Guide to Understanding Deep Learning as a Concept

January 28, 2021

1. Introduction

In today’s world, the term Artificial intelligence (AI) is being used very widely. This provides the idea about the impact of AI on modern technologies. Every business wants to integrate AI in their business operation to streamline and ease their activities. There is a great advancement with AI, but still, there is a need for a steady set of guides in learning AI.

There are many sources to learn, while this article will provide you with great knowledge on deep learning concepts and, more importantly, provide readers with an understanding of how Deep learning is so important in Machine learning. Many Cloud service providers like E2E provide AI-enabled cloud to provide a good stage for users AI development. “42% of executives believe AI will be of ‘critical importance’ within 2 years.”

This guide is for both technical and non-technical audiences.

2. Deep learning

Machines are used to quickly and efficiently perform certain tasks of operations that are guided by humans. Here machines require human interaction to train or operate the machines. What if machines learn by the training and perform the operations on their own? Any human learns to perform the task by practicing and repeating the tasks, and based on the outcome, he will memorize to perform the tasks. Next time the brain automatically triggers to perform the task more quickly and efficiently.

Deep learning is also constructed in the same pattern. Here machines learn based on the training provided, and the same like the brain, neural networks are constructed to fetch the data. Example: image, sound classification, object detection, image segmentation.

If you are still wondering how important is Deep learning, here are some stats:

  • 75% of Netflix users’ film selection is enabled by Netflix’s deep learning algorithms.
  • Talking about the value, the global machine learning market in 2017 was valued at $1.58B, and this is projected at $20.83B in 2024.
  • In today’s number, at least 2 in 10 companies use AI-enabled software for their business development.
  • 83% of Global market leaders say AI & ML is transforming customer engagement.

Even in the era of the pandemic, AI and ML have enabled a lot of innovations.

To get started with learning deep learning as a concept, handwritten digital recognition is considered.

2.1. What is Handwritten Digit Recognition?

Handwritten digit recognition is the potential of computer-aided devices to identify human handwritten digits. It is a difficult task for computers because handwritten digits are not of the same shape or pattern. Everyone has their style of writing the diagram. Handwritten digit recognition is an essential functionality. This uses the image to compare the image of the required pattern.

2.2. Classification of Handwritten Digit Recognition

One of the key functionalities of deep learning and AI is image recognition. This functionality is key for handwritten digit recognition. As a guide, this article will aid in creating mathematical models for identifying handwritten digits. Below examples of handwritten numbers 4 and 2 are shown below.

Here the goal is to create a neural network where the model will recognize the handwritten digits imputed. Like in the given image, the model needs to recognize the image as 4 and 2.

2.2.1. Classification issue with uncertainty

In the above example, sometimes we also fail to recognize the numerical number. Here we need to train the computer for accurate recognition. Here the machine is dealing with classification problems, where given an image, the model needs to classify between 0-9 Digits.

To solve the classification issue, the neural network will return a vector using the 10 positions providing the chances of the digit occurrence.

2.2.2. Data format and manipulation

Moving on to the next phase, the article provides the details in modeling the neural network. Learners can collect the MNIST data to train the model containing 60,000 or more (Greater the training data, more the accuracy) of hand-written digits. The dataset needs to be a black and white set of images and a good resolution of 28×28 pixels.

To accommodate the ingestion of a dataset for the selected neural network, transformation needs to be made from the input (image) in 2D onto the vector image of 1D. The standard format of the matrix 28×28 digits can be represented using the vector (array) of 784 digits (liking is done row by row). This is the standard format to include as an input for the largely connected neural network.

Now, the representation stage, which includes, each label needs to be represented as a vector of 10 positions, this needs to correspond to the position of the digit representing the image containing 1 and the rest containing 0s. The process of changing the label to a vector including as many zeros in the digit with various labels, and labeling 1s in the index to the adjacent label is termed as one-hot encoding. For a better understanding of Digit 4 can be encoded as,


[0.0 0.0 0.0 1.0 0.0 0.0 ]

3. Neural Network

Next, we will look into concepts regarding the neural network. Neural networks are used to train the model.

3.1. Concepts of Neural network.

To showcase the basic operation of a Neural network, let us consider a simple example in which a set of points are showcased in a two-dimensional plane and points are labeled as “circle” or “triangles”:

Now consider a new point “P”, where we need to find what label belongs to it:

A most common way is to mark a straight line separating the two groups that are shown below and the line is used as a classifier, Considering the input data, each input is represented using vectors form (p1, p2) indicating the coordinates in two-dimensional space, returning the functions in ‘0’ or ‘1’ to separate the identity and to know if it needs to classified as “triangle” or “circle”, defined as, And the line is expressed as,

In order to classify the input elements P, in two dimensional, it requires learning the vector weight W which is of the same dimension of the input vector, so vector (w1, w2) and a d bias.

After getting the calculated values, construction of the artificial neuron network can be started for new element P. Here the neuron applies the vector weight W onto the values in the dimension of the input element P, later at the end adding bias values. After this, the result will be passed through a nonlinear function known as the “activation” function, which will produce the result between “0” and “1”. The r is the artificial neuron and it is defined and expressed as,
Considering the function that applies the transformation to variable r which produces ‘0’ or ‘1’. Now considering the sigmoid function which returns the output value between 0 and 1.

Now let’s check the formula, the output always tends to give the value close to 0 and 1. Now if the input s is positive and large, “e” minus s will be zero and the r will take the value 1. Now if the s has a large value and tends to negative. Then the value of r will be 0. Graphical representation of the sigmoid function can be made as shown below,

3.2. Multi-Layer Perceptron

Multilayer perceptron can be referred to as a neural network with one or more input layers which are composed of perceptrons (commonly referred to as a hidden layer) and later the final layer. Or Deep learning can be referred to as a neural network model composed of the multilayer. Below is the visual representation of the scheme,
Image source:

Mostly the MLPs are used during the classification, here we need to classify among the classes (0-9). The outer layer takes up the task of providing the probability using the function called softmax. There can be many activation functions, then the one considered sigmoid, that is softmax activation function. Which is very well suitable when classifying in more than two classes. A detailed explanation of the softmax activation function is provided in the next session.

3.3. Softmax activation function

Now the input set is a set of handwritten images. Given an image the algorithm needs to provide the probability, it is among the 10 possible digits. Now take the example of digit 2 it looks like the digit 2 70%, but the tail part may seem to be 3 30%, this is also true when humans are identifying the digit. So it needs to be modeled in such a way the highest probability is considered, this is a probability distribution function. Here the vector of probability needs to correspond to a digit and their sums need to be in 10 probabilities than the result to be 1.

As discussed earlier this can be achieved by using the output layer with the usage of the softmax activation function. Here each of the neurons in the softmax layer will depend on outputs from the other neurons and their sum needs to be 1.

Working on a softmax activation layer? It’s very straight forward, based on the evidence that any image belongs to a particular class, value is converted into probabilities belonging to possible classes. Here the weighted sum of the evidence output is considered for each of its pixels.

Where the algorithm has created the reference model based on the training set and later this image from the input is compared to the matching probability and this provides the result to input the softmax activation function.

Image source:

Once the result is calculated and that each belongs to digits in the 10 classes and the result is 1. The softmax function makes use of the exponential value to normalize.The equation can be written as,

3.4. Neural Network model for identifying handwritten digit

Here a simple neural network can be written based on the sequence of two-layer, which can be represented as,

Image source:

Here we can see 784 input features (28×28). The first layer containing 10 neurons using the sigmoid activation function, “distills” are taken to provide the function value between the 10 values. Next coming to the softmax layer of 10 neurons, this means the matrix of 10 probability values is provided.

4. Learning process

This is a vital process of deep learning, where the learning process is carried based on the (weight W and d biases). The weight values are learned and this value is propagated in the network. Propagated value is then shared in the network known as backpropagation to train the value and optimize the given network. Next after optimizing the network the forward propagation takes place which is explained in the next part.

4.1. Training Loop

Primarily we come across the forward propagation, when the neural network is exposed to the training data, they move forward in the network collecting the prediction label for the calculation. Here the data is passed through the network where the transformation is applied, this value is then sent to the next layer from the previous layer. At final the data are crossed among all the layers and once the calculation is complete the final layer is reached using the result of label prediction for the input example.

Next, the loss function is issued to estimate the loss and compare the measure of correctness in relation to the exact result. Here we aim not to get any divergence between the actual and prediction value. Now the model needs to be trained until the weights in the network of neurons have the perfect prediction value. After that the loss value is calculated the information is back propagated for optimizing the network, starting from the final node to the starting node. Once the loss is seen as possible to zero, this network is ready to make the prediction.

4.2. Cross-Entropy Loss Function

The loss function that is used here is the cross-entropy function, which allows the comparison between any given two probability distributions. Cross entropy loss is used to measure the performance of the given classification model, giving the output value between 0-1. At any given time the perfect model should have a log loss of 0.

5. Conclusion

In this article, we have visited the basic and main concepts of the neural network model. This will provide basic and general insights into understanding deep learning and how it is used in detecting hand-written digits. Following this coding needs to be done for working on the model and creating a node.

E2E Network deep learning ready cloud services should be your first choice when choosing AI-enabled cloud. E2E network cloud service comes with ready to use tools that are integrated to handle any volume of machine learning workload. E2E network not only provides a cost-effective cloud solution but also 99 % SLA coverage enabling the perfect stage to engage in designing and implementing uninterrupted machine learning projects.

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This is a decorative image for: Top 12 skills a CEO should demand in a data scientist to hire in 2022
September 21, 2022

Top 12 skills a CEO should demand in a data scientist to hire in 2022

Two decades ago, data scientists didn’t exist. Sure, some people cleaned, organized and analyzed information — but the data science professionals we admire today stand at the head of a relatively new (and vaunted) career path.

It is certainly one of the most popular careers because it is in great demand and highly paid. With data being the primary fuel of industry and organization, company executives must now determine how to drive their company in this rapidly changing environment. Not only is a growth blueprint essential, but so are individuals who can put the blueprint into action. When most senior executives or human resource professionals think of data-driven employment, a data scientist is the first position that comes to mind.

In this blog, we will discuss the top 12 skills a CEO should demand if hiring a data scientist in 2022. 

  1. Problem-Solving and Critical Thinking

Finding a needle in a haystack is the goal of data science. You'll need a candidate who has a sharp problem-solving mind to figure out what goes where and why, and how it all works together. Thinking critically implies making well-informed, suitable judgments based on evidence and facts. That means leaving your own ideas at the door and putting your faith - within reason - in the evidence. 

Being objective in the analysis is more difficult than it appears at first. One is not born with the ability to think critically. It's a talent that, like any other, can be learned and mastered with time. Always look for a candidate who is prepared to ask questions and change his/her opinion, even if it means starting over.

  1. Teamwork 

If you go through job listings on sites like Indeed or LinkedIn, you'll notice one phrase that appears repeatedly: must work well in a team. Contrary to popular belief, most scientific communities, including those in data science, do not rely on a single exceptional mind to drive forward development. A team's cohesiveness and collaboration power are typically more significant than any one member's brilliance or originality. Your potential candidate will not contribute to success if s/he does not play well with others or believes that s/he does not require assistance from your colleagues. If anything, candidates' poisonous attitudes may cause stress, decreased levels of accomplishment, and failure on the team.

Harvard researchers revealed in 2015 that even "moderate" amounts of toxic employee conduct might increase attrition, lower employee morale, and reduce team effectiveness. Eighty percent of employees polled said they wasted time worrying about coworker incivility. Seventy-eight per cent claimed toxicity had reduced their dedication to their work, and 66 per cent said their performance had suffered as a result. The fact is that being a team player is significantly more productive and fulfilling than being a solo act. Look for a candidate with good cooperation abilities, and both you and your team will profit!

  1. Communication 

Capable data scientists must be able to communicate the conclusions they get from data. If your candidate lacks the ability to convert technical jargon into plain English, no matter how significant the results are, your audience will not grasp them. Communication is one of the most important skills a data scientist can learn — and one that many pros struggle with. 

One 2017 poll that tried to uncover the most common impediments that data scientists encountered at work discovered that the majority of them were non-technical. Among the top seven barriers were "explaining data science to others," "lack of management/financial support," and "results not utilised by decision-makers."

You fail if you can't communicate - therefore look for a candidate who knows how to interpret! And can break down complicated topics into digestible explanations; rather than giving a dry report.

  1. Business Intelligence 

Sure, a candidate can’t start teaching abstruse mathematical theory whenever you want — but can they explain how that theory can be applied to advance business? True, data scientists must have a strong grasp of their field as well as a solid foundation of technical abilities. However, if a candidate is required to use those abilities to advance a corporate purpose, they must also have some level of business acumen. Taking a few business classes will not only help them bridge the gap between their data scientist peers and business-minded bosses, but it will also help them advance the company's growth and their career as well. It may also assist them in better applying their technical talents to create useful strategic insights for your firm.

  1. Statistics and mathematics 

When it comes to the role of arithmetic in machine learning, perspectives are mixed. There is no disputing that college-level comprehension is necessary. Linear algebra and calculus should not sound like other languages. However, if you're looking for a candidate for an internship or a junior position, then they don't need to be a math guru. But if you are looking for a candidate to work as a researcher, then the candidate must have more than just a strong math background. After all, research propels the business ahead, and you won't be able to accomplish anything until you have a candidate with a thorough grasp of how things function.

The fact is that just because data science libraries enable data scientists to perform complex arithmetic without breaking a sweat doesn't mean they shouldn't be aware of what's going on behind the surface. Get a candidate with the fundamentals right.

  1. AI and Machine Learning 

Machine learning is an essential ability for any data scientist. It is used to create prediction models ranging from simple linear regression to cutting-edge picture synthesis using generative adversarial networks. When it comes to machine learning, there is a lot to look for in a potential candidate. Regression, decision trees, SVM, Naive Bayes, clustering, and other classic machine learning techniques (supervised and unsupervised) are available. Then there are neural networks, which include feed-forward, convolutional, recurrent, LSTM, GRU, and GAN. There's also reinforcement learning, but you get the idea - machine learning is a vast subject. 

  1. Skills in cloud and MLOps

To remain relevant to the industry's current demands, more than three out of five (61.7%) companies say they need data scientists with updated knowledge in cloud technologies, followed by MLOps (56.1%) and transformers (55%). Three out of every four professionals with ten or more years of experience are learning MLOps to expand their skill sets. Cloud technologies (71.7%) are being learned as a fundamental new talent by mid-career professionals with 3-6 years of experience, followed by MLOps (62.3%), transformers (60.4%), and others.

Professionals in retail, CPG, and e-commerce are more likely (73.7%) to learn cloud technology as a new skill. As much as 70% of BFSI personnel upskill in MLOps. Another 70% and 60% of pharma and health workers are interested in acquiring transformers and computer vision as fundamental skills.

So make sure you don't miss out on such a talent who can bring cloud and MLOps skills into your company. 

  1. Storytelling and Data Visualization 

Data visualisation is enjoyable. Of course, it depends on who you ask, but many people consider it the most gratifying aspect of data science and machine learning. Look for a candidate who is a visualisation specialist and understands how to show data based on business requirements, and also how to integrate visualisations so that they tell a story. It might be as easy as integrating a few plots in a PDF report or as sophisticated as creating an interactive dashboard suited to the client's requirements.

The data visualisation tools utilised are determined by the language. Plotly, which works with R, Python, and JavaScript, may be the best option if you need a candidate for searching for a cross-platform interactive solution. Consider Tableau and PowerBI when you need a candidate for viewing data using a BI tool. 

Figure: Use of Data Visualization tools. 

  1. Programming 

Without programming, there is no data science. How else would you give the computer instructions? All data scientists must be familiar with writing code, most likely in Python, R, or SQL these days. The breadth of what a candidate will perform with programming languages differs from that of traditional programming professions in that they’ll lean toward specific libraries for data analysis, visualisation, and machine learning. 

Still, thinking like a coder entails more than just understanding how to solve issues. If there is one thing that data science sees a lot of, it is issues that need to be solved. But nothing is worse than understanding how to fix an issue but failing to transform it into long-lasting, production-ready code.

Out of the host of programming languages, 90% CEOs hire data science specialists who are specialists in Python as their preference for statistical modelling. Beyond that, the use of SQL (68.4%) is highest in retail, CPG, and ecommerce, followed by IT at 62.9%. R is the most widely used programming language if you operate in the pharma and healthcare business, with three in five (60%) data scientists reporting using it for statistical modelling.

  1. Mining Social Media 

The process of extracting data from social media sites such as Facebook, Twitter, and Instagram is referred to as social media mining. Skilled data scientists may utilise this data to uncover relevant trends and extract insights that a company can then use to gain a better knowledge of its target audience's preferences and social media actions. You need data scientists well versed with this type of study as it is essential for building a high-level social media marketing plan for businesses. Given the importance of social media in day-to-day business and its long-term viability, hiring data scientists with social media data mining abilities is an excellent strategy for company growth.

  1. Data manipulation 

After collecting data from various sources, a data scientist will almost surely come across some shoddy data that has to be cleaned up. You need to hire a candidate that knows what Data wrangling is. How to use it for the rectification of data faults such as missing information, string formatting, and date formatting. 

  1. Deployment of a Model 

What is the use of a ship if it cannot float? Non-technical users should not be expected to connect to specialised virtual machines or Jupyter notebooks only to check how your model operates. As a result, the ability to deploy a model is frequently required for data scientist employment.

The easiest solution is to establish an API around your model and deploy it as any other application — hosted on a virtual machine operating in the cloud. Things get harder if you wish to deploy models to mobile, as mobile devices are inferior when it comes to hardware. 

If speed is critical, sending an API call and depending on an Internet connection isn't the best option. Consider distributing the model directly to the mobile app. Machine learning developers may not know how to design mobile apps, but they may examine lighter network topologies that will have reduced inference time on lower-end hardware.

Consider hiring a candidate who is well versed with all the things discussed above related to deploying a model. 


And there you have it: the top twelve talents skills a CEO must look for while hiring a data scientist. Keep in mind that skill levels or talents themselves may differ from one firm to the next. Some data science jobs are more focused on databases and programming, while others are more focused on arithmetic. Nonetheless, we believe that these 12 data science skills are essential for your potential candidate in 2022.

This is a decorative image for: Towards Complete Icon Labelling in Mobile Applications
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Towards Complete Icon Labeling in Mobile Applications

Why is Icon Labeling Important?

Icon labeling projects aim to create a machine learning algorithm that can automatically label icons in mobile applications. The algorithm is generally trained on a dataset of labeled images and learns to recognize the objects in the pictures. Labeling icons is a tedious task and often requires human intervention.

Thus, automating this process by training an algorithm on a labeled image dataset can pave the way for complete icon labeling. This article will walk you through labeling icons using machine learning. Icons may seem like a small part of your app, but they're critical for branding and user experience. Icons need to be labeled by hand, which is time-consuming and tedious.

It isn't easy to keep up with the volume of new icons on mobile phones, and keeping the icons organized takes a lot of effort. The wrong icon can ruin your app's design and make it difficult for users to use. With any icon labeling project, labeling icons is easy. Your database will be automatically and consistently labeled by Artificial Intelligence that recognizes objects in images.

How to Label Icons Effectively?

Prepare your data set. It should include the icon's name, a short description, and an image of the icon. You can use any file type uploaded to any storage or drive.

Next, you will need to create a project in the platform and enable billing if it has not been done already. Then you can create a new dataset by specifying a dataset ID and name.

The use of labeling icons in UI design has been around for many years. The most popular use case is to offer users an indication of what they can do on a particular screen. You can do so by adding labels to the icons.

Icons often indicate the user's action to complete a task (e.g., save, delete, etc.). However, this could be problematic for people with disabilities or who cannot understand or read English fluently due to language and communication barriers.

Labeling icons is complex, especially when the icon is not well-known. We propose a novel method for labeling icons with conversational agents and chatbots. Machine Learning techniques can help generate a set of labeled examples for a conversational agent or chatbot training.

Tips for using icons in your app

Labels are the most critical component of an icon, as they communicate the meaning to users. Designers should keep their icons simple and schematic and include a visible text label to make them good touch targets.

Icon designers also need to be careful when designing icons. Designers should keep their icons simple and schematic, include a visible text label and make them good touch targets. Labels are the most crucial component of an icon as they communicate meaning to users.

Icons should be simple and schematic with a clear visible text label that communicates what the icon means to users. Icons are also suitable for touching targets for screen readers, so designers must consider this when designing them.

Icon labels are an essential feature that can make or break an icon. Designers are often designing icons with less-than-perfect or downright nasty labels. Terrible labels can lead to misinterpretation and confusion, leading to lost business or a tarnished reputation. Labels are not just crucial for designers; they're critical to users.

The label conveys the meaning of a symbol, so it should be simple, visible, and easy for interaction purposes. If designers ignore these principles, icons will become meaningless, unhelpful, and challenging to navigate. Designers must create good touch targets that are easily recognizable. After all, it's about bringing users the best.


Iconography is the basis of every UI design. Designers need to understand how it shapes an interface’s usability. Every icon in an interface serves a purpose. When implemented carefully and in the correct manner, icons can help users navigate through the workflow. It's good to be a part of this cutting-edge iconography which can help you further push the boundaries of Deep Learning and expand your understanding of recognizing icon types.

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September 21, 2022

Prompt-based Learning can Make Language Models More Capable

Artificial Intelligence promotes supervised learning and builds a system that catches the fundamental relationships between various inputs and outputs. In this way, Natural Language Processing (NLP) plays a very integral part in linking the input with the output. It was hard to retrieve the knowledge and information from the important datasets in the NLP technology. But with the development of neural network models, it has become very convenient to retrieve information from the datasets. 

Learn the neural network models to better understand and deal with the features of NLP technology. The new training that the researchers are trying to provide to the learners of neural networks to understand NLP models is popularly known as Prompt-based Learning. This means this type of learning will not be supervised anymore. It will however be used to solve various tasks but what is the correct prompt for a particular language model is a task in itself.

What is Prompt-based Learning?

Machine Learning researchers and engineers need a proper strategy through which they can train the large language models. Such training will enable the large language model (LLM) to deal with different tasks without the need for a training session each time. 

There are certain traditional languages which are being popularly used but they are required to be retrained every time there is a change in the functioning. BERT and GPT-3 are the two best examples of such languages. Search languages need to be fine-tuned every time. As a result, a lot of tasks have to be done as retraining to get the work done. Prompt-based learning eliminates all these needs.

Advantages of Prompt-based Learning

Prompt-based learning offers several benefits when compared to the traditional way of fine-tuning methods. The benefits of prompt-based learning are enumerated as follows:

  • Prompt-based learning works exceptionally well in the case of labelled data that are in small amounts.
  • It helps you to achieve strong and accurate results by doing comparatively less work.
  • Prompt-based learning also helps you to achieve the standard of efficiency and the process becomes cost-effective.
  • These AI models require less energy consumption and thus, Prompt-based learning saves energy consumption.

These advantages play a very big part in why various companies choose prompt-based learning to train their NLP technologies.

Challenges of Prompt-based Learning

Despite a ton of advantages that prompt-based learning has in store, it has certain disadvantages as well. These disadvantages come in the form of challenges that can be stated as follows:

  • It is difficult to design prompt designs that will be effective.
  • Finding an accurate combination of prompt templates and the answers to them is another difficult task at hand.
  • Prompt-based learning has been implied in a few selected domains only. It needs to be explored more.
  • If the person who is handling the AI models does not understand the proper working of Prompt-based learning, then all the hard work will go in vain.
  • A constant trial and error approach is required to keep a check on whether a particular strategy is yielding fruitful results or not.

The discipline of Artificial Intelligence is ever-changing and ever-growing. It is one of the most dynamic disciplines that have come into existence. Also, know about why good conversation matters and how AI can help so that you get deeply rooted in this idea. Prompt-based learning is breezing the gap between the traditional and new age data models.

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