Brochure
Enquire- Introduction and Outline
- Where to get the Code
- How to Succeed in this Course
- Tensorflow or Theano - Your Choice!
Advanced AI: Deep Reinforcement Learning in Python
BY
Udemy
Master the skills in artificial intelligence using techniques and processes of deep reinforcement learning and neural ...Read more
Online
₹ 1199
Quick Facts
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Medium of instructions
English
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Mode of learning
Self study
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Mode of Delivery
Video and Text Based
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Course overview
Advanced AI: Deep Reinforcement Learning in Python certification course is designed by Lazy Programmer Inc. - artificial intelligence and machine learning engineer and its team which is presented by Udemy, an online learning platform, to assist students to succeed in their occupations.
Advanced AI: Deep Reinforcement Learning in Python online course focuses on the use of deep learning and neural networks in reinforcement learning. The course will teach about the TD Lambda algorithm to deepen the understanding of temporal difference learning, and a particular variety of neural networks called the RBF network, policy gradient method, and deep Q-Learning (DQN), and asynchronous advantage actor-critic (A3c).
Advanced AI: Deep Reinforcement Learning in Python syllabus is designed for individuals who are familiar with programming as this course comes with prerequisites such as college-level calculus, probability, object-oriented programming, python coding, Numpy coding, linear regression, gradient descent, writing ANNs and CNNs in Theano or TensorFlow, Markov decision processes (MDPs) and implement dynamic programming, Monte Carlo, and temporal difference learning to solve MDPs. The interested learners could enrol on the course by paying the fee.
The highlights
- Certificate of completion
- Self-paced course
- A fully online course
- English videos with multi-language subtitles
- 10.5 hours of pre-recorded video content
- 30-day money-back guarantee
- Unlimited access
- Accessible on mobile devices and TV
Program offerings
- Certificate of completion
- Self-paced course
- English videos
- Multi-language subtitles
- 10.5 hours of pre-recorded video content
- 30-day money-back guarantee
- Unlimited access
- Accessible on mobile devices and tv
Course and certificate fees
Fees information
₹ 1,199
certificate availability
Yes
certificate providing authority
Udemy
Who it is for
What you will learn
Machine learning
Knowledge of python
Knowledge of artificial intelligence
After completing the Advanced AI: Deep Reinforcement Learning in Python certification course, candidates will get to know about reinforcement learning using RBF networks, policy gradient methods using neural networks, creating several deep learning agents like DQN and A3C. Candidates will also learn to solve problems using reinforcement learning techniques, Q-learning using deep neural networks and convolutional neural networks with deep Q-learning.
The syllabus
Introduction and Logistics
The Basics of Reinforcement Learning
- Reinforcement Learning Section Introduction
- Elements of a Reinforcement Learning Problem
- States, Actions, Rewards, Policies
- Markov Decision Processes (MDPs)
- The Return
- Value Functions and the Bellman Equation
- What does it mean to “learn”?
- Solving the Bellman Equation with Reinforcement Learning (pt 1)
- Solving the Bellman Equation with Reinforcement Learning (pt 2)
- Epsilon-Greedy
- Q-Learning
- How to Learn Reinforcement Learning
- Suggestion Box
OpenAI Gym and Basic Reinforcement Learning Techniques
- OpenAI Gym Tutorial
- Random Search
- Saving a Video
- CartPole with Bins (Theory)
- CartPole with Bins (Code)
- RBF Neural Networks
- RBF Networks with Mountain Car (Code)
- RBF Networks with CartPole (Theory)
- RBF Networks with CartPole (Code)
- Theano Warmup
- Tensorflow Warmup
- Plugging in a Neural Network
- OpenAI Gym Section Summary
TD Lambda
- N-Step Methods
- N-Step in Code
- TD Lambda
- TD Lambda in Code
- TD Lambda Summary
Policy Gradients
- Policy Gradient Methods
- Policy Gradient in TensorFlow for CartPole
- Policy Gradient in Theano for CartPole
- Continuous Action Spaces
- Mountain Car Continuous Specifics
- Mountain Car Continuous Theano
- Mountain Car Continuous Tensorflow
- Mountain Car Continuous Tensorflow (v2)
- Mountain Car Continuous Theano (v2)
- Policy Gradient Section Summary
Deep Q-Learning
- Deep Q-Learning Intro
- Deep Q-Learning Techniques
- Deep Q-Learning in Tensorflow for CartPole
- Deep Q-Learning in Theano for CartPole
- Additional Implementation Details for Atari
- Pseudocode and Replay Memory
- Deep Q-Learning in Tensorflow for Breakout
- Deep Q-Learning in Theano for Breakout
- Partially Observable MDPs
- Deep Q-Learning Section Summary
A3C
- A3C - Theory and Outline
- A3C - Code pt 1 (Warmup)
- A3C - Code pt 2
- A3C - Code pt 3
- A3C - Code pt 4
- A3C - Section Summary
- Course Summary
Theano and Tensorflow Basics Review
- (Review) Theano Basics
- (Review) Theano Neural Network in Code
- (Review) Tensorflow Basics
- (Review) Tensorflow Neural Network in Code
Setting Up Your Environment (FAQ by Student Request)
- Anaconda Environment Setup
- How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow
Extra Help With Python Coding for Beginners (FAQ by Student Request)
- How to Code by Yourself (part 1)
- How to Code by Yourself (part 2)
- Proof that using Jupyter Notebook is the same as not using it
- Python 2 vs Python 3
- Is Theano Dead?
Effective Learning Strategies for Machine learning (FAQ by Student Request)
- How to Succeed in this Course (Long Version)
- Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced?
- Machine Learning and AI Prerequisite Roadmap (pt 1)
- Machine Learning and AI Prerequisite Roadmap (pt 2)
Appendix / FAQ Finale
- What is the Appendix?
- BONUS
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