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DEEP LEARNING(AI)

 10,000.00  4,999.00

Deep learning is a machine learning technique that teaches computers to do what comes naturally to humans-learn by example.In deep learning, a computer model learns to perform classification tasks directly from images, text, or sound.

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Description

Description

ARTIFICIAL INTELLIGENCE COURSE CONTENT

Introduction to Deep Learning
Deep Learning: A revolution in Artificial Intelligence
Limitations of Machine Learning
What is Deep Learning?
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, Under fitting and Over fitting, Optimisation
Understanding Neural Networks with Tensor Flow
How Deep Learning Works?
Activation Functions
Illustrate Perception
Training a Perception
Important Parameters of Perception
What is Tensor Flow?
Tensor Flow code-basics
Graph Visualisation
Constants, Placeholders, Variables
Creating a Model
Step by Step – Use-Case Implementation
Deep dive into Neural Networks with Tensor Flow
Understand limitations of a Single Perception
Understand Neural Networks in Detail
Illustrate Multi-Layer Perception
Back propagation – Learning Algorithm
Understand Back propagation – Using Neural Network Example
MLP Digit-Classifier using Tensor Flow
Tensor Board
Master Deep Networks
Why Deep Networks
Why Deep Networks give better accuracy?
Use-Case Implementation on SONAR data set
Understand How Deep Network Works?
How Back propagation Works?
Illustrate Forward pass, Backward pass
Different variants of Gradient Descent
Types of Deep Networks
Convolution Neural Networks (CNN)
Introduction to CNNs
CNNs Application
Architecture of a CNN
Convolution and Pooling layers in a CNN
Understanding and Visualising a CNN
Recurrent Neural Networks (RNN)
Introduction to RNN Model
Application use cases of RNN
Modelling sequences
Training RNNs with Back propagation
Long Short-Term memory (LSTM)
Recursive Neural Tensor Network Theory
Recurrent Neural Network Model
Restricted Boltzmann Machine (RBM) and Auto encoders
Restricted Boltzmann Machine
Applications of RBM
Collaborative Filtering with RBM
Introduction to Auto encoders
Auto encoders applications
Understanding Auto encoders
Keras API
Define Keras
How to compose Models in Keras
Sequential Composition
Functional Composition
Predefined Neural Network Layers
What is Batch Normalisation
Saving and Loading a model with Keras
Customising the Training Process
Using Tensor Board with Keras
Use-Case Implementation with Keras
TFLearn API
Define TFLearn
Composing Models in TFLearn
Sequential Composition
Functional Composition
Predefined Neural Network Layers
What is Batch Normalisation
Saving and Loading a model with TFLearn
Customising the Training Process
Using Tensor Board with TFLearn
Use-Case Implementation with TFLearn

 

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