Deep Learning with Python Table of Contents


Table of Content
 


Module 1

UNDERSTANDING DEEP LEARNING

  • The Course Overview
  • A Brief History of Deep Learning
  • Deep Learning Today
  • Tools, Requirements, and Setup

BUILDING THE BASIC BLOCKS OF MACHINE LEARNING

  • Exploring Supervised Learning
  • Representational Learning and Feature Engineering
  • Linear Regression
  • The Perceptron

DIVING INTO DEEP NEURAL NETWORKS

  • Feedforward Networks
  • Backpropagation
  • Neural Networks from Scratch
  • Overfitting and Regularization

DISCOVERING CONVOLUTIONAL NEURAL NETWORKS (CNNS)

  • Understanding CNNs
  • Implementing a CNN
  • Deep CNNs

USING CNNS TO SOLVE INCREASINGLY COMPLEX TASKS

  • Very Deep CNNs
  • Batch Normalization
  • Fine-Tuning

LEARNING ABOUT DETECTION AND SEGMENTATION

  • Semantic Segmentation
  • Fully Convolutional Networks

EXPLORING RECURRENT NEURAL NETWORKS

  • Recurrent Neural Networks
  • LSTM and Advancements

OBJECT DETECTION USING CNNS

  • Building a CNN to Detect General Images
  • Training and Deploying on a Cluster

MOVING FORWARD WITH DEEP LEARNING AND AI

  • Comparison of DL Frameworks
  • Exciting Areas for Upcoming Research


Module 2

GETTING STARTED WITH DEEP LEARNING

  • The Course Overview
  • Fundamentals of Neural Networks
  • Training Deep Neural Networks
  • Using Forward Propagation, Backprop, and SGD
  • Logistic Regression with a Neural Network Mindset
  • Convolutional Neural Network Handwriting Recognition

DEEP MODELS WITH MXNET AND TENSORFLOW

  • Working with MxNet and Gluon
  • Defining and Training Neural Networks in MxNet/Gluon
  • Working with TensorFlow and Keras
  • Defining and Training Neural Networks in Keras/TensorFlow
  • Comparing the Two Frameworks
  • Mini Project - CIFAR Classification

IMPROVING DEEP NEURAL NETWORKS

  • Weight Initialization for Deep Networks
  • Regularization and Dropout
  • Normalizing and Vanishing/Exploding Gradients
  • Mini Project – SIGNS Dataset

OPTIMIZATION ALGORITHMS

  • Understanding Stochastic Gradient Descent
  • Adaptive Learning Algorithms - RMSProp and Adam
  • Mini Project - Language Modeling

HYPERPARAMETER TUNING

  • Hyperparameters
  • Tuning Hyperparameters - Grid Search
  • Tuning Hyperparameters - Random Search
  • Mini Project -Music Synthesis


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