AI101 - Comprehensive Deep Learning Tutorial
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img added finetuning preprocessing steps Dec 11, 2018
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1. Introduction to Numpy.ipynb Added Placeholder chapter Nov 30, 2018
2. Introduction to Tensorflow.ipynb added new chapters Dec 1, 2018
2.10 Tensorflow Datasets.ipynb Finetuning and Data Wrangling Dec 14, 2018
2.11. Reading Checkpoints in Tensorflow.ipynb added finetuning preprocessing steps Dec 11, 2018
2.3-4. Placeholders in Tensorflow.ipynb added new chapters Dec 1, 2018
2.5. Variables in Tensorflow.ipynb added new chapters Dec 1, 2018
2.6.1.Saving Session in Tensorflow.ipynb added new chapters Dec 1, 2018
2.6.2 Restoring Session in Tensorflow.ipynb added new chapters Dec 1, 2018
2.8.1. NeuralNetwork in Tensorflow.ipynb added datasets Dec 7, 2018
2.8.2 NeuralNetwork in Tensorflow via Keras.ipynb changes in class Dec 6, 2018
2.8.3. NeuralNetwork in Keras.ipynb NN using Tensorflow and Keras Dec 1, 2018
2.9 Realtime Metric in Tensorflow.ipynb changes in class Dec 6, 2018
5.1. Finetuning for CatvsDogs.ipynb added finetuning preprocessing steps Dec 11, 2018
5.1. Understanding Images and Data Preperation.ipynb Finetuning and Data Wrangling Dec 14, 2018
5.2. OpenCV Selected Topics.ipynb Finetuning and Data Wrangling Dec 14, 2018
5.3.1. Finetuning for CatsvsDogs in Keras.ipynb Finetuning and Data Wrangling Dec 14, 2018
5.4.1. Manipulating Model.ipynb Finetuning and Data Wrangling Dec 14, 2018
README.md Header Image URL Nov 22, 2018
Untitled.ipynb Adding Tensorflow Chapter Nov 30, 2018
finetuning_keras.py Finetuning and Data Wrangling Dec 14, 2018
finetuning_tensorflow.py Finetuning and Data Wrangling Dec 14, 2018

README.md

Deep Learning Tutorial

Course Content

Essential Programming

  • Introduction to Deep Learning
  • Introduction to Numpy
  • Introduction to Tensorflow and Keras

Essential basics of Linear Algebra

  • Solution of Equations, row and column Interpretation

  • Vector Space Properties

  • Partial Derivative of Polynomial and Two conditions for Local Minima

  • Physical Interpretation of gradient (Direction of Maximum Change)

  • Matrix Vector Multiplication

  • EVD and interpretation of Eighen Vectors

  • Linear Independence and Rank of Matrix

  • Orthonormal Matrices, Projection Matrices, Vandemonde Matrix, Markov Matrix, Symmetric, Block Diagonal

Selected topics of Machine Learning

  • Intuition behind Linear Regression, classification

  • Grid Search

  • Gradient Descent

  • Training Pipeline

  • Metrics - ROC Curve, Precision Recall Curve

  • Calculating Entropy

Basics of Neural Network

  • Evolution of Perceptrons, Hebbs Principle, Cat Experiment

  • Single layer NN

  • Tensorflow Code

  • Multilayer NN

  • Back propagation, Dynamic Programming

  • Mathematical Take on NN

  • Function Approximator

  • Link with Linear Regression

  • Dropout and Activation

  • Optimizers and Loss Functions

Introduction to Convolutional Neural Network

  • 1D and 2D Convolution
  • Why CNN for Images and speech?
  • Convolution Layer
  • Coding Convolution Layer
  • Learning Sharpening using single convolution Layer in Tensor-Flow

Different Layers in CNN pipeline

  • Convolution
  • Pooling
  • Activation
  • Dropout
  • Batch Normalization
  • Object Classification
  • Creating Batch in Tensorflow and Normalize
  • Training MNIST and CIFAR datasets
  • Understanding a pre-trained Inception Architecture
  • Input Augmentation Techniques for Images

Transfer Learning

  • Finetuning last layers of CNN Model
  • Selecting appropriate Loss
  • Adding a new class in the last Layer
  • Making a model Fully Convolutional for Deployment
  • Finetune Imagenet for Cats vs Dog Classification.

Object Detection and Localization

  • Different types of problem in Objects
  • Difficulties in Object Detection and Localization
  • Fast RCNN
  • Faster RCNN
  • YOLO v1-v3
  • SSD
  • MobileNet

Autoencoders

  • Image Compression - Simple Autoencoder
  • Denoising Autoencoder
  • Variational Autoencoder and Reparematrization Trick
  • Robust Word Embedding using Variational Autoencoder

Time Series Modelling

  • Evolution of Recurrent Structures
  • LSTM, RNN, GRU, Bi-RNN, Time-Dense
  • Learning a Sine Wave using RNN in Tensorflow
  • Creating Autocomplete for Harry Potter in Tensorflow

GANs

  • Generative vs Discrimative Models

  • Theory of GAN

  • Simple Distribution Generator in Tensorflow using MCMC (Markov Chain Monte Carlo)

  • DCGAN,WGANs for Images

  • InfoGANs, CycleGANs and Progressive GANs

  • Creating a GAN for generating Manga Art

Model Free Approaches in Reinforcement Learning

  • Model Free Prediction
  • Monte Carlo Prediction and TD Learning
  • Model Free Control with REINFORCE and SARSA Learning
  • Assignment Implementation of REINFORCE and SARSA Learning in Gridworld
  • Off policy vs On Policy Learning
  • Importance Sampling for Off Policy Learning
  • Q Learning

Behavioral Cloning and Deep Q Learning

  • Understanding Deep Learning as Function Approximator
  • Theory of Behavioral Cloning and Deep Q Learning
  • Revisiting Point Collector Example in Unity and
  • **Assignment : **Training Cartpole Example via Deep Q Learning

Deep Learning in Action

  • Face Detection using Yolo-v3
  • Building Autocomplete Feature using RNNs
  • Real-time Depth Prediction and Pose Estimation
  • How is Deep Learning used in Autonomous Driver Assistant systems
  • Tips and Tricks for scaling and easy Deployment of Deep Learning Models