Video Description
Learn different Artificial Intelligence learning techniques with neural networks
About This Video
- Learn the fundamentals of Deep Learning and use them to build intelligent systems
- Solve real-world problems such as face detection, handwriting recognition, and more
- Work with reinforcement learning, convolutional networks, and other deep learning concepts
In Detail
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.
Table of Contents
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Chapter 1 : Object Detection and Tracking
- The Course Overview 00:02:08
- Installing OpenCV 00:01:35
- Frame Differencing 00:02:43
- Tracking Objects Using Colorspaces 00:03:26
- Object Tracking Using Background Subtraction 00:03:25
- Building an Object Tracker Using the CAMShift Algorithm 00:05:32
- Optical Flow Based Tracking 00:04:42
- Face Detection and Tracking 00:04:45
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Chapter 2 : Artificial Neural Networks
- Introduction to Artificial Neural Networks 00:03:30
- Building a Perceptron Based Classifier 00:02:40
- Constructing Single and Multilayer Neural Networks 00:05:08
- Building a Vector Quantizer 00:02:32
- Analyzing Sequential Data Using Recurrent Neural Networks 00:02:25
- Visualizing Characters in an Optical Character Recognition Database 00:02:36
- Building an Optical Character Recognition Engine 00:03:23
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Chapter 3 : Reinforcement Learning
- What Is Reinforcement Learning? 00:04:55
- Creating an Environment 00:02:47
- Building a Learning Agent 00:02:42
- Chapter 4 : Deep Learning with Convolutional Neural Networks