Training & Internship - Deep Learning with TensorFlow

3 months


This course will cover the fundamentals and contemporary usage of the TensorFlow library for deep learning research. We aim to help students understand the graphical computational model of TensorFlow, explore the functions it has to offer, and learn how to build and structure models best suited for a deep learning project. Through the course, students will use TensorFlow to build models of different complexity, from simple linear/logistic regression to convolutional neural network and recurrent neural networks to solve tasks such as word embedding, translation, optical character recognition, reinforcement learning. Students will also learn best practices to structure a model and manage research experiments.


TensorFlow is a powerful open-source software library for machine learning developed by researchers at Google. It has many pre-built functions to ease the task of building different neural networks. TensorFlow allows distribution of computation across different computers, as well as multiple CPUs and GPUs within a single machine. TensorFlow provides a Python API, as well as a less documented C++ API. For this course, we will be using Python.





 3 Months   


 Upcoming   Batch date   


 Please contact us from


 Training   Options   



 Timings for online class


 3 hours Online class sessions on Saturdays & Sundays [Class timings: 2 pm - 5 pm]






 Rs. 19,980/-  or  Rs. 6,660/- x  3 Installments 







 Please register using the form at the end of this page.



 Target   Audience   



 IT Professionals:- Those who would like to switch their field or upgrade their skills in ML and Data Science.

 Students :- Those who aspire to form their career in the field of AI

 Anyone Who love Data Science and AI



 Proficiency in Python:

All class assignments will be in Python. There is a tutorial here for those who aren't as familiar with Python. If you have a lot of programming experience but in a different language (e.g. C/C++/Matlab/Javascript), you will probably be fine.


 Knowledge in Machine Learning:

We will not ask you to take derivatives or build your own optimizers, but you should know what they are and how to use them.

 Basic Theoretical Understanding of Neural Networks:

This course focuses more on the practical usage of Tensorflow in deep learning projects, therefore you can benefit more from the course if you already have basic understanding of neural networks: feed-forward, convnet, LSTM, sequence to sequence model.




Course Content

Deep Learning in Python
    Overview of important python packages for Deep Learning


Tensor Flow
    What is Tensor Flow?
    Tensor Flow code-basics
    Graph Visualization
    Constants, Placeholders, Variables
    Tensorflow Basic Operations
    Linear Regression with Tensor Flow
    Logistic Regression with Tensor Flow
    K Nearest Neighbor algorithm with Tensor Flow
    K-Means classifier with Tensor Flow
    Random Forest classifier with Tensor Flow


Neural Networks using Tensor Flow
    Quick recap of Neural Networks
    Activation Functions, hidden layers, hidden units
    Illustrate & Training a Perceptron
    Important Parameters of Perceptron
    Understand limitations of A Single Layer Perceptron
    Illustrate Multi-Layer Perceptron
    Back-propagation – Learning Algorithm
    Understand Back-propagation – Using Neural Network Example

Deep Learning Networks
    What is Deep Learning Networks?
    Why Deep Learning Networks?
    How Deep Learning Works?
    Feature Extraction
    Working of Deep Network
    Training using Backpropagation
    Variants of Gradient Descent
    Types of Deep Networks
    Feed forward Neural Networks (FNN)
    Convolutional Neural Networks (CNN)
    Recurrent Neural Networks (RNN)
    Generative Adversal Neural Networks (GAN)
    Restrict Boltzman Machine (RBM)

Convolutional Neural Networks (CNN)
    Introduction to Convolutional Neural Networks
    CNN Applications
    Architecture of a Convolutional Neural Network
    Convolution and Pooling layers in a CNN
    Understanding and Visualizing a CNN
    Transfer Learning and Fine-tuning Convolutional Neural Networks


Recurrent Neural Network (RNN)
    Intro to RNN Model
    Application use cases of RNN
    Modelling sequences
    Training RNNs with Backpropagation
    Long Short-Term Memory (LSTM)
    Recursive Neural Tensor Network Theory
    Recurrent Neural Network Model


Restricted Boltzmann Machine (RBM)
    What is Restricted Boltzmann Machine?
    Applications of RBM
    Collaborative Filtering with RBM
    Introduction to Autoencoders & Applications
    Understanding Autoencoders


Deep Learning with Keras
    Define Keras
    How to compose Models in Keras
    Sequential Composition
    Functional Composition
    Predefined Neural Network Layers
    What is Batch Normalization
    Saving and Loading a model with Keras
    Customizing the Training Process
    Using TensorBoard with Keras
    Use-Case Implementation with Keras
    Intuitively building networks with Keras

Natural Language Processing (NLP)
   Introduction to NLP
   NLP Libraries
   Case studies on NLP

Internship Project:


Deep Learning with TensorFlow project using Industrial Application


  • NLP Chatbot
  • Text Classification
  • Face & Image Recognition
  • Object Detection
  • Deep Fake 
  • Stock Price Prediction 
  • Fraud Detection
  • Recommender System


Key Takeaways from the Deep Learning course:

  • Cutting edge coding skills for Deep Learning
  • Assignments and Review every Week.
  • Hands on Industrial project use case at the end of the course.
  • Certification after completing all the Assignment and Internship.
  • Placement and Internship Opportunities.


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Next steps?


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