Description

Deep Learning with TensorFlow

Deep Learning is a subfield of machine learning concerned with algorithms inspired by the structure and function of the brain called artificial neural networks. It is intersection of statistics, artificial intelligence, and data to build accurate models. TensorFlow is one of the newest and most comprehensive libraries for implementing deep learning. With deep learning going mainstream, making sense of data and getting accurate results using deep networks is possible.

How it works

A deep learning model is designed to continually analyse data with a logic structure like how a human would draw conclusions. To achieve this, deep learning uses a layered structure of algorithms called an artificial neural network (ANN).

What you will learn from this course

This course will offer you an opportunity to explore various complex algorithms for deep learning. You will also learn how to train model to derive new features to make sense of deeper layers of data. Using TensorFlow, you will learn how to train model in supervise and unsupervised category.

Introduction to Deep learning

AI and Deep learning

Advantage of DL

Deep Learning Primitives

Deep Learning Architecture

The Neural viewpoint

The Representation Viewpoint

TensorFlow Fundamentals

Introduction of Tensors

Installation of Tensors

Scalars, Vectors, and Matrices

Matrix Mathematics

Initializing Constant Tensors

Basic Computation using TensorFlow

Sampling Random Tensors

Tensor Addition and Scaling

Matrix Operation

Tensor Shape Manipulation

Tensor Types

TensorFlow Graphs

TensorFlow Sessions

TensorFlow Variable

Logistic Regression Model Building and Training

Introduction to Neural Network

Basic Neural Network

The Neurons

Single Hidden Layer Model

Multiple Hidden Layer Model

Input, Output, Hidden Layers

Details of Activation Functions: Sigmoid Function Hyperbolic Tangent

Function, SoftMax

Selection of Right Activation Functions

Network learning technique

Weight initialization

Forward Propagation

Backpropagation

Optimization Algorithms

Regularization

Linear and Logistic Regression with TensorFlow

Overview of Linear and Logistic Regression

Loss Functions

Gradient Descent

Automatic Differentiation Systems

Learning with TensorFlow

Training Linear and Logistic Regression model

Evaluating Model Accuracy

Convolutional Neural Networks

Visual Cortex Architecture

Convolutional Layer

Filters

Stacking Multiple Feature Maps

TensorFlow Implementation

Pooling/Subsampling

Fully Connected Layer

MNIST digit classification example

Recurrent Neural Networks

Recurrent Neurons

Memory cells

Input and Output Sequences

Basic RNNs in TensorFlow

Static Unrolling through Time

Dynamic Unrolling through Time

Handling Variable Length Input/Output Sequence

Training RNNs

Creative RNNs

Deep RNNs

Distributing a Deep RNN Across Multiple GPUs

The Difficulty of Training over many Time Steps

Reinforcement Learning

Policy Search

Introduction to OpenAI Gym

Neural Network Policies

The Credit Assignment Problem

Policy Gradients

Markov Decision Process

Temporal Difference Learning and Q-Learning

Approximate Q-Learning and Deep Q-Learning

Prerequisites :

Basic understanding of linear algebra , calculus and probability are must for really understanding deep learning . It is expected that one has some knowledge or experience in basic Python programming skills with the capability to work effectively with data structures . Understanding how to frame a machine learning problem, including how data is represented will be an added advantage.

Who can attend

Anyone who has coding experience with an engineering background or relevant knowledge in mathematics and computer science can take this session to get understanding of Deep learning.