by Sandipan Dey | Released March 2019 | ISBN: 9781838557829

Using AI with Keras for building complex deep learning networks with fewer lines of coding in PythonAbout This VideoHelps to understand the core concepts behind AI and how to apply it to day-to-day problems.Choosing a data setDistribution of the data set between training, testing and validation setHow to define your own customized modelHow to deploy your code on GPU (if your computer has it)Learn how to use the Python library Keras using Tensorflow backendHyperparameters Optimization in your network architecture which can be tuned for better performanceIn DetailAI will help you solve key challenges in the future in several domains. It is an exciting time to be doing AI with world making its shift towards Industry 2.0 with automation in focus.This course will help you learn by doing an industry relevant problem in image processing domain, develop and understand automation and AI techniques. You will learn how to harness the power of algorithms by creating apps which intelligently interact with the world around you, addressing common challenges faced in AI ecosystem.By the end of the course, you will be able to build real-world artificial intelligence applications using Keras and Python.All the code and supporting files for this course are available on GitHub at: https://github.com/PacktPublishing/Hands-on-Artificial-Intelligence-with-Keras-and-PythonDownloading the example code for this course: You can download the example code files for all Packt video courses you have purchased from your account at http://www.PacktPub.com. If you purchased this course elsewhere, you can visit http://www.PacktPub.com/support and register to have the files e-mailed directly to you. Show and hide more

1. Chapter 1 : Introduction
   • The Course Overview 00:02:58
   • AI, Machine Learning, Deep Learning Overview 00:03:56
   • Keras Overview 00:03:43
   • Development Environment Setup 00:04:40
2. Chapter 2 : CNN, Deep Driving Simulator
   • Deep Learning(DL) 00:08:46
   • Deep Neural Nets : Forward and Backward Propagation 00:13:56
   • CNN Intuition 00:12:00
   • Autonomous Driving Simulator 00:05:30
   • Running in Autonomous Mode 00:16:59
3. Chapter 3 : Deep Reinforcement Learning with Keras for Game AI
   • Reinforcement Learning and Q-Learning for Game AI 00:07:17
   • Setting up Game Environment – OpenAI Gym with Keras 00:06:55
   • Developing DQN-Based Dino Run Game 00:09:21
   • Developing Model for Dino Run Game to Run in AI Mode 00:05:18
4. Chapter 4 : Image Style Transfer Using Neural Network Style Transfer
   • Neural Network Style Transfer 00:06:05
   • Neural Network Style Transfer – CNN Code Using Keras 00:10:20
   • Generative Adversarial Network 00:09:18
   • Style Transfer – CGAN Code 00:08:58
5. Chapter 5 : Conclusion, What’s Next?
   • CNN Problem and Trends 00:06:24
   • RL Problems and Trends 00:04:41
   • GAN Problems and Trends 00:04:34
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