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Program Overview

What you'll learn


Python is an interpreted, high-level, general-purpose programming language.

SciKit Learn

Scikit-learn is a free software machine learning library for the Python programming language. It features various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy


TensorFlow is a free and open-source software library for dataflow and differentiable programming across a range of tasks. It is a symbolic math library, and is also used for machine learning applications such as neural networks.


Keras is an open-source neural-network library written in Python. It is capable of running on top of TensorFlow, Microsoft Cognitive Toolkit, R, Theano, or PlaidML.


Best-in-class content by leading faculty and industry leaders in the form of videos, cases and projects

Introduction to Deep Learning
  • Define Deep Learning
  • Neural Networks
  • Deep Learning Applications
  • What is a Perceptron
  • Logic Gates with Perceptron
  • Activation Functions
  • Sigmoid
  • ReLU
  • Softmax
  • Hyperbolic Functions
How to Train ANNs
  • Introduction
  • Perceptron Learning Rule
  • Gradient Descent Rule
  • Minimize Cost Function
  • Tuning Learning Rate
  • Stochastic vs Batch Gradient Descent
Multi-Layer ANN
  • Intro to MLP
  • Forward Propagation
  • Minimize Cost Function
Introduction to TensorFlow
  • Intro to TensorFlow
  • Computational Graph
  • Key Highlights
  • Creating a Graph
  • Regression Example
  • Gradient Descent
  • Saving and Restoring Models
  • Tf.layers API
  • Keras-Based networks
  • TensorBoard
Training Deep Neural Nets
  • Vanishing/Exploding Gradients
  • Xavier Initialization
  • Leaky ReLUs and ELUs
  • Batch Normalization
  • Transfer Learning
  • Naive Bayes
  • Removing a Container
  • Unsupervised Pre-Training
Convolutional Neural Networks
  • Intro to CNNs
  • Convolution Operation
  • Kernel Filter
  • Feature Maps
  • Pooling
  • CNN Architecture
  • Implement CNN in Tensor Flow
Recurrent Neural Networks
  • Intro to RNNs
  • Unfolded RNNs
  • Basic RNN Cell
  • Dynamic RNN
  • Training RNNs
  • Time-series predictions
  • LSTM
  • Word Embedding’s
  • Seq2Seq Models
  • Implement RNN in TensorFlow


Executive Program in Deep Learning Technology Certified By Google


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Frequently Asked Questions

Deep Learning involves taking large volumes of structured or unstructured data and using complex algorithms to train neural networks. It performs complex operations to extract hidden patterns and features (for instance, distinguishing the image of a cat from that of a dog).
The process of standardizing and reforming data is called “Data Normalization.” It’s a pre-processing step to eliminate data redundancy. Often, data comes in, and you get the same information in different formats. In these cases, you should rescale values to fit into a particular range, achieving better convergence.
One of the most basic Deep Learning models is a Boltzmann Machine, resembling a simplified version of the Multi-Layer Perceptron. This model features a visible input layer and a hidden layer -- just a two-layer neural net that makes stochastic decisions as to whether a neuron should be on or off. Nodes are connected across layers, but no two nodes of the same layer are connected.
The RNN can be used for sentiment analysis, text mining, and image captioning. Recurrent Neural Networks can also address time series problems such as predicting the prices of stocks in a month or quarter.
Tensorflow provides both C++ and Python APIs, making it easier to work on and has a faster compilation time compared to other Deep Learning libraries like Keras and Torch. Tensorflow supports both CPU and GPU computing devices.
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