English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 30h 55m | 2.29 GB
Deep reinforcement learning is one of AI’s hottest fields. Researchers, engineers, and investors are excited by its world-changing potential. In this advanced program, you’ll master techniques like Deep Q-Learning and Actor-Critic Methods, and connect with experts from NVIDIA and Unity as you build a portfolio of your own reinforcement learning projects.
Table of Contents
1 Welcome to Deep Reinforcement Learning
2 Get Help from Peers and Mentors
3 Get Help with Your Account
4 Learning Plan
5 Introduction to RL
6 The RL Framework The Problem
7 The RL Framework The Solution
8 Monte Carlo Methods
9 Temporal-Difference Methods
10 Solve OpenAI Gym’s Taxi-v2 Task
11 RL in Continuous Spaces
12 What’s Next
13 Study Plan
14 Deep Q-Networks
15 Deep RL for Robotics
16 Navigation
17 Opportunities in Deep Reinforcement Learning
18 Optimize Your GitHub Profile
19 Study Plan
20 Introduction to Policy-Based Methods
21 Policy Gradient Methods
22 Proximal Policy Optimization
23 Actor-Critic Methods
24 Deep RL for Finance (Optional)
25 Continuous Control
26 Strengthen Your Online Presence Using LinkedIn
27 Study Plan
28 Introduction to Multi-Agent RL
29 Case Study AlphaZero
30 Collaboration and Competition
31 Dynamic Programming
32 Neural Networks
33 Convolutional Neural Networks
34 Deep Learning with PyTorch
35 Cloud Computing
36 Udacity Workspaces
37 C++ Getting Started
38 C++ Vectors
39 Practical C++
40 C++ Object Oriented Programming
41 C++ Intro to Optimization
42 C++ Optimization Practice
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