Deep Learning with TensorFlowLive Class

Overview

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• 1 – Deep Learning
  • Introduction
  • Input Layer
  • Hidden Layer
  • Output Layer
  • Activation Layer
  • Hands-On Creation of Layers in TF
• 2 – Perceptron
  • Introduction
  • Input
  • Linear Operation
  • Activation
  • Output of Perceptron
  • Limitation of Perceptron
  • Hands-On Creation of Layers in TF
• 3 – Perceptron Training
  • How to Compute Output?
  • How is Loss determined?
  • How Parameters Updated?
  • Gradient Descent Algorithm
  • Backpropagation
  • Hands-On Training
• 4 – Training Hyperparameters
  • Batch Size
  • Learning Rate
  • Epochs
  • Hands-On Implementation
• 5 – Multi-Layer Perceptron
  • Structure of Multi-Layer Perceptron
  • How Output is determined
  • Loss
  • Backpropagation
  • Gradient Descent
  • Hands-On
• 6 – TensorFlow
  • Introduction
  • Recipe of Neural Networks for Various tasks
  • Syntax for Neural Network Building
  • Hands-On with Overall Architecture
• 7 – TensorFlow Project 1 –Build Multi-Class Classification
  • Loading the data
  • Normalization of Data
  • Split the dataset
  • Model Compilation
  • Model Training and Evaluation
• 8 – Convolutional Neural Networks
  • Properties of a Digital Image
  • CNN Layer
  • Filters/Kernels
  • Pooling Layers
  • Neural Network Architecture
  • Hands-On CNN
• 9 – TensorFlow Project 2 – Building an Image Classifier of Cats & Dogs Using CNN
  • Creation of Neural Network from Scratch
  • Training
  • Evaluation of Model
  • Transfer Learning with VGG16 Model
  • Model Performance & Tuning
  • Hyperparameter Tuning
• 10 – Learning Summary
  • Quick Overview of Learnings
  • TensorFlow Hands-On Recap