Deep Learning Training Program

What is Deep Learning

Curse of Dimensionality

Machine Learning vs. Deep Learning

Use cases of Deep Learning

Human Brain vs. Neural Network

What is Perceptron

Learning Rate

Epoch

Batch Size

Activation Function

Single Layer Perceptron

What is NN

Types of NN

Creation of simple neural network using tensorflow

Image Classification Example

What is Convolution

Convolutional Layer Network

Convolutional Layer

Filtering

ReLU Layer

Pooling

Data Flattening

Fully Connected Layer

Predicting a cat or a dog

Saving and Loading a Model

Face Detection using OpenCV

Introduction to Vision

Importance of Image Processing

Image Processing Challenges – Interclass Variation, ViewPoint Variation, Illumination, Background Clutter, Occlusion & Number of Large Categories

Introduction to Image – Image Transformation, Image Processing Operations & Simple Point Operations

Noise Reduction – Moving Average & 2D Moving Average

Image Filtering – Linear & Gaussian Filtering

Disadvantage of Correlation Filter

Introduction to Convolution

Boundary Effects – Zero, Wrap, Clamp & Mirror

Image Sharpening

Template Matching

Edge Detection – Image filtering, Origin of Edges, Edges in images as Functions, Sobel Edge Detector

Effect of Noise

Laplacian Filter

Smoothing with Gaussian

LOG Filter – Blob Detection

Noise – Reduction using Salt & Pepper Noise using Gaussian Filter

Nonlinear Filters

Bilateral Filters

Canny Edge Detector - Non Maximum Suppression, Hysteresis Thresholding

Image Sampling & Interpolation – Image Sub Sampling, Image Aliasing, Nyquist Limit, Wagon Wheel Effect, Down Sampling with Gaussian Filter, Image Pyramid, Image Up Sampling

Image Interpolation – Nearest Neighbour Interpolation, Linear Interpolation, Bilinear Interpolation & Cubic Interpolation

Introduction to the dnn module

• Deep Learning Deployment Toolkit

• Use of DLDT with OpenCV4.0

OpenVINO Toolkit

• Introduction

• Model Optimization of pre-trained models

• Inference Engine and Deployment process

Regional-CNN

Selective Search Algorithm

Bounding Box Regression

SVM in RCNN

Pre-trained Model

Model Accuracy

Model Inference Time

Model Size Comparison

Transfer Learning

Object Detection – Evaluation

mAP

IoU

RCNN – Speed Bottleneck

Fast R-CNN

RoI Pooling

Fast R-CNN – Speed Bottleneck

Faster R-CNN

Feature Pyramid Network (FPN)

Regional Proposal Network (RPN)

Mask R-CNN

Issues with Feed Forward Network

Recurrent Neural Network (RNN)

Architecture of RNN

Calculation in RNN

Backpropagation and Loss calculation

Applications of RNN

Vanishing Gradient

Exploding Gradient

What is GRU

Components of GRU

Update gate

Reset gate

Current memory content

Final memory at current time step

What is LSTM

Structure of LSTM

Forget Gate

Input Gate

Output Gate

LSTM architecture

Types of Sequence-Based Model

Sequence Prediction

Sequence Classification

Sequence Generation

Types of LSTM

Vanilla LSTM

Stacked LSTM

CNN LSTM

Bidirectional LSTM

How to increase the efficiency of the model

Backpropagation through time

Workflow of BPTT

What is Boltzmann Machine (BM)

Understanding Autoencoders

Architecture of Autoencoders

Brief on types of Autoencoders

Applications of Autoencoders

What is BERT

Brief on types of BERT

Applications of BERT