Deep Learning for Computer Vision

Basic and Advanced Computer Vision

What you will learn

Basic and Advanced Computer Vision

Artificial Neural Network

Keras Tools, Keras API Support

Image Processing, CNN


Computer vision is an area of deep learning dedicated to interpreting and understanding images. It is used to help teach computers to “see” and to use visual information to perform visual tasks

Computer vision models are designed to translate visual data based on features and contextual information identified during training. This enables models to interpret images  and apply those interpretations to predictive or decision making tasks.

Image processing involves modifying or enhancing images to produce a new result. It can include optimizing brightness or contrast, increasing resolution, blurring sensitive information, or cropping. The difference between image processing and computer vision is that the former doesn’t necessarily require the identification of content.

Deep Learning is part of a broader family of machine learning methods based on artificial neural networks.

Deep-learning architectures such as deep neural networks,  recurrent neural networks, convolutional neural networks have been applied to fields including computer vision, speech recognition, natural language processing, machine translation, bioinformatics, drug design, medical image analysis, material inspection and board game programs, where they have produced good results

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Artificial neural networks (ANNs) were inspired by information processing and distributed communication nodes in biological systems. ANNs have various differences from biological brains.

Keras is the most used deep learning framework. Keras follows best practices for reducing cognitive load: it offers APIs, it minimizes the number of user actions required for common use cases, and it provides clear & actionable error messages.

Following topics are covered as part of the course

  • Introduction to Deep Learning
  • Artificial Neural Networks (ANN)
    • Activation functions
    • Loss functions
    • Gradient Descent
    • Optimizer
  • Image Processing
    • Convnets (CNN), hands-on with CNN
  • Gradients and Back Propagation – Mathematics
    • Gradient Descent
    • Mathematics
  • Image Processing  / CV – Advanced
    • Image Data Generator
    • Image Data Generator – Data Augmentation
    • VGG16 – Pretrained network
    • VGG16 – with code improvements
  • Functional API
    • Intro to Functional API
    • Multi Input Multi Output Model
  • Image Segmentation
  • Pooling
    • Max, Average, Global
  • ResNet Model
    • Resnet overview
    • Resnet concept model
    • Resnet demo
  • Xception
    • Depthwise Separable Convolution
    • Xception overview
    • Xception concept model
    • Xception demo
    • Visualize Convnet filters



Deep Learning Introduction
Keras Introduction

Artificial Neural Network (ANN)

Optimizer and Activation Functions
Demo- Activation Function
Loss Functions

Keras – Quick Start

Prerequisite, Environment (Dev)
Getting Started with Keras
Demo with Keras

Functional API

Functional API and Demo
MIMO Functional API with Demo

Computer Vision – CNN, Handling Images

CNN – Convolutional Neural Network
Demo – CNN (Part 1)
Demo – CNN (Part 2)

Mathematics – Gradients, Back Propagation

Gradients, Back Propagation (Part 1)
Gradients, Back Propagation (Part 2)

DL for Computer Vision

Keras Image/Data Processing Tools
Data Augmentation
VGG16, Pretrained network
Demo – VGG16
Improvements with Data Generation – VGG16

Advanced DL for Computer Vision

Image Segmentation, Demo
ResNet Overview
Pooling, ResNet Demo
Depthwise Separable Convolution
Xception Concept Overview
Xception Model Demo
Keras Xception support
Visualize Convnet filters for Xception
Filters Interpretation
Model Fitment – Design Issues

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