Artificial Intelligence with Python – Deep Neural Networks [Video]

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Artificial Intelligence with Python – Deep Neural Networks [Video]

Prateek Joshi

Learn different Artificial Intelligence learning techniques with neural networks

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Video Details

ISBN 139781789132670
Course Length1 hour and 19 minutes

Video Description

The course is an introduction to the basics of deep learning methods. We will start with object detection and tracking, in which we will track faces, objects and eyes. We will then build a neural network and an OCR. We will then learn how to build learning agents that can learn from interacting with the environment. We will use Deep Learning with Convolutional Neural Networks, and use TensorFlow to build neural networks. We will then build an image classifier using convolutional neural networks.

Style and Approach

The video course is structured in such a way that the explanation of a concept is followed by a relevant example. Also, algorithms are explained and their respective code and training dataset is provided.

Table of Contents

Object Detection and Tracking
The Course Overview
Installing OpenCV
Frame Differencing
Tracking Objects Using Colorspaces
Object Tracking Using Background Subtraction
Building an Object Tracker Using the CAMShift Algorithm
Optical Flow Based Tracking
Face Detection and Tracking
Artificial Neural Networks
Introduction to Artificial Neural Networks
Building a Perceptron Based Classifier
Constructing Single and Multilayer Neural Networks
Building a Vector Quantizer
Analyzing Sequential Data Using Recurrent Neural Networks
Visualizing Characters in an Optical Character Recognition Database
Building an Optical Character Recognition Engine
Reinforcement Learning
What Is Reinforcement Learning?
Creating an Environment
Building a Learning Agent
Deep Learning with Convolutional Neural Networks
What are Convolutional Neural Networks?
Building a Perceptron-Based Linear Regressor
Building an Image Classifier Using a Single Layer Neural Network
Building an Image Classifier Using a Convolutional Neural Network

What You Will Learn

  • Build applications based on deep learning algorithms 
  • Detect and track objects using different algorithms
  • Learn how reinforcement learning works

Authors

Table of Contents

Object Detection and Tracking
The Course Overview
Installing OpenCV
Frame Differencing
Tracking Objects Using Colorspaces
Object Tracking Using Background Subtraction
Building an Object Tracker Using the CAMShift Algorithm
Optical Flow Based Tracking
Face Detection and Tracking
Artificial Neural Networks
Introduction to Artificial Neural Networks
Building a Perceptron Based Classifier
Constructing Single and Multilayer Neural Networks
Building a Vector Quantizer
Analyzing Sequential Data Using Recurrent Neural Networks
Visualizing Characters in an Optical Character Recognition Database
Building an Optical Character Recognition Engine
Reinforcement Learning
What Is Reinforcement Learning?
Creating an Environment
Building a Learning Agent
Deep Learning with Convolutional Neural Networks
What are Convolutional Neural Networks?
Building a Perceptron-Based Linear Regressor
Building an Image Classifier Using a Single Layer Neural Network
Building an Image Classifier Using a Convolutional Neural Network

Video Details

ISBN 139781789132670
Course Length1 hour and 19 minutes
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