Description
AI DEEP LEARNING TOPICS LIST
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, Optimization
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
Convolutional 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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