Deep Learning Training in Belgaum

MODULE 1

• Introduction to Tensor Flow
• Computational Graph
• Key highlights
• Creating a Graph
• Regression example
• Gradient Descent
• TensorBoard
• Modularity
• Sharing Variables
• Keras Perceptrons
• What is a Perceptron?
• XOR Gate

MODULE 2

• Activation Functions
• Sigmoid
• ReLU
• Hyperbolic Fns, Softmax Artificial Neural Networks
• Introduction
• Perceptron Training Rule
• Gradient Descent Rule

MODULE 3

• Gradient Descent and Backpropagation
• Gradient Descent
• Stochastic Gradient Descent
• Backpropagation
• Some problems in ANN Optimization and Regularization
• Overfitting and Capacity
• Cross-Validation
• Feature Selection
• Regularization
• Hyperparameters

MODULE 4

• Introduction to Convolutional Neural Networks
• Introduction to CNNs
• Kernel filter
• Principles behind CNNs
• Multiple Filters
• CNN applications Introduction to Recurrent Neural Networks
• Introduction to RNNs
• Unfolded RNNs
• Seq2Seq RNNs
• LSTM
• RNN applications

MODULE 5

• Deep learning applications
• Image Processing
• Natural Language Processing
• Speech Recognition
• Video Analytics
Download Syllabus - Deep Learning
This syllabus is not final and can be customized as per needs/updates