Deep learning is a subfield of artificial intelligence technology, where various neural networks will process the information and give accurate predictions. In deep learning we can implement various frameworks like keras , tensor flow , pytorch in real time applications.

Total Duration: 8 Hrs | Modules: 5 | Assignments: 5 | Projects – 10

Module 1: Introduction to deep learning

Introduction to deep learning
What is deep learning
History of deep learning
Importance of deep learning
Define neural networks and list various types of neural networks .
Various industrial applications of deep learning

Module 2: artificial neural networks

Introduction to artificial neural networks
Architecture of artificial neural networks
Pros and cons of ANN network
Implementation of ANN for churn model prediction.

Module 3: deep learning frameworks

Key Learning Objectives: This Module will introduce the participants the Overview of various deep learning frameworks which includes tensorflow ,keras, pytorch.

Assignment 1: apply various initializers for a keras model.

Lesson 1: keras and tensorflow

We need to learn about various initializers, constraints, regularizations, activation functions to create a deep learning model with the help of keras framework.

Assignment 2: list various constraints and regularizers.

Lesson 2: various constraints and regularizers implementations in keras deep learning model.

Implement constraints and regularizers

Assignment 3: what are various constraints how to implement them ?

Lesson 3: activation functions

Learn how to implement activation functions in keras framework.

Assignment 4 - describe how to use the pytorch framework .

Lesson 4: pytorch

Implementation of pytorch library file with deep learning models .

Project - a sample deep learning model creation using pytorch.

Module 4: deep learning algorithms

Key Learning Objectives:

List all various deep learning algorithms .

Lesson 5: various deep learning algorithms

Importance of various deep learning algorithms along with its implementations.

Lesson 6: applications

Performance analysis for various input data using different deep learning algorithms.

Assignment 5 – implement lstm model for bitcoin price prediction

Project - image recognition using CNN.

Lesson 7:

Define RNN and give its application.

Assignment 6 – house hold estimation using RNN.

Lesson 8: tensorflow

Face recognition using tensorflow.

Lesson 9: human pose estimation and tracking using deep learning.

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