Module 1 Section: 1 - Welcome to the course!
Unit 1 Introduction
Unit 2 Where to get the Materials
Unit 3 BONUS: Meet Your Instructors
Module 2 Section 2: Part 0 - Fundamentals Of Reinforcement Learning
Unit 1 Welcome to Part 0 - Fundamentals of Reinforcement Learning
Module 3 Section 3: Q- Learning Intuition
Unit 1 Plan of Attack
Unit 2 What is reinforcement learning?
Unit 3 The Bellman Equation
Unit 4 The "Plan"
Unit 5 Markov Decision Process
Unit 6 Policy vs Plan
Unit 7 Adding a "Living Penalty"
Unit 8 Q-Learning Intuition
Unit 9 Temporal Difference
Unit 10 Q-Learning Visualization
Module 4 Section 4: ---------- Part 1 - Self-Driving Car (Deep Q-Learning) ----------
Unit 1 Welcome to Part 1 - Self-Driving Car (Deep Q-Learning)
Module 5 Section 5: Deep Q-Learning Intuition
Unit 1 Plan of Attack
Unit 2 Deep Q-Learning Intuition - Learning
Unit 3 Deep Q-Learning Intuition - Acting
Unit 4 Experience Relay
Unit 5 Action Selection Policies
Module 6 Section 6: Installation for Part 1
Unit 1 Plan of Attack (Pratice Tutorials)
Unit 2 Where to get the Materials
Unit 3 Windows Option 1: End-to-End installation steps
Unit 4 Windows Option 2 - Part A: Installing Ubuntu on Windows
Unit 5 Windows Option 2 - Part B: Installing PyTorch and Kivy on your Ubuntu VM
Unit 6 Mac or Linux: Installing Anaconda
Unit 7 Mac or Linux: Installing PyTorch and Kivy
Unit 8 Getting Started
Module 7 Section 7: Creating the environment
Unit 1 Self Driving Car - Step 1
Unit 2 Self Driving Car - Step 2
Module 8 Section 8: Building the AI
Unit 1 Self Driving Car - Step 3
Unit 2 Self Driving Car - Step 4
Unit 3 Self Driving Car - Step 5
Unit 4 Self Driving Car - Step 6
Unit 5 Self Driving Car - Step 7
Unit 6 Self Driving Car - Step 8
Unit 7 Self Driving Car - Step 9
Unit 8 Self Driving Car - Step 10
Unit 9 Self Driving Car - Step 11
Unit 10 Self Driving Car - Step 12
Unit 11 Self Driving Car - Step 13
Unit 12 Self Driving Car - Step 14
Unit 13 Self Driving Car - Step 15
Unit 14 Self Driving Car - Step 16
Module 9 Section 9: Playing with the AI
Unit 1 Self Driving Car - Level 1
Unit 2 Self Driving Car - Level 2
Unit 3 Self Driving Car - Level 3
Unit 4 Self Driving Car - Level 4
Unit 5 Challenge Solutions
Module 10 Section 10: ---------- Part 2 - Doom (Deep Convolutional Q-Learning) ----------
Unit 1 Welcome to Part 2 - Doom (Deep Convolutional Q-Learning)
Module 11 Section 11: Deep Convolutional Q-Learning Intuition
Unit 1 Plan of Attack
Unit 2 Deep Convolutional Q-Learning Intuition
Unit 3 Eligibility Trace
Module 12 Section 12: Installation for Part 2
Unit 1 Where to get the Materials
Unit 2 Installing Open AI Gym and ppaquette
Module 13 Section 10: - Building an AI
Unit 1 Doom - Step 1
Unit 2 Doom - Step 2
Unit 3 Doom - Step 3
Unit 4 Doom - Step 4
Unit 5 Doom - Step 5
Unit 6 Doom - Step 6
Unit 7 Doom - Step 7
Unit 8 Doom - Step 8
Unit 9 Doom - Step 9
Unit 10 Doom - Step 10
Unit 11 Doom - Step 11
Unit 12 Doom - Step 12
Unit 13 Doom - Step 13
Unit 14 Doom - Step 14
Unit 15 Doom - Step 15
Unit 16 Doom - Step 16
Unit 17 Doom - Step 17
Module 14 Section: 11 - Playing with the AI
Unit 1 Watching our AI play Doom
Module 15 Section 15: ---------- Part 3 - Breakout (A3C) ----------
Unit 1 Welcome to Part 3 - Breakout (A3C)
Module 16 Section 16: A3C Intuition
Unit 1 Actor-Critic
Unit 2 Asynchronous
Unit 3 Advantage
Unit 4 LSTM Layer
Module 17 Section 17: Installation for Part 3
Unit 1 Installing OpenCV
Module 18 Section 18: Building an AI
Unit 1 Breakout - Step 1
Unit 2 Breakout - Step 2
Unit 3 Breakout - Step 3
Unit 4 Breakout - Step 4
Unit 5 Breakout - Step 5
Unit 6 Breakout - Step 6
Unit 7 Breakout - Step 7
Unit 8 Breakout - Step 8
Unit 9 Breakout - Step 9
Unit 10 Breakout - Step 10
Unit 11 Breakout - Step 11
Unit 12 Breakout - Step 12
Unit 13 Breakout - Step 13
Unit 14 Breakout - Step 14
Unit 15 Breakout - Step 15
Module 19 Section 19: Annex 1: Artificial Neural Networks
Unit 1 What is Deep Learning?
Unit 2 Plan of Attack
Unit 3 The Neuron
Unit 4 The Activation Function
Unit 5 How do Neural Networks work?
Unit 6 How do Neural Networks learn?
Unit 7 Gradient Descent
Unit 8 Stochastic Gradient Descent
Unit 9 Backpropagation
Module 20 Section 20: Annex 2: Convolutional Neural Networks
Unit 1 Plan of Attack
Unit 2 What are convolutional neural networks?
Unit 3 Step 1 - Convolution Operation
Unit 4 Step 1(b) - ReLU Layer
Unit 5 Step 2 - Pooling
Unit 6 Step 3 - Flattening
Unit 7 Step 4 - Full Connection
Unit 8 Summary
Unit 9 Softmax & Cross-Entropy
Module 21 Unpublished Section - EARLY BIRD BONUSES
Unit 1 BONUS #1: AI Algorithms Cheatsheet
Unit 2 BONUS #2A: LaTeX Mini-Handbook
Unit 3 BONUS #2B: LaTeX Mini-Handbook - Video Walkthrough
Unit 4 BONUS 3: Exclusive AI Insights Video
Unit 5 How To Download Your Early Bird Bonuses
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Artificial Intelligence

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Course Description

Here is what you will get with this course:

1. Complete beginner to expert AI skills – Learn to code self-improving AI for a range of purposes. In fact, we code together with you. Every tutorial starts with a blank page and we write up the code from scratch. This way you can follow along and understand exactly how the code comes together and what each line means.

2. Code templates – Plus, you’ll get downloadable Python code templates for every AI you build in the course. This makes building truly unique AI as simple as changing a few lines of code. If you unleash your imagination, the potential is unlimited.

3. Intuition Tutorials – Where most courses simply bombard you with dense theory and set you on your way, we believe in developing a deep understanding for not only what you’re doing, but why you’re doing it. That’s why we don’t throw complex mathematics at you, but focus on building up your intuition in coding AI making for infinitely better results down the line.

4. Real-world solutions – You’ll achieve your goal in not only 1 game but in 3. Each module is comprised of varying structures and difficulties, meaning you’ll be skilled enough to build AI adaptable to any environment in real life, rather than just passing a glorified memory “test and forget” like most other courses. Practice truly does make perfect.

5. In-course support – We’re fully committed to making this the most accessible and results-driven AI course on the planet. This requires us to be there when you need our help. That’s why we’ve put together a team of professional Data Scientists to support you in your journey, meaning you’ll get a response from us within 48 hours maximum.