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Deep learning is one of the promising segments of Machine learning and Artificial Intelligence technologies. Besant Technologies Deep learning Online Certification training with TensorFlow is designed by industry professionals as per the industry demands and requirements. Deep Learning training with TensorFlow is very much helpful for you to master in building the Deep learning models, Autoencoder Neural Networks, SoftMax function, Restricted Boltzmann Machine, and Keras technology.

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Upcoming Batch Schedule for Deep Learning Online Training

Besant Technologies provides flexible timings to all our students. Here is the Deep Learning Online Training Schedule for our branch. If this schedule doesn’t match please let us know. We will try to arrange appropriate timings based on your flexible timings.

  • 01-11-2021 Mon (Mon - Fri)Weekdays Batch 08:00 AM (IST)(Class 1Hr - 1:30Hrs) / Per SessionGet Fees
  • 28-10-2021 Thu (Mon - Fri)Weekdays Batch 08:00 AM (IST)(Class 1Hr - 1:30Hrs) / Per SessionGet Fees
  • 30-10-2021Sat (Sat - Sun)Weekend Batch11:00 AM (IST) (Class 3Hrs) / Per SessionGet Fees
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Deep Learning Training Course Syllabus

Introduction to Deep learning:

Objective: In this module, you will get a basic understanding of deep learning and what kind of problems deep learning will address. Also, get clarity about the difference between Machine learning and Deep learning.

  • Origin of Deep Learning
  • Machine Learning limitations
  • Introduction about Deep Learning
  • Deep Learning advantages and Machine Learning limitations
  • Real-life use cases
  • Brush up Machine Learning concepts

Hands-on: How to implement linear regression and logistic regression using Deep learning

Understanding the Neural network with TensorFlow:

Objective: In this module, you will get an idea about the deep learning structure and how we can build it using Neurons (Perceptron). You will familiarize yourself with different activation functions and have a brief introduction to the TensorFlow framework.

  • Structure and working of Deep Learning
  • Detailed explanation about Perceptron
  • Different Activation functions
  • Introduction to TensorFlow
  • What is the computational graph?
  • Basic TensorFlow coding and graph visualization
  • Brief introduction about Variables, Constants and Place Holders
  • Creating a simple TensorFlow model

Hands-on: Build a classification model using TensorFlow

Deep dive into Neural Network with TensorFlow:

Objective: In this module, you will be learning about Deep Neural Networks and how does it work. Also, you will get more understanding of forwarding and backward propagation.

  • Different layers in the Neural network
  • Understanding Neural Network in detail
  • Introduction to Multi-layer Perceptron
  • What is Forward propagation and Backpropagation?
  • Build a Multi-layer perceptron model using TensorFlow
  • Familiarise in using Tensor Board

Hands-on: Build a deep neural network to classify digits in MNIST dataset

Master Deep Networks:

Objective: In this module, you will get more hands-on for the TensorFlow framework. You will get to know more details about the data flow in TensorFlow.

  • What is a Deep Neural Network?
  • How Deep Neural Network helps to increase accuracy?
  • Understanding the working of Deep Neural Network
  • What is Weight and Bias, how it is getting updated?
  • How gradient descent is useful to update parameters?
  • Types of Deep Networks

Hands-on: Building a classification model using a Multi-layered perceptron.

Convolutional Neural Network(CNN):

Objective: In this module, you will be learning what convolutional neural network is and how it is different from Feedforward neural network. Also, you will get an understanding of various layers in the Convolutional Neural Network and what are the real-time applications of CNN.

  • Introduction to CNN
  • Advantage of CNN over other Neural Networks
  • Applications of CNN
  • Architecture of CNN
  • Different layers and its use to build a CNN model
  • Real-time use cases of CNN

Hands-on: Building an Image classification model using CNN

Recurrent Neural Network (RNN):

Objective: In this module, you will get an understanding of what is RNN and it’s working. Also, get to know the advantage of RNN over other Neural Network models and you will be familiarised with LSTM, why we need to use LSTM and the real-time application of LSTM.

  • Introduction to RNN
  • How RNN is different from other Neural Network models
  • Structure and working of RNN
  • Exploding and Vanishing Gradient descend problem
  • Long Short-Term Memory (LSTM)
  • How LSTM overcome the problem of Vanishing Gradient descent?
  • Real-time use-cases of LSTM

Hands-on: Building an RNN model to predict the next word in the sentence.

Restricted Boltzmann Machine (RBM) and Autoencoders:

Objective: In this module, you will get a great understanding of RBM and Autoencoders. Also, you will get an idea about how Autoencoders different from PCA and you will be gone through some real-time applications of RBM and Autoencoders.

  • What is Restricted Boltzmann Machine (RBM)
  • Applications of RBM
  • How to do Collaborative Filtering with RBM?
  • Introduction to Autoencoders
  • Autoencoders applications
  • Understanding Autoencoders and how it is different from PCA

Hands-on: Predicting the customer rating for each movie.

Keras API:

Objective: In this module, you will get to know the various functions and features of Keras API and how does it use. After this session, you will be able to develop a Neural Network model using Keras API.

  • Introduction to Keras
  • How to build a Models in Keras using TensorFlow backend
  • Sequential and Functional Composition
  • Explaining Predefined Neural Network Layers
  • What is Batch Normalization?
  • How to save and load a model
  • Using TensorBoard with Keras

Hands-on: Building an image classification model using Keras

TFLearn API:

Objective: In this module, you will get to know the different functionalities and features of TFLearn API and how does it use. After this session, you will be able to develop a Neural Network model using TFLearn API.

  • Introduction to TFLearn
  • How to build a Models in TFLearn using TensorFlow backend
  • Sequential and Functional Composition
  • Explaining Predefined Neural Network Layers
  • What is Batch Normalization?
  • How to save and load a model
  • Using TensorBoard with TFLearn

Hands-on: Building a Neural network model to classify the digits in MNIST dataset using TFLearn

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About Deep Learning Online Training Course

Deep Learning Online training course with TensorFlow provided by Besant Technologies will enclose the concepts of Artificial Intelligence, Deep learning frameworks, exploring neural networks, and implementation of machine learning algorithms with Deep networks. This real-time training will also examine you to learn different layers in neural networks and data abstraction features in Deep learning. Our hands-on Deep learning online training course is a stepping stone in your journey towards Data Science.

This placement oriented training ensures you to get good knowledge with real-time project support. We have the best trainers who are certified in Deep learning. Our trainers give you the best online training along with the lab practices in all trending Deep learning tools. Deep Learning advancements can be seen in creating power grid efficiency, smartphone applications, improving agricultural yields, advancements in healthcare, and finding climate change. With the help of this Deep Learning online course, one can know how to manage neural networks and interpret the results. The average salary for engineers with Deep learning skills is about $120,000 per annum.

What will you learn in this Deep Learning online training?

During the course period of this Deep Learning online training, we’ll cover the following topics:

Neural Networks:

Building blocks – backpropagation, hyperparameters, feedforward, softmax, gradient descent, cross-entropy loss.

Deep Neural Networks:

Implementation Deep neural networks – hyperparameter selections, learning rate, weight initialization.

Computer Vision:

  • Conventional Neural Networks (CNN).
  • Semantic segmentation, detection, and localization.
  • CNN image processing.
  • CNN architecture.

Natural Language Processing:

  • Recurrent Neural Networks (RNN).
  • Word embedding – Glove, word2vec.
  • Long-short term memory.
  • NLP techniques – POS tagging, Tokenization, Bag of words.

Deep learning on AWS:

  • Alexa badge voice technology.
  • AWS DeepLens Reinforcement learning.

Keras API:

  • Composing Keras models.
  • Functional and Sequential composition.
  • Batch Normalization.

What are the Objectives of Deep Learning Online Training?

By the end of this Deep Learning online training course, you will able to achieve the following:

  • Understanding neural networks, implementing deep learning algorithms and traversing data abstraction layers which will make you understand the structure of data.
  • Understanding TensorFlow concepts, operations, and functions.
  • Become a master in advanced topics like recurrent neural networks and conventional neural networks.
  • Building Deep Learning models with TensorFlow and interpreting the results.
  • Improve and troubleshoot deep learning models.
  • Differentiating Artificial Intelligence, Deep Learning, and Machine learning.
  • Building the own deep learning project.

Who should attend this Deep Learning Certification Course?

The prerequisites for learning Deep learning course is one must have the basic knowledge in the following languages and tools:

The Deep Learning certification course is for all the professionals and experts who are passionate about Deep learning, and also this course is suitable for:

  • Analytics Managers.
  • Developers who want to become “Data Scientists.”
  • Information Architects who are willing to gain expertise in predictive analytics.
  • Business Analysts.
  • Data Science Analysts.

What are the prerequisites for learning Deep Learning Online Training Course?

The prerequisites for learning Deep learning course is one must have the basic knowledge in the following languages and tools:

  • Python Programming Language.
  • Python libraries like Numpy, Pandas, Scikit-learn, Pandas, Scipy.
  • Natural Language Processing libraries.
  • Matplotlib.
  • Github repositories.

What are Deep Learning job opportunities?

The job opportunities for Deep learning are extremely very attractive. If you trained and certified in Deep learning with TensorFlow, then you crack the job easily. Deep learning online training provided by Besant Technologies is carefully designed in such a way that it is beneficial for both beginners and experts. Our training team will forward your resume to the reputed companies and provide you 24/7 job support.

The following are some of the Deep learning Job opportunities:

  • Technical Experts in Deep Learning
  • Speech Scientist
  • Computer vision/Machine learning expert
  • Deep Learning specialist manager
  • Chief Platform Architect – AI/ML

What is TensorFlow?

TensorFlow is one of the Deep learning libraries developed by Google, which uses Machine learning in its products to improve the translation, search engine, and image captioning. TensorFlow takes input as a multidimensional array and constructs flowchart operations that are going to perform on the input. Google users can experience a more refined search with Artificial Intelligence.

If the user types a word in the search bar, Google displays the recommendations, what could be the next word. Google also uses Machine learning to take advantage of considerable data sets to give the best experience for users. Google doesn’t have massive data; that’s why it developed TensorFlow to accelerate Machine learning, Deep learning, and neural network research.

Example:

TensorFlow can be used to make algorithms in visualizing the objects in a photograph. It can also make the computer recognize the image objects.

Deep learning with TensorFlow

TensorFlow is a software library for Deep learning which is used for mathematical computation. It uses data flow graphs to represent multidimensional data arrays and mathematical operations. Google created it for Machine learning, but it is widely used for Deep Learning in developing solutions. Initially, load data on traffic signs and explore it with simple plotting and statistics. In this exploration process, the user needs to manipulate the data in such a way that it is possible to feed the data into the required model. That’s why it takes a lot of time to rescale images.

Example:

The best application of Deep Learning with TensorFlow is pattern recognition. It’s been applied to video, images, voice, sound, time-series data, and text. It classifies the data with human accuracy. It can also pick your application: turns as a lie detector by recognizing facial expressions.

Applications:
  • Video Recognition
  • Text Summarization
  • Speech Recognition Systems
  • Sentiment Analysis
  • Self-Driving Cars

Deep learning with python:

Deep Learning is considered as one of the powerful machine learning techniques. Python is a high-level programming language that can be used for producing algorithms for Deep learning. Deep learning libraries are available on the Python ecosystem, such as TensorFlow, Keras, and Theano. Nowadays, the primary Python scripting skills are enough to do advance research in Deep Learning. Python libraries can provide functions in building deep nets that can train quickly on the machines. Let’s evaluate the mathematical expressions with matrices and vectors. Technically speaking, both the input data and neural sets are represented as matrices. In this, the developer can use a single machine with GPU. But, Python allows the implementation of vectorized functions.

Example:

By using Deep Learning, we can zoom the videos beyond the resolution. In the year 2017, Google researchers trained in Deep Learning and predicted faces in low-resolution images. The resolution of pixel recursive works on photos to expand to a great extent.

Applications:
  • Pixel Restoration
  • Describing Pictures
  • Changing Gaze in Photos
  • Real-Time Analysis of human behavior
  • Translation
  • Generating Pictures of Volcanoes and Galaxies
  • Creating New Images
  • Searching for Text in Videos and Images
  • Generating Voice

What are neural networks?

Neural networks are algorithm sets that are modeled after the human brain to recognize patterns. Neural networks interpret the sensory data through clustering and labeling raw input and machine perception. The designs of Neural networks are numerical and contained in vectors. They can also clarify the clusters. Mostly, neural networks include computational unit layers with different connections; those layers are called neurons. These networks can able to transform the data to classify as an output. Each neuron multiplies the initial value by sum results or some benefits into the same neuron. Adjusting of the resulting number can be made by neuron’s bias and normalize the activation function output.

Example:

A neural network called DeepText can understand the textual content of thousands of posts within a fraction of seconds. This application can be used on Facebook.

Applications:
  • Text classification
  • Named Entity Recognition (NER)
  • Semantic Parsing
  • Part-of-Speech Tagging
  • Paraphrase Detection
  • Multi-document Summarization
  • Speech Recognition
  • Spell Checking
  • Character Recognition
  • Machine Translation

Natural Language Processing (NLP):

Natural language processing is a subfield of computer science, artificial intelligence, and information engineering concerned with the relation between human (natural) languages and computer languages. NLP also teaches the machines to perform the tasks of natural languages such as dialogue and machine translation. For a long time, the majority of problems to study NLP problems are time-consuming, shallow machine learning models, and hand-crafted features. Natural Language Processing separates the words into morphemes and identifies the class of every individual morpheme. This task difficulty depends on the structure of words being considered. The English language has simple morphology, which leads to the possibility of separating the words quickly. Such an approach is not possible in languages like Meitei and Turkish.

Example:

Natural language processing represents the observed text history with succinctly to predict the next word. Then it puts the predicted words in a sequence. This whole process is known as Language Modeling.

Applications:
  • Language Modeling
  • Speech Recognition
  • Text Classification
  • Caption Generation
  • Document Summarization
  • Machine Translation
  • Question Answering

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Trainer Profile of Deep Learning Online Training

Our Trainers provide complete freedom to the students, to explore the subject and learn based on real-time examples. Our trainers help the candidates in completing their projects and even prepare them for interview questions and answers. Candidates are free to ask any questions at any time.

  • More than 7+ Years of Experience.
  • Trained more than 2000+ students in a year.
  • Strong Theoretical & Practical Knowledge.
  • Certified Professionals with High Grade.
  • Well connected with Hiring HRs in multinational companies.
  • Expert level Subject Knowledge and fully up-to-date on real-world industry applications.
  • Trainers have Experienced on multiple real-time projects in their Industries.
  • Our Trainers are working in multinational companies such as CTS, TCS, HCL Technologies, ZOHO, Birlasoft, IBM, Microsoft, HP, Scope, Philips Technologies etc

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Deep Learning Exams & Certification

Besant Technologies Certification is Accredited by all major Global Companies around the world. We provide after completion of the theoretical and practical sessions to fresher’s as well as corporate trainees.

Our certification at Besant Technologies is accredited worldwide. It increases the value of your resume and you can attain leading job posts with the help of this certification in leading MNC’s of the world. The certification is only provided after successful completion of our training and practical based projects.

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Deep Learning Certification Training Key Features:

30+ Hours Course Duration

100% Job Oriented Training

Industry Expert Faculties

Free Demo Class Available

Completed 800+ Batches

Certification Guidance

Projects of Deep Learning Online Training

Project title – CNN based face detector from dlib

Industry – Artificial Intelligence

Problem Statement – Detect the face in all angles with the highest Accuracy.

Topics – This detector is based on the histogram of oriented gradients (HOG) and linear SVM. Explaining how

these detector works are beyond the scope of this project.

Highlights

I have majorly used dlib for face detection and facial landmark detection. The frontal face detector in dlib works really well. It is simple and just works out of the box.

Group Discount

If you have Three or more people in your training we will be delighted to offer you a group discount.

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Additional Info of Deep Learning Online Training

What is Pytorch?

Pytorch is a package of Python-based scientific computing targeted for providing flexibility in Deep learning research platform and high-level features like tensor computations. It is also one of the Deep learning libraries, and it gives a fierce competition for TensorFlow, especially in the research work. It allows Deep learning scientists, neural network debuggers, and Machine learning developers to run the code in real-time.

Example:

Pytorch is used to build the Convolutional Neural Networks (CNNs). These networks fed the images with specific things, for example, a dog or a kitten. Suppose, if the CNN sees kitten images, it collects the data sets of that particular image. This application is used in healthcare to detect skin cancer.

Applications:
  • Handwriting recognition
  • Forecast time sequences
  • Text generation
  • Style transfer
Advantages of Pytorch:
Pythonic in nature:
  • Pytorch library can smoothly integrate with the stack of Python data science. It can leverage all the functionalities and services which are offered by Python environment.
Simple Interface:
  • Pytorch offers API usage; it is very easy to run and operate Python.
Computational graphs:
  • PyTorch provides the best platform in offering the dynamic computational graphs and can change them according to the run time. This can be useful when you have no idea about creating the neural network model.

What is Keras?

Keras is a Python Neural network library that runs on top of TensorFlow or Theano. It is designed to be fast, modular, and easy to use. A Google engineer Francois Chollet developed it. Keras handles only high-level computation. For low-level computation, it uses a “Backend engine.” Keras high-level API can handle multiple input-output models, and it can also compile the model with optimizer functions.

Example:

Keras is used in face recognition. It identifies the person on video frames or images. In a null set, Keras extract the image features and compare them with the labeled faces in a database.

Applications:
  • Predicting images
  • Fine-tuning
  • Feature extraction
Advantages of Keras:
  • Keras can make quick network models.
  • The user can deploy Keras on many devices such as a Web browser, TensorFlow Android, Cloud engine, iOS with Core Machine learning.
  • Keras can also support multiple GPUs at a time and support data parallelism.

Training Courses Reviews

It was a great experience to undergo the Deep Learning Training course from Besant Technologies. Classes were informative, and the Instructor was able to clear all my queries. The best part of Besant Technologies is its 24*7 support system. Thank you, Besant Technologies, for the excellent training.

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Sharath

Besant Technologies provides excellent training in the Deep Learning course. I am very pleased with this live online training. My Instructor was very Knowledgeable committed, and he explains everything with patience. The quality of content that Besant Technologies had was too good.

R

Riya

Recently, I have opted for Deep Learning Certification Training from Besant Technologies. The classes were informative. The maintenance of class timings was perfect. I highly recommend for anyone willing to take Deep Learning training from Besant Technologies.

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Naveen

I recently took Deep Learning Online Certification Training from Besant Technologies. I’m happy with the class lectures and the Besant Technologies team. They also provide practical training which helped me to learn very quickly. It’s been a great experience.

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Bala

I recently took training in the Deep Learning Certification Course from Besant Technologies. The trainer was very responsive to all the questions I asked. I am thankful to the management of Besant Technologies for their support throughout my training period.

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Vanitha

Last month, I enrolled in Deep Learning Certification training at Besant Technologies. The trainer explains every topic with real-time examples. He covered all the topics like Deep Learning with Python, TensorFlow, Neural networks, Natural Language Processing, Pytorch, Keras, and so on. I can say Besant Technologies is one of the best institutes in providing Deep Learning online training.

V

Venkat

I recently completed my Deep Learning training from Besant Technologies. It was a great learning experience in the training period of my Deep Learning course. The trainer explained every concept very patiently. The team at Besant Technologies is committed to helping their students achieve the best results. Overall I had a good experience with Besant Technologies.

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Besant Technologies offers 250+ IT training courses in more than 20+ branches all over India with 10+ years of Experienced Expert level Trainers.

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Besant Technologies is the Legend in offering placement to the students. Please visit our Placed Students List on our website.

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  • Our trainers are more than 10+ years of experience in course relavent technologies.
  • Trainers are expert level and fully up-to-date in the subjects they teach because they continue to spend time working on real-world industry applications.
  • Trainers have experienced on multiple real-time projects in their industries.
  • Are working professionals working in multinational companies such as CTS, TCS, HCL Technologies, ZOHO, Birlasoft, IBM, Microsoft, HP, Scope, Philips Technologies, etc…
  • Trained more than 2000+ students in a year.
  • Strong theoretical & practical knowledge.
  • Are certified professionals with high grade.
  • Are well connected with hiring HRs in multinational companies.

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