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,

4

[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: https://www.tutorialspoint.com/tensorflow/tensorflow_multi_layer_perceptron_learning.htm

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: https://towardsdatascience.com/deep-learning-basics-1d26923cc24a

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: https://towardsdatascience.com/handwritten-digit-mnist-pytorch-977b5338e627

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.

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Project Management for AI-ML-DL Projects

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An efficient project manager will ensure that there is ample time from the concept to the final product so that a client’s requirements are met without any delays and issues.

How is Project Management Done For AI, ML or DL Projects?

As already established, efficient project management is of great importance in AI/ML/DL projects. So, if you are planning to move into this field as a professional, here are some tips –

  • Identifying the problem-

The first step toward managing an AI project is the identification of the problem. What are we trying to solve or what outcome do we desire? AI is a means to receive the outcome that we desire. Multiple solutions are chosen on which AI solutions are built.

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After the problem has been identified, then testing the solution is done. We try to find out whether we have chosen the right solution for the problem. At this stage, we can ideally understand how to begin with an artificial intelligence or machine learning or deep learning project. We also need to understand whether customers will pay for this solution to the problem.

AI and ML engineers test this problem-solution fit through various techniques such as the traditional lean approach or the product design sprint. These techniques help us by analysing the solution within the deadline easily.

  • Preparing the data and managing it-

If you have a stable customer base for your AI, ML or DL solutions, then begin the project by collecting data and managing it. We begin by segregating the available data into unstructured and structured forms. It is easy to do the division of data in small and medium companies. It is because the amount of data is less. However, other players who own big businesses have large amounts of data to work on. Data engineers use all the tools and techniques to organise and clean up the data.

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To keep the blog simple, we will try not to mention the technical side of AI algorithms in the content here. There are different types of algorithms which depend on the type of machine learning technique we employ. If it is the supervised learning model, then the classification helps us in labelling the project and the regression helps us predict the quantity. A data engineer can choose from any of the popular algorithms like the Naïve Bayes classification or the random forest algorithm. If the unsupervised learning model is used, then clustering algorithms are used.

  • Training the algorithm-

For training algorithms, one needs to use various AI techniques, which are done through software developed by programmers. While most of the job is done in Python, nowadays, JavaScript, Java, C++ and Julia are also used. So, a developmental team is set up at this stage. These developers make a minimum threshold that is able to generate the necessary statistics to train the algorithm.  

  • Deployment of the project-

After the project is completed, then we come to its deployment. It can either be deployed on a local server or the Cloud. So, data engineers see if the local GPU or the Cloud GPU are in order. And, then they deploy the code along with the required dashboard to view the analytics.

Final Words-

To sum it up, this is a generic overview of how a project management system should work for AI/ML/DL projects. However, a point to keep in mind here is that this is not a universal process. The particulars will alter according to a specific project. 

Reference Links:

https://www.datacamp.com/blog/how-to-manage-ai-projects-effectively

https://appinventiv.com/blog/ai-project-management/#:~:text=There%20are%20six%20steps%20that,product%20on%20the%20right%20platform.

https://www.datascience-pm.com/manage-ai-projects/

https://community.pmi.org/blog-post/70065/how-can-i-manage-complex-ai-projects-#_=_

This is a decorative image for Top 7 AI & ML start-ups in Telecom Industry in India
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Top 7 AI & ML start-ups in Telecom Industry in India

With the multiple technological advancements witnessed by India as a country in the last few years, deep learning, machine learning and artificial intelligence have come across as futuristic technologies that will lead to the improved management of data hungry workloads.

 

The availability of artificial intelligence and machine learning in almost all industries today, including the telecom industry in India, has helped change the way of operational management for many existing businesses and startups that are the exclusive service providers in India.

 

In addition to that, the awareness and popularity of cloud GPU servers or other GPU cloud computing mediums have encouraged AI and ML startups in the telecom industry in India to take up their efficiency a notch higher by combining these technologies with cloud computing GPU. Let us look into the 7 AI and ML startups in the telecom industry in India 2022 below.

 

Top AI and ML Startups in Telecom Industry 

With 5G being the top priority for the majority of companies in the telecom industry in India, the importance of providing network affordability for everyone around the country has become the sole mission. Technologies like artificial intelligence and machine learning are the key digital transformation techniques that can change the way networks rotates in the country. The top startups include the following:

Wiom

Founded in 2021, Wiom is a telecom startup using various technologies like deep learning and artificial intelligence to create a blockchain-based working model for internet delivery. It is an affordable scalable model that might incorporate GPU cloud servers in the future when data flow increases. 

TechVantage

As one of the companies that are strongly driven by data and unique state-of-the-art solutions for revenue generation and cost optimization, TechVantage is a startup in the telecom industry that betters the user experiences for leading telecom heroes with improved media generation and reach, using GPU cloud online

Manthan

As one of the strongest performers is the customer analytics solutions, Manthan is a supporting startup in India in the telecom industry. It is an almost business assistant that can help with leveraging deep analytics for improved efficiency. For denser database management, NVIDIA A100 80 GB is one of their top choices. 

NetraDyne

Just as NVIDIA is known as a top GPU cloud provider, NetraDyne can be named as a telecom startup, even if not directly. It aims to use artificial intelligence and machine learning to increase road safety which is also a key concern for the telecom providers, for their field team. It assists with fleet management. 

KeyPoint Tech

This AI- and ML-driven startup is all set to combine various technologies to provide improved technology solutions for all devices and platforms. At present, they do not use any available cloud GPU servers but expect to experiment with GPU cloud computing in the future when data inflow increases.

 

Helpshift

Actively known to resolve customer communication, it is also considered to be a startup in the telecom industry as it facilitates better communication among customers for increased engagement and satisfaction. 

Facilio

An AI startup in Chennai, Facilio is a facility operation and maintenance solution that aims to improve the machine efficiency needed for network tower management, buildings, machines, etc.

 

In conclusion, the telecom industry in India is actively looking to improve the services provided to customers to ensure maximum customer satisfaction. From top-class networking solutions to better management of increasing databases using GPU cloud or other GPU online services to manage data hungry workloads efficiently, AI and MI-enabled solutions have taken the telecom industry by storm. Moreover, with the introduction of artificial intelligence and machine learning in this industry, the scope of innovation and improvement is higher than ever before.

 

 

References

https://www.inventiva.co.in/trends/telecom-startup-funding-inr-30-crore/

https://www.mygreatlearning.com/blog/top-ai-startups-in-india/

This is a decorative image for Top 7 AI Startups in Education Industry
June 29, 2022

Top 7 AI Startups in Education Industry

The evolution of the global education system is an interesting thing to watch. The way this whole sector has transformed in the past decade can make a great case study on how modern technology like artificial intelligence (AI) makes a tangible difference in human life. 

In this evolution, edtech startups have played a pivotal role. And, in this write-up, you will get a chance to learn about some of them. So, read on to explore more.

Top AI Startups in the Education Industry-

Following is a list of education startups that are making a difference in the way this sector is transforming –

  1. Miko

Miko started its operations in 2015 in Mumbai, Maharashtra. Miko has made a companion for children. This companion is a bot which is powered by AI technology. The bot is able to perform an array of functions like talking, responding, educating, providing entertainment, and also understanding a child’s requirements. Additionally, the bot can answer what the child asks. It can also carry out a guided discussion for clarifying any topic to the child. Miko bots are integrated with a companion app which allows parents to control them through their Android and iOS devices. 

  1. iNurture

iNurture was founded in 2005 in Bengaluru, Karnataka. It provides universities assistance with job-oriented UG and PG courses. It offers courses in IT, innovation, marketing leadership, business analytics, financial services, design and new media, and design. One of its popular products is KRACKiN. It is an AI-powered platform which engages students and provides employment with career guidance. 

  1. Verzeo

Verzeo started its operations in 2018 in Bengaluru, Karnataka. It is a platform based on AI and ML. It provides academic programmes involving multi-disciplinary learning that can later culminate in getting an internship. These programmes are in subjects like artificial intelligence, machine learning, digital marketing and robotics.

  1. EnglishEdge 

EnglishEdge was founded in Noida in 2012. EnglishEdge provides courses driven by AI for getting skilled in English. There are several programmes to polish your English skills through courses provided online like professional edge, conversation edge, grammar edge and professional edge. There is also a portable lab for schools using smart classes for teaching the language. 

  1. CollPoll

CollPoll was founded in 2013 in Bengaluru, Karnataka. The platform is mobile- and web-based. CollPoll helps in managing educational institutions. It helps in the management of admission, curriculum, timetable, placement, fees and other features. College or university administrators, faculty and students can share opinions, ideas and information on a central server from their Android and iOS phones.

  1. Thinkster

Thinkster was founded in 2010 in Bengaluru, Karnataka. Thinkster is a program for learning mathematics and it is based on AI. The program is specifically focused on teaching mathematics to K-12 students. Students get a personalised experience as classes are conducted in a one-on-one session with the tutors of mathematics. Teachers can give scores for daily worksheets along with personalised comments for the improvement of students. The platform uses AI to analyse students’ performance. You can access the app through Android and iOS devices.

  1. ByteLearn 

ByteLearn was founded in Noida in 2020. ByteLean is an assistant driven by artificial intelligence which helps mathematics teachers and other coaches to tutor students on its platform. It provides students attention in one-on-one sessions. ByteLearn also helps students with personalised practice sessions.

Key Highlights

  • High demand for AI-powered personalised education, adaptive learning and task automation is steering the market.
  • Several AI segments such as speech and image recognition, machine learning algorithms and natural language processing can radically enhance the learning system with automatic performance assessment, 24x7 tutoring and support and personalised lessons.
  • As per the market reports of P&S Intelligence, the worldwide AI in the education industry has a valuation of $1.1 billion as of 2019.
  • In 2030, it is projected to attain $25.7 billion, indicating a 32.9% CAGR from 2020 to 2030.

Bottom Line

Rising reliability on smart devices, huge spending on AI technologies and edtech and highly developed learning infrastructure are the primary contributors to the growth education sector has witnessed recently. Notably, artificial intelligence in the education sector will expand drastically. However, certain unmapped areas require innovations.

With experienced well-coordinated teams and engaging ideas, AI education startups can achieve great success.

Reference Links:

https://belitsoft.com/custom-elearning-development/ai-in-education/ai-in-edtech

https://www.emergenresearch.com/blog/top-10-leading-companies-in-the-artificial-intelligence-in-education-sector-market

https://xenoss.io/blog/ai-edtech-startups

https://riiid.com/en/about

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