Description
Become a Deep Reinforcement Learning Expert is an in-depth reinforcement learning course published by Udacity Academy. Deep reinforcement learning is one of the technologies related to artificial intelligence and deep learning that has been used in a wide range of industries, the most important of which are video games and robotics.
Among the most important algorithms of this technique are DQN (Deep Q-Networks) and DDPG (Deep Deterministic Policy Gradients) which are used in various projects such as self-propelled vehicles. With the help of these algorithms, various operators and neural networks can be trained to perform very complex and time-consuming tasks with high accuracy. At the end of this training course, you will build a complete portfolio and be ready to enter the job market.
Apple, Google, and Facebook are among the most important companies that have made countless advances in investing in deep learning algorithms. This course is an advanced and highly specialized course, and students must have a relative mastery of topics such as Python programming principles, statistics and probability, machine learning, and in-depth learning, and prior to starting this course.
What you will learn in the Become a Deep Reinforcement Learning Expert course:
- Principles and foundations of reinforcement learning
- Architecture and development patterns of deep learning-based systems
- Receive, store and interpret telecommunication data
- Evolutionary algorithm
- Design and development of advanced algorithms to train and practice data to simulated and virtual robots
Become a Deep Reinforcement Learning Expert Course specifications
Publisher: Udacity
Instructor: Alexis Cook , Arpan Chakraborty , Mat Leonard , Luis Serrano , Cezanne Camacho , Dana Sheahan , Chhavi Yadav , Juan Delgado and Miguel Morales
Language: English
Level: Advanced
Number of Courses: 42
Duration: 10 to 10 15 hours a week for about 4 months
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Become a Deep Reinforcement Learning Expert Course topics
Part 01: Introduction to Deep Reinforcement Learning
Module 01: Introduction to Deep Reinforcement Learning
Part 02: Value-Based Methods
Module 01: Value-Based Methods
Module 02: Career Services
Part 03: Policy-Based Methods
Module 01: Policy-Based Methods
Module 02: Career Services
Part 04: Multi-Agent Reinforcement Learning
Module 01: Multi-Agent Reinforcement Learning
Part 05 (Elective): Special Topics in Deep Reinforcement Learning
Module 01: Special Topics in Deep Reinforcement Learning
Part 06 (Elective): Neural Networks in PyTorch
Module 01: Neural Networks in PyTorch
Part 07 (Elective): Computing Resources
Module 01: Computing Resources
Part 08 (Elective): C ++ Programming
Module 01: C ++ Basics
Module 02: Performance Programming in C ++
Prerequisites for the Become a Deep Reinforcement Learning Expert course
What are the prerequisites for enrollment?
We recommend that you complete a course in Deep Learning equivalent to the Deep Learning Nanodegree program prior to entering the program. You will need to be able to communicate fluently and professionally in written and spoken English.
Additionally, you should have the following knowledge:
- Intermediate Python programming knowledge, including:
Strings, numbers, and variables Statements, operators, and expressions Lists, tuples, and dictionaries Conditions, loops Generators & comprehensions Procedures, objects, modules, and libraries Troubleshooting and debugging Research & documentation Problem-solving Algorithms and data structures
Basic shell scripting:
- Run programs from a command line
- Debug error messages and feedback
- Set environment variables
- Establish remote connections
Basic statistical knowledge, including:
- Populations, samples
- Mean, median, mode
- Standard error
- Variation, standard deviations
- Normal distribution
Intermediate differential calculus and linear algebra, including:
- Derivatives & Integrals
- Series expansions
- Matrix operations through eigenvectors and eigenvalues
What software and versions will I need in this program?
You will need a computer running a 64-bit operating system (most modern Windows, OS X, and Linux versions will work) with at least 8GB of RAM, along with administrator account permissions sufficient to install programs including Anaconda with Python 3.6 and supporting packages. Your network should allow secure connections to remote hosts (like SSH). We will provide you with instructions to install the required software packages.
Become a Deep Reinforcement Learning Expert Course pictures
Installation guide
In order to view the lessons of the course in an organized and regular manner, run the index.html file and run the videos through this file.
English subtitle
Quality: 720p
download link
Download Part 1 – 1 GB
Download Part 2 – 1 GB
Download Section 3 – 196 MB
file password link
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