English | MP4 | AVC 1920×1080 | AAC 44KHz 2ch | 1h 36m | 935 MB
A practical approach to deep learning and deep reinforcement learning for building real-world applications using TensorFlow
TensorFlow is one of the most commonly used frameworks for Deep Learning and AI. This course will be your guide to understand and learn the concepts of Artificial intelligence by applying them in a real-world project with TensorFlow.
This course will show you how to combine the power of Artificial Intelligence and TensorFlow to develop some exciting applications for the real world. This course will take you through all the relevant AI domains, tools, and algorithms required to build optimal solutions and will show you how to implement them hands-on. You will then be taken through techniques such as reinforcement learning, heuristic searches, neural networks, Computer Vision, OpenAI Gym, and more in different stages of your application. This course will show you how to implement AI practically using TensorFlow models and how it eases the way you interact with the technology.
You will learn how TensorFlow can be used to analyze a variety of data sets and will learn to optimize various AI algorithms. By the end of the course, you will have learned to build intelligent apps by leveraging the full potential of Artificial Intelligence with TensorFlow.
The approach throughout the course will be one of breaking down the task of building AI systems with TensorFlow into its component parts, using practical examples and step-by-step instructions along the way. We’ll also focus on optimizing the solutions we build. Throughout the course, we’ll explain different algorithms and implement them along the way.
What You Will Learn
- Explore the current state of Machine Learning and Artificial Intelligence.
- Develop the understanding to build AI systems using different machine learning models.
- Optimize machine learning models for better performance and accuracy.
- Understand different deep learning models for computer vision
- Explore generative models and how they generate information from random noise.
- Code the most trending AI algorithms that outperform humans in video games.
Table of Contents
01 The Course Overview
02 The Current State of Artificial Intelligence
03 Setting Up the Environment for Deep Learning
04 Deep Learning in Fashion
05 An Intro to Transfer Learning – Skin Cancer Classification
06 Fundamentals of Object Localization and Detection
07 YOLO(You only look once) – Single Shot Object Detection
08 Unravelling Adversarial Learning and Generative Adversarial Nets
09 Generating Handwritten Digits Using GANs
10 Generating New Pokemons Using a DCGAN
11 Super-Resolution Generative Adversarial Networks
12 Setting Up OpenAI Gym
13 Introduction to Reinforcement Learning
14 Simple Q-Learning – Building Our First Video Game Bot
15 Deep Q-Learning – Building a Game Bot that Plays the Classic Atari Games
16 Deep Reinforcement Learning with Policy Gradient – AI that Plays Pong
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