Deep Learning for Computer Vision 2022 by Shrirang Korde

Deep Learning for Computer Vision (2022) by Shrirang Korde

Published 07/2022
MP4 | Video: h264, 1280×720 | Audio: AAC, 44.1 KHz, 2 Ch
Genre: eLearning | Language: English + srt | Duration: 32 lectures (10h 12m) | Size: 4.7 GB

Basic and Advanced Computer Vision

What you’ll learn
Basic and Advanced Computer Vision
Artificial Neural Network
Keras Tools, Keras API Support
Image Processing, CNN

Requirements
Python

Description
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

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

Who this course is for
Python programmers, Machine Learning aspirants, Deep Learning Aspirants


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