English | MP4 | AVC 1920×1080 | AAC 44KHz 2ch | 1h 19m | 367 MB
Learn different Artificial Intelligence learning techniques with neural networks
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.
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.
What You Will Learn
- Build applications based on deep learning algorithms
- Detect and track objects using different algorithms
- Learn how reinforcement learning works
Table of Contents
01 The Course Overview
02 Installing OpenCV
03 Frame Differencing
04 Tracking Objects Using Colorspaces
05 Object Tracking Using Background Subtraction
06 Building an Object Tracker Using the CAMShift Algorithm
07 Optical Flow Based Tracking
08 Face Detection and Tracking
09 Introduction to Artificial Neural Networks
10 Building a Perceptron Based Classifier
11 Constructing Single and Multilayer Neural Networks
12 Building a Vector Quantizer
13 Analyzing Sequential Data Using Recurrent Neural Networks
14 Visualizing Characters in an Optical Character Recognition Database
15 Building an Optical Character Recognition Engine
16 What Is Reinforcement Learning
17 Creating an Environment
18 Building a Learning Agent
19 What are Convolutional Neural Networks
20 Building a Perceptron-Based Linear Regressor
21 Building an Image Classifier Using a Single Layer Neural Network
22 Building an Image Classifier Using a Convolutional Neural Network
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