Deep Learning Courses

Deep learning powers several artificial intelligence applications and services capable of enhancing automation and physical and analytical tasks without human interference. It is essential to learn deep learning because this technology is responsible for powering everyday products and services (for example - voice-enabled TV remotes, digital assistants, and credit card fraud recognition) and developing technologies (like self-driving cars).

Learning deep learning from scratch helps an individual to use it for areas like medical research, automated driving, industrial automation, aerospace and defence, electronics, health care, government, marketing, and sales.

In most cases, real-life deep learning applications are so impeccably integrated into everyday products and services that we are hardly aware of the complex data processing occurring in the background.

 The following section discusses deep learning usage in some prominent areas:

Financial services

A decent deep learning example can be its application in financial services. Financial institutions frequently use predictive analytics to execute algorithmic trading of stocks, evaluate business menaces for loan approvals, identify fraud, and organise client investment and credit portfolios.

AI in Marketing

Deep learning in data science is highly prevalent these days, using AI as an effective tool for customer service management. Implementing AI techniques facilitates enhanced speech recognition in call routing and call-center management. Consequently, customers benefit from a seamless experience.

 The deep learning ai can be the deep learning analysis of audio that lets systems evaluate the emotional tone of a customer. In this deep learning in ai example, if the customer responds poorly to an AI chatbot, that system can redirect the conversation to human operators.

Customer service

Another great deep learning example in real life is its use in customer service. Plenty of organisations employ deep learning technology in their customer service conduits.

For example, chatbots employed in a wide range of services, applications, and customer service portals are the direct form of AI. Traditional chatbots utilise natural language and visual recognition. But, more sophisticated deep learning chatbot solutions try to determine if multiple responses to uncertain questions exist. Depending on the responses received, the deep learning chatbot attempts to answer these questions directly or pass the discussion to a human user.

Virtual assistants like Amazon Alexa, Google Assistant, or Apple's Siri further outspread the idea of a chatbot using speech recognition functionality. It develops a new method to engross users in a tailored way.

Healthcare

The usefulness of deep learning in healthcare is highly prevalent because it owns a great ability to improve healthcare facilities for patients. The healthcare industry had hugely benefited from deep learning potential right from when the images and records in the healthcare industry got digitised.

The image recognition applications can help radiologists and medical imaging specialists (in the form of deep learning in medical imaging) to study more images in less time. Moreover, deep learning requires limited data to learn from.

Law enforcement

Deep learning algorithms can study and learn from transactional data to detect large perilous patterns indicating fraudulent or illegal doings. Speech recognition, computer vision and deep learning, and many other deep learning applications can enhance the efficiency of investigative analysis. This is possible by retrieving patterns and proof from images, video and sound recordings, and documents. As a result, law enforcement can effectively analyse huge amounts of data more accurately and rapidly.

Automated Driving

Automotive researchers use deep learning to identify objects like traffic lights and stop signs automatically. Deep learning is also useful for detecting pedestrians, and thus, it aids in reducing accidents.

Deep learning can capture the images nearby in self-driven cars by processing a large amount of data. Subsequently, it determines what actions must be accomplished –turning left, turning right, or stopping. Accordingly, there will be a reduction in accidents every year.

Industrial Automation

Deep learning helps enhance the safety of workers working close to heavy machinery. It automatically detects when objects or people are within a risky distance from machines.

Aerospace and Defence

Deep learning can recognise objects from satellites that trace areas of interest and recognise safe or unsafe regions for troops.

Automatic Image Caption Generation

The concept of image captioning deep learning is prevalent these days. After you upload the image, the algorithm accordingly generates a caption. For example, if you say brown-coloured hair, it displays brown-coloured hair with a caption at the bottom of the image.

Text generation

Deep learning machines are trained in the grammar and writing style of a text piece. Subsequently, a deep learning model automatically creates an entirely new text that matches the original text's writing style, grammar, and spelling.

Computer vision

Deep learning for computer vision is greatly enhanced to equip computers with high accuracy for image classification and object detection, restoration, and image segmentation deep learning.

The majority of the deep learning methods use deep neural network architectures. This is why deep learning models are commonly regarded as ‘deep neural networks’.

The term ‘deep’ in a neural network in deep learning typically relates to the number of hidden layers encased in the neural network. Usually, there are 2-3 hidden layers in traditional neural networks, but the deep network can include as many as 150.

Essentially, deep learning models are trained by implementing huge sets of characterised data and neural network architectures. These architectures and data directly study features from the data without undergoing manual feature extraction.

Artificial neural networks or deep neural networks try to imitate the human brain using a blend of data weights, inputs, and biases. These components work collectively to precisely distinguish, categorise, and define objects within the data.

In deep neural networks, multiple layers of mutually dependent nodes exist. Each layer builds upon the preceding layer to optimise the categorisation or prediction. The corresponding progression of computations through the network is termed ‘forward propagation’.

Input and output layers existing in a deep neural network are known as visible layers. In the input layer, the deep learning model inputs the data for processing. On the other hand, in the output layer, the final prediction or deep learning classification takes place. The classification in deep learning leads to accurate prediction.

Another important process in deep learning is called backpropagation. It uses algorithms, for example, gradient descent, to compute prediction errors. Subsequently, the algorithm fine-tunes the biases and weights of the function by shifting backwards over the layers in an attempt to train the model. The forward propagation and backpropagation work collectively to let a neural network in deep learning make predictions and resolve errors accordingly. With the repetitive use, the deep learning algorithm becomes progressively more precise.

The description above exemplifies the easiest kind of deep neural network. But deep learning algorithms are quite complex, and various types of neural networks are employed to solve specific problems.

For example, Convolutional neural networks (CNNs) are extensively used in deep learning for computer vision and image classification applications. They can detect patterns and features in an image and enable tasks like deep learning in object detection and recognition. Recurrent neural networks (RNNs) are widely used in natural language and speech recognition applications. The reason is that RNNs control sequential or time-series data.

Deep learning is extensively used nowadays because companies in various industries emphasise using cutting-edge computational techniques to discover useful information concealed over enormous volumes of data. Although the artificial intelligence domain is now decades old, innovations in artificial neural networks empower the eruption of deep learning.

The fields of AI and Deep Learning got a significant impetus because computers are getting closer to conveying human-level capabilities. Nowadays, consumers are overwhelmed with an assortment of chatbots such as Amazon‘s Alexa, Apple‘s Siri, and Microsoft‘s Cortana that employ natural language processing and machine learning to answer questions.

Currently, companies of all industries are targeting to present their big data deep learning sets as a training platform for developing sharper AI programs. These programs can extract valuable information and interrelate it with the world more naturally.

As per researchers, certain components lay the foundation for developing smart, self-learning machines and will begin rivalling humans in their insight. These components are cutting-edge neural networks, outstandingly powerful distributed GPU-based systems and the availability of huge volumes of training data.

Deep Learning algorithms are widely used in manufacturing industries because they transform complex, time-consuming, expensive manufacturing processes into easy-to-understand, quick, and cost-effective ones. Moreover, Deep learning instils problem-solving ability in manufacturers. This ability can surpass conventional machine vision applications. Deep Learning achieves this with excellent reliability and strength.

Deep learning software optimised for factory automation empowers companies in several industries to develop innovative inspection systems.  These systems drive the boundaries of machine vision and outline the future of industrial automation. Furthermore, these cutting-edge inspection systems blend the reliability and efficiency of a computerised system with the flexibility of human visual inspection.

By going through these three key points, you can understand why Deep Learning is used and why it's so popular. These points justify how Deep Learning is superior to classical machine learning algorithms:

• Deep learning models are independent of manual feature extraction. Instead, they can learn purposeful representations of the assigned features, called Representation Learning. Specifically, it is useful when handling non-trivial tasks wherein selecting an instructive subset of features can be very challenging.
• In the case of ample data and reasonable computational competence, the Deep Learning models usually perform better than traditional machine learning algorithms. The reason is they can signify a much wider set of functions.
• Certain Deep Learning architectures, for example, Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs), benefit from semantic knowledge in their architecture. This makes them predominantly competent in a group of tasks. For example, CNNs are influenced by the visual cortex.

Few more points justifying why to use Deep Learning:

• Deep Learning surpasses other learning approaches if the data size is big.
• In the absence of domain adaptation deep learning for feature introspection, Deep Learning methods surpass traditional learning techniques because they have to concern less regarding feature engineering.
• Deep Learning algorithms can solve complex problems related to natural language processing, image classification, and speech recognition.

Deep learning is a subdivision of machine learning. It uses algorithms for data processing, mimics the thinking process, and creates abstractions. Moreover, it uses layers of algorithms for data processing, understanding human speech, and recognising objects visually. Exploring its great features benefits myriad industries, but it is significant to know the associated history that illustrates how it's gradually developed. The below section illustrates Deep Learning history:

Development of Deep Learning Algorithms

The initial efforts in developing deep learning algorithms trace back to 1965. In that year, Valentin Grigorʹevich Lapa and Alexey Grigoryevich Ivakhnenko used models with polynomial activation functions and successively analysed these functions.

The 1970s-era marked a momentary impediment to the development of AI. During this time, restrictions were put on research in areas like artificial intelligence and deep learning due to a lack of funding. But, individuals continued the research without funding through those challenging years.

Kunihiko Fukushima was the first to use the convolutional neural network in deep learning, designing the deep learning neural networks with convolutional layers and multiple pooling. Moreover, in 1979, he developed an artificial neural network called Neocognitron. This network utilised a hierarchical and multi-layered design. This design facilitated the computer to learn to identify visual patterns. Furthermore, the networks simulated modern versions and were skilled with a reinforcement tactic of recurring activation in the multiple layers. These networks become more robust with time.

Development of the FORTRAN code for Back Propagation

The 1970s further saw a significant growth of backpropagation. It utilises errors in training the deep learning models. The concept of backpropagation gained popularity when Seppo Linnainmaa completed his master’s thesis, incorporating a FORTRAN code for backpropagation. Even though being developed in the 1970s, this concept was not deployed to neural networks till 1985. In 1985, Hinton and Rumelhart Williams validated the backpropagation concept in a neural network. This validation lets backpropagation offer remarkable distribution representations.

Yann LeCun enlightened the foremost practical manifestation of backpropagation, in 1989, at Bell Labs. This was done using a deep convolutional neural network and backpropagation to read the transcribed digits. Moreover, this combination of a backpropagation system and convolutional neural network in deep learning was adopted to read the numerals of transcribed checks.

The 1985-90s era perceived another hiatus in artificial intelligence. Consequently, it impacted the research of deep learning neural networks. Subsequently, in 1995, Dana Cortes and Vladimir Vapnik built the support vector machine. This machine was able to map and identify similar data. Sepp Hochreiter and Juergen Schmidhuber developed LSTM (Long short-term memory) in 1997. It proved useful for recurrent neural networks.

The year 1999 marked the next remarkable advancement in deep learning advancement. This year, computers used the speed of GPU processing. The advancement in deep learning led to quicker processing which increased the computational speeds by 1000 times across ten years. In that era, neural networks started competing with support vector machines. Neural networks used the same data as a support vector machine and offered better results but with slow speed.

Development of Deep Learning in the 2000s and beyond

The Vanishing Gradient Problem was announced in 2000 when the upper layers could not learn the “features” (lessons) created in the lower layers due to the absence of a learning signal in these layers. It was not a major problem for every neural network. However, it is limited to gradient-based learning methods only. Moreover, this problem resulted in some activation functions that compressed their input and decreased the output range in a disordered way. Consequently, large areas of input were mapped to an enormously small range.

In 2001, META Group (presently called Gartner) presented a consolidated research report.  This report demonstrated the opportunities and challenges of three-dimensional data growth. This report depicted the blitz of big data deep learning and demonstrated the increasing speed and volume of data with increasing the range of data types and sources.

In 2009, Fei-Fei Li (an AI professor at Stanford) launched ImageNet. It assembled a free database of 14+ million labelled images. All these images worked as the inputs to train the neural nets. By the year 2011, there was a significant increase in the speed of GPUs. This speed was adequate to train convolutional neural networks without requiring pre-training on each layer. So, by 2011, Deep learning showed noteworthy advantages in terms of speed and efficiency.

The Cat Experiment

In 2012, Google Brain launched the results of an atypical independent project known as ‘The Cat Experiment’. This project discovered the challenges of unsupervised learning. On the other hand, Deep learning sets up supervised learning that implies the convolutional neural network is trained through labelled data similar to the images from ImageNet.

The Cat Experiment used a neural network across 1,000 computers where 10 million untagged images (without labels) were randomly taken from YouTube. These images were taken for feeding inputs into the training software. From 2012 onwards, unsupervised learning remains a major goal in Deep Learning.

The year 2018 and beyond depicted the evolution of artificial intelligence. This evolution will be based on Deep Learning. Note that Deep learning is still in its growth phase. It constantly needs innovative ideas to advance further.

The detailed deep learning classification includes Pre-Processing, Learning, and Convolutional Neural Networks. Let’s understand each of them:

1. Pre-Processing

To understand how pre-processing takes place in Deep Learning, we first need background information on Variance and Covariance.

a. Variance and covariance

A variable’s variance describes the number of values spread. The covariance indicates the amount of dependency between any two variables. If the covariance value is positive, then the values of the first variables increase when the values of the second variable increase, and vice versa. If the value of covariance is negative, then the values of the first variables decrease when the values of the second variable increase, and vice versa.

Here is the formula to calculate variance:

Here, n = length of the vector

  = the mean of the vector

The formula to calculate the covariance between two variables X and Y:

Pre-processing implies all the transformation of raw data before it is fed to deep learning or machine learning algorithms. For example, training a deep convolutional neural network on raw images may result in bad classification performances. The Pre-processing component is also vital to speed up training, for example, using centring and scaling techniques.

b. Mean normalisation

The next important component of Pre-processing is Mean normalisation, which refers to removing the mean from every observation.

The formula to calculate Mean Normalisation:

Here, X’: normalised dataset

X: original dataset

: mean of X.

Mean normalisation centres the data around 0.

c. Standardisation

Standardisation puts all features on an identical scale. Every zero-centred dimension is divided by the corresponding standard deviation.

Here, X’: standardised dataset

X: original dataset

 x̅: mean of X

σx: standard deviation of X.

d. Whitening

Whitening (also called sphering) data means transforming the data to a covariance matrix, i.e. the identity matrix (1 in the diagonal and 0 for the remaining cells). It is called ‘Whitening’ because the values of the remaining cells are 0 in reference to the white noise.

Although whitening is slightly more complex than other pre-processing, all essential tools are available to perform it.

Whitening in Deep Learning involves the following steps:

1. Zero-center the data
2. Decorrelate the data
3. Rescale the data
   
   

2. Learning

The learning component is the most important one in Deep Learning. This component helps you create neurons in a computer. For that, we use an artificial structure known as an artificial neural network in which there are neurons and nodes. There are certain neurons for the input value and certain for the output value. In between, plenty of neurons in the hidden layer might be interrelated.

 a. Deep Neural Network

It is a kind of neural network with some specific complexity level. Multiple hidden layers exist in between the input and output layers. These values can model and process non-linear relationships.

 b. Deep Belief Network (DBN)

DBN is a multi-layer belief network belonging to a class of Deep Neural Networks.

 Here are the steps to perform DBN for accurate learning in Deep Learning:

1. Firstly, learn a layer of features from visible units with the help of the Contrastive Divergence algorithm.
2. Treat activations of formerly trained features as visible units and subsequently learn features of features.
3. Lastly, the entire DBN is trained when the learning for the last hidden layer is completed.
   
   

c. Recurrent Neural Network

Based on the idea of performing the same task for each sequence element, the learning of recurrent neural networks enables sequential and parallel computation. Its working is identical to the human brain; there is a huge feedback network of connected neurons. The connected neurons can store essential information about the input they obtained, making them more accurate.

3. Convolutional Neural Networks

In simple terms, a neural network is a sequence of algorithms that undertakes to recognise underlying relationships in a data set via a process that resembles how the human brain works. Neural networks relate to systems of neurons that are either artificial or organic.

Convolutional Neural Networks are unique neural networks primarily used for deep learning image classification, image clustering, and object detection deep learning. DNNs allow unsupervised development of hierarchical deep learning image representations. But to attain the best accuracy, deep convolutional neural networks are more preferred than any other neural networks.

In other words, a Deep Learning Convolutional Neural Network (CNN) is a deep learning neural network implemented for processing organised arrays of data like images. CNN is widely used in computer vision, and now it has transformed into state-of-the-art for various visual applications like image classification. Also, it got successful in natural language processing for text classification.

In deep learning, a convolutional neural network (brief as CNN or ConvNet) represents a class of deep neural networks typically employed to analyse visual imagery. When discussing a neural network, we would think about the matrix multiplications; however, this is not the case with CNN. It implements a special technique known as Convolution. From a mathematics viewpoint, convolution is a mathematical operation performed on two functions that generates a third function that shows how the shape of one is altered by the other.

Convolutional neural networks choose the patterns in the input deep learning image efficiently. The patterns include lines, circles, gradients, faces, and eyes. With this property, convolutional neural networks become so efficient for computer vision. Contrasting formerly released computer vision algorithms, CNN can directly operate on a raw image and doesn’t require any pre-processing.

A CNN is a feed-forward neural network, usually consisting of up to 20 or 30 layers. Moreover, the huge power of a convolutional neural network derives from a unique type of layer known as the convolutional layer. CNN is extensively used for image identification and classification. It influences the present-day healthcare industry and enhances the outcome of patients’ cures.

a. Convolutional Layer

The fundamental building block in a convolutional neural network is a convolutional layer. You can envisage a convolutional layer as several tiny square templates known as convolutional kernels sliding across the image and looking for patterns. The kernel will return a huge positive value for the specific part of the image that matches the kernel’s pattern. The kernel will return 0 or a smaller value if there is no match.

Plenty of convolutional layers stacked on one another are present in CNNs. Each of these layers is competent in identifying more refined shapes. It is possible to identify transcribed digits using 3 or 4 convolutional layers. While using 25 convolutional layers, it is possible to differentiate human faces via face detection deep learning.

The use of convolutional layers in a CNN resembles the construction of the human visual cortex. Its construction shows a sequence of layers that processes an incoming image and gradually identifies more complex features.

Applications of Convolutional Neural Networks:

• Identify Faces, Tumors, Street Signs
• Image Recognition
• Video Analysis
• Anomaly Detection Deep Learning
• NLP deep learning
• Checkers Game
• Drug Discovery
• Time Series Forecasting

A recurrent Neural Network (RNN) is a category of Neural Networks where outputs from the preceding step are provided as inputs to the present step.

In other words, an RNN is a kind of artificial neural network deep learning that employs time-series or sequential data. The corresponding deep learning algorithms are typically used for solving temporal or ordinal problems. For example, they can solve problems related to natural language processing NLP in deep learning, speech recognition, language translation, and image captioning.

The deep learning algorithms are assimilated into famous applications like voice search, Siri, and Google Translate. Like convolutional neural networks (CNNs) and feedforward, recurrent neural networks use training data for learning purposes. These networks are categorised by memory as they accept information from previous inputs to control the present input and output.

All inputs and outputs are independent of one another in traditional neural networks. But in those cases, when it is essential to foresee the succeeding word of a sentence, you will need previous words. So, it is necessary to recollect and remember the previous words. Therefore, RNN came into the picture and solved this problem using a Hidden Layer. The key and most significant feature of the RNN is the ‘Hidden state’ that remembers information about the sequence.

RNN contains a memory that stores all information regarding what was already calculated. For each input, it uses the same parameters. It carries out the same task on every input or hidden layer to generate the output. Consequently, it decreases the intricacy of parameters, contrasting other neural networks.

A unique characteristic of recurrent networks is the ability to share parameters over all the network layers. RNN adopts a backpropagation through time (BPTT) algorithm to define the gradients. Its method is slightly dissimilar from traditional backpropagation because it is explicit to sequence data. Both BPTT and traditional backpropagation use the same principles, i.e., the model trains itself by computing errors after mapping the output layer to the input layer. With these calculations, we can fine-tune and use the model's parameters correctly.

Example of RNN

Let’s understand the RNN deep learning example.  For example, we have the idiom “As sick as a dog”. This idiom is usually used to express when someone is very ill. It must be expressed following the mentioned order to have the idiom make sense. Consequently, recurrent networks must account for the location of every word in the idiom. They utilise that information for the prediction of the next word in order.

 Types of recurrent neural networks

• One-to-one
• One-to-many
• Many-to-one
• Many-to-many

The following section shows the benefits of pursuing online Deep Learning of Neural Networks Course compared to an offline course:

• One-on-one mentorship by industry experts
• Opportunity to develop your AI product skills with the real-world projects
• Easy-to-follow and accessible course material for free
• Comprehensive TensorFlow deep learning and deep learning with python exercises
• A decent blend of theory and practical exercises
• Hands-on and engaged tutors
• Complex topics are illustrated in easily understandable ways using advanced digital technologies
• Includes helpful illustrations to match theory with the deep learning example in real life
• Digitally and practically illustrates how neural networks interact with the real world
• Video material is quite easy to follow
• Complex ideas are explained in simple ways multiple times for a thorough understanding
• Competitive price point.

• Deep learning basics  
• Basics of neural networks
• Deep reinforcement learning
• Advanced deep architectures
• Digital signal processing
• Object-oriented programing
• Programming and data structures
• Design and analysis of algorithms
• Probability and statistics
• Big data analytics
• Machine learning techniques
• Machine learning I 
• Machine learning II
• Data communication and computer networks
• Deep Neural Networks (DNN)
• Convolutional neural networks
• Recurrent neural networks
• SQL and visualisation
• Automatic Speech Recognition
• Applications of deep learning Computing techniques
• Security in computing
• Practical network training  
• Novel deep methods (deep image and deep internal learning), Tools (tensor flow, PyTorch)

The outstanding industry growth of the Deep Learning of Neural Networks industry influences autonomous systems, Metaverse, art, etc.

Robotics, manufacturing, autonomous systems in the car industry, hospitality, and several other areas are expected to show prominent growth in 2022 due to the use of Deep Learning of Neural Networks. These areas will integrate Deep Learning with different hardware, implying the growing deep learning market.

Another area where Deep Learning in the Neural Networks industry will show growth is pattern recognition. Deep Learning is getting more detailed in terms of pattern recognition. Currently, we are not just discussing deep learning image recognition but its entire spectrum of identifying objects, shapes, and patterns and also illustrating them.

Discerning patterns on images or employing deep recurrent neural networks and demonstrating their time-series features, variable significance, causal inference, and centrality statistics are the prominent phases of deep learning advancement.

Deep Learning of Neural Networks industry growth will also be seen in Metaverse. The use of Deep Learning to design digital 3D worlds will perhaps be another huge aspect we will observe in 2022-23.

AI machine learning deep learning is gradually proving to be a crucial tool in designing Generative Digital Arts. For that, Deep Learning of Neural Networks is one of the key areas to look for. The use of high computation power enables designing the previously inconceivable generative arts, amalgams of millions of images, or abstract designs. Moreover, the development of Audio Deep Learning practice and NLP will facilitate AI-aided music composers with myriad options and enhanced workflows.

Pursuing Deep learning Neural Network courses in India can improve your career’s trajectory and fulfil the ever-increasing demand for practised data scientists. Since the world is getting digitised, and with AI and Machine learning becoming more advanced with time, the Deep learning of Neural Network course can boost your career. These courses can optimise your skills in working on algorithms and problem-solving skills.

The demand for these courses is high in India because deep learning processes rapidly and precisely voluminous data. It is prevalent in industries including manufacturing, healthcare, and deep learning in finance.

These courses emphasise imparting fundamental Deep Learning of Neural Network skills, machine learning algorithms, and many more.  Hence, you can kick-start your career and land your dream job in the leading industry. Moreover, these courses aim at improving your logical skills, predictive analytical skills, decision-making abilities, etc.

These courses suit candidates willing to master AI, machine learning, or Deep Learning. Moreover, it imparts knowledge on using the frequently used tools at workplaces. These tools are AI and Deep Learning tools, so candidates become industry-ready.

After completing the Deep learning of Neural Network course in India, the provided training certificate will allow you to showcase your skills and accelerate your career. Moreover, after completing these courses, you can work on quizzes, real case studies, assignments, and many more.

Completing the project work and scoring well in quizzes and interviews provide you with a course certificate. Subsequently, you can be eligible to apply for multiple posts in MNCs in India and throughout the world.

A few of the leading companies hiring Deep Learning specialists in India are Microsoft, Amazon, Intel, Samsung, Accenture, IBM, Facebook, etc.

The salary of a Deep Learning of Neural Networks Specialist in India can differ based on several factors. Here we outline a few factors:

• Salary based on job titles
• Salary based on skills
• Salary based on educational qualification
• Salary based on experience

1. Salary based on job titles

Here is the list of top-paying job titles for Deep Learning of Neural Networks Specialist in India:

• AI/ML Specialist
• (Senior) Simulation Specialist
•  Senior Specialist - Data Scientist
•  Lead Artificial Intelligence Specialist
• Machine Learning Consultant
• Senior Specialist, 5G System Simulation
• Artificial intelligence security specialist (Senior)

 2. Salary based on skills

The following list shows the skills most reliable employers look for when hiring Deep Learning of Neural Networks Specialists in India:

• Big Data Analytics
• Python/C++ Programming Language
• Software Development
• Natural Language Processing
• Computer Vision
• Deep Learning Image Processing
• Data Modelling
• Data Analysis
• Statistical skills
• Data modelling, data structures, and software architecture

Different ML and DL frameworks and libraries such as Keras, TensorFlow, deep learning with PyTorch, Caffe, Theano, DeepLearning4J, etc.

Verbal communication skills, analytical skills, and problem-solving skills

3. Salary based on educational qualifications

 4. Salary based on experience

The salary of a Deep Learning of Neural Networks Specialist Abroad can differ based on several factors. Here we outline a few factors:

• Salary based on job titles
• Salary based on employer
• Salary based on location

1. Salary based on job titles

Source

 2. Salary based on Employer

Source

 3. Salary based on the job location

Highest paying cities for Deep Learning Specialists:

Source