function

If one is unsure about the activation function to utilise, just select RELU, which is a broad activation function that is used in most circumstances these days. If our output layer is meant to be used for binary identification/detection, the sigmoid function is an obvious choice.

import numpy as np #designing the function for sigmoid

fig, ax = plt.subplots(figsize=(9, 5))

#Axis spines are basically the lines that are confining the given plot area ax.spines['left'].set_position('center') ax.spines['right'].set_color('none') ax.spines['top'].set_color('none') axxaxis.set_ticks_position('bottom') ax.yaxis.set_ticks_position('left') # Create and show the sigmoid plot

ax.plot(x,sigmoid(x)[1], color=”#9621E2″, linewidth=3, label=”derivative”)

#placing the legend on the upper right corner of the axes ax.legend(loc="upper right", frameon=False) fig.show()

Output, Source: Author

Conclusion

Intuition Behind Perceptron For Deep Learning

Introduction

Perceptron is one of the most fundamental concepts of deep learning which every data scientist is expected to master. It is a supervised learning algorithm specifically for binary classifiers.

Note: If you are more interested in learning concepts in an Audio-Visual format, We have this entire article explained in the video below. If not, you may continue reading.

In this article, we will develop a solid intuition about Perceptron with the help of an example. Without any further delay, let’s begin!

Before you continue, I recommend you check out the following article-

Deep Learning 101: Beginners Guide to Neural Network

Understanding Perceptron with an Example

Let’s start with a simple example of a classification problem. Our aim here is to predict whether the loan should be approved or not, depending on the salary of a person.

In order to do that will need to build a model that takes the salary of the person as an input and predicts whether the loan should be approved for the person or not.

Suppose your bank wants to reduce the risk of loan default and hence decides to roll out loans to only such individuals who have a monthly salary of 50000 and above.

In this case, we want our model to learn to check whether the salary input which is represented as X here, is greater than 50000 or not. Here are the tasks we want our model to perform-

The first thing is it should take in the salary as input.

Next, it has to check whether the given salaries are greater than 50,000 or not.

And if the salary’s more than 50,000 only then give output as “Yes”.

Effectively, this model takes in some input, processes it, and generates an output. This is similar to what happens in a biological neuron:

It takes the input to the dendrite, processes the provided information, and generates the output. You can see the similarity right? Thus the model that we’re talking about can also be called a Neuron.

Now coming back to our loan example. Let’s have a closer look at each of these tasks, starting from the input. We have a single input which is salary but in general, we can have multiple features just like the applicant’s salary, his/her father’s salary, spouse’s salary that can be deciding factors to approve the Loan. And our neurons will take in all of these features as input in order to make decisions. This is to say that the neuron will have multiple inputs. This is similar to the multiple dendrites that we saw in the biological neuron.

Now that there are multiple salary features about the person we’ll take all of them into account as they represent the total income of the household. We can sum them up and check if the total income of the household crosses the threshold or not.

So, the Total Income = Applicant Salary(X1) + Father Salary(X2) + Spouse Salary(X3)

We need to compare this Total Income with the Threshold. Here is the equation representing the same:

We have X1, X2, and X3 as input features and we want to check if their sum crosses the particular threshold, which is 50000 in our case. Now if you bring this threshold to the left side of the equation it will become something like this:

and if we represent this whole quantity which is “- threshold” with a new term Bias, the updated equation would look something like:

this will have the sum of four quantities which are X1, X2, and X3, and note that the bias is actually “- threshold”.

Now this quantity which is Bias, although we have selected it arbitrarily here, it is actually something that neuron learn from the underlined data. If the input exceeds the magnitude of the bias, we want the neuron to give the output as “YES”. That means the loan can be approved by this person. This event is known as the firing of a neuron. If want to write this relationship using equations, we can use the following equations:

We will say that output should be 1 when this equation is true and output should be 0 in all other cases. These two equations can be represented in the form of a function. Let’s see how:

So here we have the sum of the features X1, X2, X3, and bias represented as Z and we want our output to be 1 if the Z is greater than 0, otherwise 0.

So we can use a Step Function here and this is the graph of the step function:

It basically gives us the output 1 for any value greater than zero and gives an output 0 for any value less than zero. So in order to find output, we will apply the step function on Z here. We have denoted this step function as following;

This step function in this case is used to scale the output of the neuron and in Deep Learning we have an option of choosing such functions to apply to the output of the neurons. They are known as Activation Functions. So when we use the step function as the activation function for a neuron it is called a Perceptron.

End Notes

In this article, we saw how a perceptron model works. It takes in multiple features like applicant salary, father salary, etc. as input and checks whether the sum of these, which is the total income of the household, exceeds the threshold or not. If it does only then it will give an output as one, which means the loan should be approved, otherwise the step function will give an output as zero.

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Music Genres Classification Using Deep Learning Techniques

This article was published as a part of the Data Science Blogathon

Introduction:

In this blog, we will discuss the classification of music files based on the genres. Generally, people carry their favorite songs on smartphones. Songs can be of various genres. With the help of deep learning techniques, we can provide a classified list of songs to the smartphone user. We will apply deep learning algorithms to create models, which can classify audio files into various genres. After training the model, we will also analyze the performance of our trained model.

Dataset:

We will use GITZAN dataset, which contains 1000 music files. Dataset has ten types of genres with uniform distribution. Dataset has the following genres: blues, classical, country, disco, hiphop, jazz, reggae, rock, metal, and pop. Each music file is 30 seconds long.

Process Flow:

Figure 01 represents the overview of our methodology for the genre classification task. We will discuss each phase in detail. We train three types of deep learning models to explore and gain insights from the data.

Fig. 01

First, we need to convert the audio signals into a deep learning model compatible format. We use two types of formats, which are as follows:

1. Spectrogram generation:

A spectrogram is a visual representation of the spectrum signal frequencies as it varies with time. We use librosa library to transform each audio file into a spectrogram. Figure 02 shows spectrogram images for each type of music genre.

Fig. 02

2. Wavelet generation: –

The Wavelet Transform is a transformation that can be used to analyze the spectral and temporal properties of non-stationary signals like audio. We use librosa library to generate wavelets of each audio file. Figure 03 shows wavelets of each type of music genre.

Fig. 03

3, 4. Spectrogram and Wavelet preprocessing

From Figure 02 and 03, it is clear that we treat our data as image data. After generating spectrograms and wavelets, we apply general image preprocessing steps to generate training and testing data. Each image is of size (256, 256, 3).

5. Basic CNN model training:

After preprocessing the data, we create our first deep learning model. We construct a Convolution Neural Network model with required input and out units. The final architecture of our CNN model is shown in Figure 04. We use only spectrogram data for the training and testing.

Fig. 04

We train our CNN model for 500 epochs with Adam optimizer at a learning rate of 0.0001. We use categorical cross-entropy as the loss function. Figure 05 shows the training and validation losses and model performance in terms of accuracy.

Fig. 05

6. Transfer learning-based model training

We have only 60 samples of each genre for training. In this case, transfer learning could be a useful option to improve the performance of our CNN model. Now, we use the pre-trained mobilenet model to train the CNN model. A schematic architecture is shown in Figure 06.

Fig. 06

The transfer learning-based model is trained with the same settings as used in the previous model. Figure 07 shows the training and validation loss and model performance in terms of accuracy. Here, also we use only spectrogram data for the training and testing.

Fig. 07

7. Multimodal training

We will pass both spectrogram and wavelet data into the CNN model for the training in this experiment. We are using the late-fusion technique in this multi-modal training. Figure 08 represents the architecture of our multi-modal CNN model. Figure 09 shows the loss and performance scores of the model with respect to epochs.

Fig. 08 Fig. 09

Comparison:

Figure 10 shows a comparative analysis of the loss and performance of all three models. If we analyze the training behavior of all three models, we found that the basic CNN model has large fluctuations in its loss values and performance scores for training and testing data. The multimodal model has shown the least variance in performance. Transfer learning model performance increases gradually compared to multimodal and basic CNN models. Validation loss value shot up suddenly after the 30 epochs. On the other side, validation loss decreases continuously for the other two models.

Fig. 10

Testing the models

After training our models, we test each model on the 40% test data. We calculate precision, recall, and F-score for each music genre (class). Our dataset is balanced; therefore, the macro average and weighted average of precision, recall, and F-score are the same.

1. Basic CNN model

Figure 11 presents the results of our CNN model on the test data. CNN model was able to classify “classical” genre music with the highest F1-score. CNN performed worst for “Rock” and “reggae” genre music. Figure 12 shows the confusion matrix of the CNN model on the test data.

Fig. 11

Fig. 12

2. Transfer learning based model

We used the transfer learning technique to improve the performance of genre classification. Figure 13 presents the results of the transfer learning-based model on test data. F1-score for “hiphop”, “jazz”, and “pop” genres increased due to transfer learning. If we look at overall results, we have achieved only a minor improvement after applying transfer learning. Figure 14 shows the confusion matrix for the transfer learning model on the test data.

Fig. 13

Fig. 14

3. Multimodal-based model: We have used both spectrogram and wavelet data to train the multimodal-based model. In the same way, we perform the testing. We have found very surprising results. Instead of improvement, our performance reduced drastically. We have achieved only 38% of F1-score while using a multi-modal approach. Figure 16 shows the confusion matrix of the multimodal-based model on the test data.

Fig. 15 Fig. 16

Conclusion:

In this post, we have performed music genre classification using Deep learning techniques. The transfer learning-based model has performed best among all three models. We have used the Keras framework for the implementation on the google Collaboratory platform. Source code is available at the following GitHub link along with spectrogram and wavelet data on google drive. You don’t need to generate spectrograms and wavelets from the audio files.

GitHub Link. . Spectrogram and wavelets data link.

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A Guide To Making Work From Home Easier

The pros and cons of working from home

You will save time and money!

Flexible work hours can be a pro and a con Social interactions reduce (but that’s not a good thing)

Casual conversations over coffee in the break room and other day-to-day office interactions go away when you work from home. You can focus on your work instead and get things done without unnecessary distractions and interruptions.

However, everyday face-to-face interactions are a great way to collaborate, bounce ideas off of colleagues, or simply take a break from the stress. If you suddenly have to work from home, this is something you’ll miss dearly. You must go out of your way to set up “meetings” when at home, and it isn’t nearly the same.

The technology can take some getting used to

Zoom. Skype. Google Meet. Microsoft Teams. These are just a handful of the apps everyone has become intensely familiar with over the past six months. Installing these apps isn’t the only thing you need to do, however.

You have to set up and make sure that the webcam and mic are working, practically before every call. You will likely need good lighting and to make sure that there isn’t any distracting background noise. There’s just a whole lot to do now that you might never have had to think about before.

Does home insurance cover work from home?

Home insurance is property insurance covering any damages to assets inside the home. The right home insurance plan can cover everything, from your furniture to your electronic gadgets.

With more and more people working from home and likely using office devices, what your home insurance covers is a little murky. Overall, most people that are working from home now will fall under one of three categories.

Working from home because of the pandemic

If your company is making you work from home because of the pandemic, things are a little more clear cut for you. Any company property you use, such as an office laptop, should be covered under their corporate insurance plan in case of any loss or damage. I would recommend checking with someone in your workplace first, just to be sure.

Working from home as an independent contractor/freelancer

Remote work as an independent contractor or freelancer has its fair share of complications. Paying taxes is difficult, but so is figuring out homeowners’ insurance. A standard home insurance policy might not provide enough cover if you work from home. You might not be allowed to claim a personal laptop that you also use for work.

Ultimately, it all comes down to your insurance provider. It’s essential to have a conversation and dive into the finer details of what is and isn’t covered by your home insurance policy. If you’ve recently made the switch to remote work but plan to do so long-term, it’s a good idea to let the insurance company know.

Running your own business from your home

Tips and tricks to make work from home easier

Working from home is still a very new idea for many. It can also quickly become challenging to manage, once the initial sheen wears off. Here are a few things you can do to make the whole experience just a little better.

Get the right equipment: It’s vital to have the right equipment and setup. You might need to upgrade your internet plan to get a faster connection speed, or in case you deal with data caps. We’ll be taking a more detailed look at all the gear you might need to take your work from home experience to the next level.

Set up a home office: Having a separate workspace will help you get into the right mindset. It will also provide a place to leave and return to when you take a break, just to simulate how it would be in your office. You don’t need a separate room, of course. Even a desk tucked into a corner of your living room will do the trick. Don’t forget to check out our guide on how to set up a home office!

Decide on a fixed work schedule: Many consider the flexible hours a huge positive when working from home. However, time management can quickly get away from you, and you might find yourself struggling to meet deadlines and finish projects. You don’t have to be strict about it, but following a regular schedule makes approaching work a lot easier. It also gives you time to mentally “switch off” and be done with a workday.

Avoid distractions: There are distractions galore when working from home. The TV remote is within reach, you’d much rather spend time with family, and scrolling through Twitter and Instagram is probably more fun than work. A manager or colleagues aren’t around to look over your shoulder and make sure that you aren’t distracted either. There’s a lot you can do to stay focused when you’re working from home and get into a flow state.

Know when to disconnect: The ability to end your workday when working from home is as crucial as figuring out when to start. It will be tempting to power through to get all your work done. Not only will these long hours impact the quality of your work, but it could potentially affect your wellbeing. Maintaining a healthy work-life balance becomes even more critical when you’re working from home long-term.

Getting the right gear

The sudden shift to work from home left quite a few people floundering for gear. The chair you’ll be sitting on all day needs to be as comfortable as possible. A good mic makes it much easier for everyone to hear you. A high-end webcam is useful for conference calls. A great pair of headphones ensures that you don’t miss anything during a call, and can help drown out any background noise at home. I can go on and on. Instead, let’s take a look at some of our top recommendations for work from home gear.

Picking the right laptop for the job

David Imel / Android Authority

Not everyone has or needs a high-end laptop for personal use. It doesn’t take much to browse the web and social media or watch the occasional movie or TV show online. However, if you need to run specialized software for work, you might need to upgrade your laptop. Some companies might provide a work laptop, but if you need to get a new one yourself, these are some of the best options to consider.

A solid mid-ranger: Microsoft Surface Pro 7

Microsoft’s fantastic line of 2-in-1’s gets better with every iteration. The latest in the lineup, the Surface Pro 7, is worth considering if you have a little more money to spend. It starts at around $800 for the version that comes with an Intel Core i5 processor and 8GB of memory. You can upgrade the RAM and processor, with the highest-end model setting you back around $1500. Keep in mind that the Microsoft cover will cost extra, and it is quite expensive. Instead, you could consider one of the excellent keyboards we recommend below.

The best high-end laptop: MacBook Pro 13-inch

The MacBook Pro remains a crowd favorite for those in the market for a high-end laptop. Apple’s excellent craftsmanship shines through once again, and the 2023 edition also comes with the new Magic Keyboard and Touchbar. It comes with a 10th Gen Intel i5 processor, up to 16GB of RAM, and up to 1TB of storage. Four Thunderbolt 3 ports take care of all your connectivity needs. It’s up there in the price department, but there’s a reason why the MacBook Pro is so popular.

Unsurprisingly, the 13-inch MacBook Pro isn’t the most expensive option Apple has to offer. If you have more money to spend, you can get a larger screen, an i9 processor, and 32GB of RAM with the 16-inch MacBook Pro. It isn’t our main recommendation because it’s likely overkill for most users. If you’re a power user, though, there are not many others worth considering.

Are you looking for even more options? Check out our roundup of the best laptops you can buy!

Consider a desktop instead

You might not want to do “work stuff” on a personal laptop, especially if you’re used to having a private workstation in the office. Since you’ll probably look into creating a separate office space in your home, a desktop is an excellent way to set this area apart. The feeling of “going to” and “leaving” work when you step away from your desk helps too.

Desktop vs laptop: Which should you get to work from home?

A budget-friendly desktop to consider: Dell Inspiron Desktop 3880

Amazon

The Dell Inspiron Desktop 3880 starts at around $450. It’s an excellent affordable option to consider. If you aren’t happy with the 10th gen i3 processor, even the high-end i7 iteration is relatively inexpensive at $800. Depending on the model, it comes with up to 12GB of RAM and 1TB of storage. There’s no shortage of connectivity, with 8 USB-A ports, an HDMI port, an SD card reader, and more.

An excellent all-in-one: HP 27 Pavilion

Amazon

The HP 27 comes with the specs and features of a far-more expensive laptop and falls in the mid-range category. You get the 10th Gen i7 processor, 16GB of RAM, a 512GB SSD, 1TB of HDD storage, and a 27-inch Full HD display. There also are USB-A and USB-C ports, an SD card reader, and an ethernet port to cover all of your connectivity needs. It’s a sleek and beautifully-designed device that will look fantastic on any desk.

For Apple fans: new iMac (2023) 27-inch

Apple

Any macOS or iOS user will swear that there’s only one answer to the right desktop question — the iMac. The 2023 refresh is a major upgrade. It comes with a 27-inch 5K Retina Display, a 10th Gen i5 processor, 8GB of RAM, and up to 512GB of storage. Connectivity includes four USB-A ports, two Thunderbolt 3 ports, and an ethernet port. It also has one of the best built-in webcams you’ll find on a desktop. Unsurprisingly, the iMac is quite expensive but is completely worth it.

Are you a power user who has $5,000 to spare? You might want to look into the iMac Pro instead.

You need a good desk

Bogdan Petrovan / Android Authority

Staying sedentary for a long time is bad for your health. From poor posture and a stiff back to potential cardiovascular issues down the road, sitting all day is something we should all avoid. When you’re in an office, you probably get up for meetings, coffee breaks, or to simply chat with co-workers. A large part of that interaction goes away when you work from home. You’ll have to continually remind yourself to take a break and stay active, but no one wants to break their workflow either.

Standing desks vs converter desks

The entire flat top section of a standing desk comes up and goes down with you. The more expensive options have motors and do this automatically. You can save some money by opting for a manual version, but that might be inconvenient and take too long.

On the other hand, a converter desk, or riser, is ideal for anyone that doesn’t have room for a large desk, or already has a work desk or table. These are usually easier to assemble, offer more flexibility, and aren’t as expensive.

Our recommendations

Fully Jarvis bamboo standing desk

Fully

The Fully Jarvis bamboo standing desk is a beautiful sit/stand desk that is also eco-friendly. You can customize the size, choosing from a range that starts at 30 x 24 inches and goes all the way up to 78 x 30 inches. A simple up-down toggle and programmable presets makes switching between sitting and standing quick and easy. Further options include the ability to add wire management grommets, and Fully also offers grommets with plug points and USB ports. The Fully Jarvis bamboo standing desk starts at $559.

VariDesk Basic 30

Amazon

A very well-known brand in the standing desk category, Varidesk offers an excellent range of standing desks and risers. VariDesk has a slew of high-end standing desk converters available. For most users, however, the Basic 30 will more than get the job done. It comes with a two-tier design that lets you adjust the height according to your needs, with 11 adjustable settings. It’s big, spacious, and has a 25lb weight limit. It’s also priced at just around $200.

Looking for even more options? Here are some excellent standing desks worth checking out!

Pick a good monitor

Anybody who spends all day in front of a computer screen knows the value of a good monitor. If you’re moving to a home office setup, your laptop screen may not cut it. Luckily, there are a lot of great monitors you can choose from, and most are relatively affordable as well.

Best budget-pick: LG 32QN600-B

Amazon

This 32-inch LG monitor is a great budget-friendly pick. It may not be the best for gaming with a 5ms response time, but the 2K display with HDR10 capability should be perfect for getting work done or streaming your favorite shows. You can also mount it on your wall with the VESA mount and attach peripherals with two HDMI ports and a DisplayPort option. If you were hoping for a higher resolution, LG has got you covered.

The best cheap 4K monitor: Dell U2720Q

The Dell U2720Q is a great monitor for productivity. The 27-inch IPS display comes with a 3,840 x 2,160 resolution, accurate color reproduction, a 60Hz refresh rate, and a 5ms response time. You get only one HDMI and DisplayPort, but a USB-C port provides a solution with up 90W power delivery to keep your laptop or device charged while transferring data and video. It’s beautifully designed too, with a small, compact base and a near-borderless frame.

The best high-end monitor: BenQ PD2720U

Amazon

The BenQ PD2720U is one of the best monitors you can get. The 27-inch IPS LCD screen comes with a 3,840 x 2,160 resolution and a 60Hz refresh rate. Its color accuracy is its most significant selling point. It comes with professional CAD/CAM, Darkroom, and Animation display modes. It also supports HDR10 content.

It’s also not lacking in connectivity. It comes with two HDMI ports, a DisplayPort, and two Thunderbolt 3 ports (upstream and downstream) to provide a single-cable solution for charging and data transfers. The stand lets you tilt and swivel the monitor and find the optimal angle. It’s an expensive monitor, though, but it is well worth it. If you’re looking for even more screen real estate, you can also get the 32-inch model for an additional $100.

Getting the right headphones for the job

Adam Molina / Android Authority

A good pair of headphones or earbuds is a must-have if you work from home and mainly if you attend a lot of conference calls. Using your laptop audio or other external speakers isn’t a good idea since there’s usually some kind of feedback and echo. Headphones also ensure that anything discussed on the call stays private from roommates or family.

Have noisy kids, loud roommates, or dealing with construction right outside your window? You might want to splurge on noise-canceling headphones to keep you focused on work.

The best noise-canceling headphones: Sony WH-1000XM4

Adam Molina / Android Authority

The Sony WH-1000XM3 already were a fantastic pair of noise-canceling headphones, and their successor is even better. The WH-1000XM4 comes with additional features to provide an exceptional audio experience. It also offers outstanding call quality, which is more important in the work from home context. A 30-hour battery life and fast charging capabilities round out a reliable package. It’s expensive, but you get what you pay for.

An affordable alternative: AKG N60NC

You don’t have to spend hundreds of dollars to get a good pair of noise-canceling headphones. The AKG N60NC is a quarter of the price of the Sony but it offers really good audio and call quality. It also comes with 30-hour battery life, while its foldable design makes it a good travel companion. If active noise-canceling is a requirement, this is one of the best relatively inexpensive options you can get.

A good keyboard makes all the difference

Adam Sinicki / Android Authority

Sleek and ultra-thin laptops are great for portability and they certainly look good. However, their form factor doesn’t lend to a great typing experience in most cases. If your job involves typing away on a keyboard for hours on end, you might want to get one that makes it easier.

The best ergonomic keyboard: Logitech Ergo K860

The Logitech Ergo K860 is a Bluetooth keyboard ideal for those that want full, but portable, functionality. Its unique curved design allows you to type naturally and puts less strain on your wrists. The Ergo in the name is reflected in more ergonomic design features, including the wrist support pad that makes typing that bit more comfortable. You can plug the keyboard into your laptop or PC, or use it wirelessly via Bluetooth from up to 10 meters away.

The best full-size keyboard: Microsoft Surface Keyboard

The Microsoft Surface Keyboard is meant to be used mainly with Microsoft’s lineup of Surface tablets and PCs, but won’t look out of place on any desk. It’s a full-size keyboard with a dedicated number pad on the side. The nice-looking soft gray finish falls in line with Surface aesthetics. You also get optimized key feedback for a smooth typing experience.

The best mechanical keyboard: Das Keyboard 4 Professional

The Das Keyboard 4 Professional offers attention to detail that you will be hard-pressed to find elsewhere. Key inscriptions are laser etched, and the aluminum top can withstand plenty of wear and tear. The keys are made to last over 50 million presses. The media buttons are convenient, and the large volume knob is very nice to have. The typing experience is top-notch, with a lot of travel and superior tactile feedback. It’s not cheap, but it’s definitely among the best mechanical keyboards you can get.

A better mouse is worth buying

The best computer mouse can make a significant difference in comfort, performance, and precision. There are a lot of options available, and we’ve included two different types below. Of course, you should also check out our complete roundups via the links below.

Logitech MX Vertical

Logitech

Logitech is one of the best PC accessory makers around, so it’s no surprise that they have multiple devices featured on this list. Another great device is the Logitech MX Vertical. According to Logitech, the MX Vertical offers up to a 10-percent reduction in muscle strain due to the mouse’s tilt angle of 57 degrees. Apart from the solid ergonomics, the MX Vertical also offers several distinct features. The mouse provides up to four months of battery life and can be recharged through the USB-C port.

External storage devices are useful

You might run out of storage on your laptop or PC if you suddenly have to use it to save all your work files as well. You could upgrade the internal storage if it’s customizable, but an easier alternative for most would be an external hard drive. It’s a useful way to backup your data, and it’ll let you store work and personal files separately. You can also just take it with you once you can go back to the office.

Samsung T5

Unlike mechanical hard drives, the Samsung T5 is an external solid-state drive (SSD). That means there are no moving parts to worry about. The Samsung T5 is incredibly slim and compact, easily fitting in a shirt pocket. Thanks to its use of Samsung’s V-NAND flash memory and USB-C, the T5 provides incredible transfer speeds of up to 540Mbps. It’s expensive but worth every penny.

SanDisk Extreme Portable External SSD

The SanDisk Extreme Portable External SSD offers super-fast read and write speeds of up to 550 and 500Mbps, respectively. Apart from being speedy, what makes this external drive stand out is that it’s rugged. The portable SSD features an IP55 rating for protection against water and dust. It can also withstand drops from up to two meters onto a concrete floor.

You’ll find plenty of other options in our complete roundup of the best external hard-drives!

A webcam is essential!

You probably have a webcam built into your laptop. But, unless it’s the feature you took particular interest in, it’s safe to say that it won’t be the best quality. Dedicated webcams can significantly improve image quality over what laptops offer. They are your only option if you own a desktop computer too. With everybody on Zoom now, a good webcam is a must.

Razer Kiyo

The Razer Kiyo might be intended for gamers, but it’s great for video calls too. The unit comes with a circular light that makes exposure more even. The definition is crisp and smooth at 1080p@30fps. It also doesn’t cost much more than the best affordable webcams you can get either.