Deep Learning Certification

01 Learning Path

Introduction to Deep Learning

• Define Deep Learning
• Neural Networks
• Deep Learning Applications

How to Train ANNs

• Introduction
• Perceptron Learning Rule
• Gradient Descent Rule
• Minimize Cost Function
• Tuning Learning Rate
• Stochastic vs Batch Gradient Descent

Perceptron

• What is a Perceptron
• Logic Gates with Perceptron
• Activation Functions
• Sigmoid
• ReLU
• Softmax
• Hyperbolic Functions

02 Learning Path

Multi-Layer ANN

• Intro to MLP
• Forward Propagation
• Minimize Cost Function

Training Deep Neural Nets

• Vanishing/Exploding Gradients
• Xavier Initialization
• Leaky ReLUs and ELUs
• Batch Normalization
• Transfer Learning
• Naive Bayes
• Removing a Container
• Unsupervised Pre-Training

Introduction to TensorFlow

• Intro to TensorFlow
• Computational Graph
• Key Highlights
• Creating a Graph
• Regression Example
• Gradient Descent
• Saving and Restoring Models
• Tf.layers API
• Keras-Based networks
• TensorBoard

03 Learning Path

Convolutional Neural Networks

• Intro to CNNs
• Convolution Operation
• Kernel Filter
• Feature Maps
• Pooling
• CNN Architecture
• Implement CNN in Tensor Flow

Recurrent Neural Networks

• Intro to RNNs
• Unfolded RNNs
• Basic RNN Cell
• Dynamic RNN
• Training RNNs
• Time-series predictions
• LSTM
• Word Embedding’s
• Seq2Seq Models
• Implement RNN in TensorFlow