AI DEEP LEARNING

$ 97.89 $ 48.94

Deep learning is an artificial intelligence function that imitates the workings of the human brain in processing data and creating patterns for use in decision making. Deep learning is a subset of machine learning in artificial intelligence (AI) that has networks capable of learning unsupervised from data that is unstructured or unlabelled. Also known as deep neural learning or deep neural network.

Category:
Description

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

https://www.selflearningvideos.com

Contact Us