Introduction to Deep Learning
Learning Objectives:
In this module, you’ll get an introduction to Deep Learning and understand how Deep Learning solves problems which Machine Learning cannot. Understand the fundamentals of Machine Learning and relevant topics of Linear Algebra and Statistics.
Topics:
Deep Learning: A revolution in Artificial Intelligence
Limitations of Machine Learning
What is Deep Learning?
The advantage of Deep Learning over Machine learning
3 Reasons to go for Deep Learning
Real-Life use cases of Deep Learning
Review of Machine Learning: Regression, Classification, Clustering, Reinforcement Learning, Underfitting and Overfitting, Optimization
Hands-On
Implementing a Linear Regression model for predicting house prices from Boston dataset
Implementing a Logistic Regression model for classifying Customers based on an Automobile purchase dataset.
Understanding Neural Networks with TensorFlow.
Learning Objectives:
In this module, you’ll get an introduction to Neural Networks and understand it’s working i.e. how it is trained, what are the various parameters considered for its training and the activation functions that are applied.
Topics:
How Deep Learning Works?
Activation Functions
Illustrate Perceptron
Training a Perceptron
Important Parameters of Perceptron
What is TensorFlow?
TensorFlow code-basics
Graph Visualization
Constants, Placeholders, Variables
Creating a Model
Step by Step – Use-Case Implementation
Hands-On
Building a single perceptron for classification on SONAR dataset
Deep dive into Neural Networks with TensorFlow
Learning Objectives:
In this module, you’ll understand the backpropagation algorithm which is used for training Deep Networks. You will know how Deep Learning uses neural network and backpropagation to solve the problems that Machine Learning cannot.
Topics:
Understand the limitations of a Single Perceptron
Understand Neural Networks in Detail
Illustrate Multi-Layer Perceptron
Backpropagation – Learning Algorithm
Understand Backpropagation – Using Neural Network Example
MLP Digit-Classifier using TensorFlow
TensorBoard
Hands-On
Building a multi-layered perceptron for classification of Hand-written digits
Master Deep Networks
Learning Objectives:
In this module, you’ll get started with the TensorFlow framework. You will understand how it works, its various data types & functionalities. In addition, you will create an image classification model.
Topics:
Why Deep Networks
Why Deep Networks give better accuracy?
Use-Case Implementation on SONAR dataset
Understand How Deep Network Works?
How Backpropagation Works?
Illustrate Forward pass, Backward pass
Different variants of Gradient Descent
Types of Deep Networks
Hands-On
Building a multi-layered perceptron for classification on SONAR dataset
Convolutional Neural Networks (CNN)
Learning Objectives:
In this module, you’ll understand convolutional neural networks and its applications. You will learn the working of CNN, and create a CNN model to solve a problem.
Topics:
Introduction to CNN’s
CNN’s Application
The architecture of a CNN
Convolution and Pooling layers in a CNN
Understanding and Visualizing a CNN
Hands-On
Building a convolutional neural network for image classification. The model should predict the difference between 10 categories of images.
Recurrent Neural Networks (RNN)
Learning Objectives:
In this module, you’ll understand Recurrent Neural Networks and its applications. You will understand the working of RNN, how LSTM are used in RNN, what is Recursive Neural Tensor Network Theory, and finally, you will learn to create an RNN model.
Topics:
Introduction 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
Hands-On
Building a recurrent neural network for SPAM prediction.
Restricted Boltzmann Machine (RBM) and Autoencoders
Learning Objectives:
In this module, you’ll understand RBM & Autoencoders along with their applications. You will understand the working of RBM & Autoencoders, illustrate Collaborative Filtering using RBM and understand what are Deep Belief Networks.
Topics:
Restricted Boltzmann Machine
Applications of RBM
Collaborative Filtering with RBM
Introduction to Autoencoders
Autoencoders applications
Understanding Autoencoders
Hands-On
Building an Autoencoder model for classification of handwritten images extracted from the MNIST Dataset, Keras API
Learning Objectives:
In this module, you’ll understand how to use Keras API for implementing Neural Networks. The goal is to understand various functions and features that Keras provides to make the task of neural network implementation easy.
Topics:
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
Hands-On
Build a model using Keras to do sentiment analysis on twitter data reactions on GOP debate in Ohio, TFLearn API
Learning Objectives:
In this module, you’ll understand how to use TFLearn API for implementing Neural Networks. The goal is to understand various functions and features that TFLearn provides to make the task of neural network implementation easy.
Topics:
Define TFLearn
Composing Models in TFLearn
Sequential Composition
Functional Composition
Predefined Neural Network Layers
What is Batch Normalization
Saving and Loading a model with TFLearn
Customizing the Training Process
Using TensorBoard with TFLearn
Use-Case Implementation with TFLearn
Hands-On
Build a recurrent neural network using TFLearn to do image classification on hand-written digits
In-Class Project
Learning Objectives:
In this module, you should learn how to approach and implement a project end to end. The instructor will share his industry experience and related insights that will help you kickstart your career in this domain. In addition, we will be having a QA and doubt clearing session for you.
Topics:
How to approach a project?
Hands-On project implementation
What Industry expects?
Industry insights for the Machine Learning domain
QA and Doubt Clearing Session
