Overview

This AI and Deep learning course offers practical and task-oriented training using TensorFlow and Keras on Python platform. Recent developments in Deep learning have been nothing short of a revolution and have enabled some of the most exciting and powerful applications in the field of Artificial Intelligence. 

This is a specialization course which will help you to get a break into AI and Deep Learning domain, with one of the most sought-after skills. You will learn the foundations of Deep Learning, understand how to build neural networks, and learn how to build successful Deep Learning based AI projects using Tensor Flow and Keras. You will work on case studies on computer vision, text data processing, Image processing, Speech analytics - Speech to text / Voice tonality, IOT. After successful completion of this course you will master not only the theory, but also learn how it is applied in the industry.

Considering the practical application based curriculum, this is the best Deep Learning training course for Data Science professionals who are looking for an industry relevant certification from an eminent Deep Learning Institute.

Duration

80 hours

Intended Audience

Analytics professionals or aspirants with prior working knowledge of Data Science with Python, who are looking Deep Learning certification to up-skill with practical application of AI Deep Learning with TensorFlow and Keras.

Course outlines

1.      Introduction to Deep Learning

• What are the Limitations of Machine Learning?
• What is Deep Learning?
• Advantage of Deep Learning over Machine learning
• Reasons to go for Deep Learning
• Real-Life use cases of Deep Learning

2.      Introduction to Artificial Intelligence (Ai)

• History of AI
• Modern era of AI
• How is this era of AI different?
• Transformative Changes
• Role of Machine learning & Deep Learning in AI
• Hardware for AI (CPU vs. GPU vs. TPU)
• Software Frameworks for AI
• Deep Learning Frameworks for AI
• Key Industry applications of AI

3.      Deep Learning in Python

• Overview of important python packages for Deep Learning

4.      Overview of 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

5.      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

6.      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)

7.      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

8.      Recurrent Neural Networks (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

9.      Restricted Boltzmann Machine (Rbm)

• What is Restricted Boltzmann Machine?
• Applications of RBM
• Collaborative Filtering with RBM
• Introduction to Autoencoders & Applications
• Understanding Autoencoders

10.  Deep Learning with Tflearn

• 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

11.  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

12.  Key Applications of Deep Learning in AI

• Computer Vision
• Text Data Processing
• Image processing
• Audio & video Analytics
• Internet of things (IOT)

13.  Final Projects- Consolidate The Learning & Implement Them in Python

• Computer Vision
• Text Data Processing
• Image processing - PNG, PDF, JPEG, JPG etc.
• Speech analytics - Speech to text / Voice tonality
• Internet of Things - IOT