PROGRAM DETAILS

Course

Training & Internship - Deep Learning with TensorFlow

3 months

PROGRAM DETAILS

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.

 

 

 Duration   

 

 3 Months   

 

 Upcoming   Batch date   

 

 29, June 2019  (Weekend Saturday/Sunday Batch)  

 

 Training   Options   

 

 

 Timings for online class

   

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

 

 

 
 Fees     

 

 

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

 

 

 

 Contact   

 

 

 Please call Naveen at +91 79944 28133 to confirm your registration.

 

 

 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

 Prerequisites 

 

 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
    TensorBoard


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.

 

Recorded videos:

 

 

 

 

 

 

 

 

 

 

 

 

 

Next steps?

 

If you would like to join this program please register below:

 

REGISTER FOR THIS PROGRAM