What you would learn in Reinforcement Learning & Deep RL Python(Theory & Projects) course?
Rewarding Learning (RL) is an aspect of machine learning that is a subset. When using the RL training process, desired actions are rewarded, and unintentional actions are punished. In general, an RL agent can understand and interpret its surroundings and take action. They discover by trial and trial and.
Deep Reinforcement Learning (Deep RL) is another sub-field of machine learning. With Deep RL, intelligent machines and software are trained to be able to recognize their own actions the same way humans learn from experiences. In other words, Deep RL blends RL techniques with Deep Learning (DL) strategies.
Deep RL has the capability to tackle problems that were not able to be solved with machines before. Thus, the possibilities of Deep RL in various fields like robotics and medicine, finance gaming, smart grids, and other areas are huge.
The amazing capability that Artificial Neural Networks (ANNs) have to process data that is unstructured and mimic the human brain is just beginning to be tapped into. We are still just beginning to experience the full potential of this technology, which combines the capabilities and efficiency of RL with ANNs. The latest technology could transform every aspect of science and commerce.
What Makes This Course Different?
In this thorough Learning through Learning by program, every new theory explanation is followed by practical application. This course will help you find the ideal balance between theory and practice. Six projects are included in the program to make learning easier—the goal on teach RL and deep RL to the beginner. Therefore, we've done our best to make things easier.
The course, ' The Complete Handbook to Reinforcement and the Deep Learning Process, reflects the most demanded job-related competencies. The explanations of the fundamental concepts are clear and concise. The teachers focus on the more complex conceptual concepts that make it simpler for you to grasp the concepts. The speed of the video presentation is neither quick nor slow. It's ideal for learning. It will help you understand the necessary RL and Deep RL concepts and methodologies. The curriculum includes:
Simple and simple to master.
* Extremely precise.
* Practical using live code.
Updated with the most current research in this area.
The learning process will be rapid because this is a complete collection of all the essential concepts that you'll be inspired to master RL and deep RL. You will surely learn more than you have learned. At the end of each new concept, a revision task such as Homework/activity/quiz is assigned. Solutions for the tasks are also given. This will help you assess and enhance your knowledge. All of the learning is connected to the concepts and techniques you've already learned. Most of these exercises involve coding, as the purpose will be to help you prepare for real-world implementations.
In addition to the high-quality video content, you'll be able to access easy-to-understand materials for the course, exam questions, detailed notes on subtopics as well as informative handouts throughout this course. We invite you to reach our team of experts you have any questions about this course. We promise that you will receive a prompt reply.
The tutorials for the course are divided into more than 145 small HD clips. Each video will let you discover something new and interesting. You'll also learn the most important concepts and techniques that are used in RL as well as Deep RL, along with numerous practical examples. The total duration of the videos in the course totals 14.4+ hours.
What are the reasons to learn RL & Deep RL?
RL, as well as Deep RL, are among the most popular research areas that are currently being studied in the Artificial Intelligence universe.
The concept of reinforcement learning (RL) is a subset of machine learning concerned with the actions intelligent agents must perform in a given environment to increase the rewards. RL is among three fundamental machine learning models that include supervised learning and unsupervised learning.
Let's take a look at what's the next hot research subject.
Deep Reinforcement Learning (Deep RL) is a subset of machine learning that blends Reinforcement Learning (RL) and Deep Learning (DL). Deep RL incorporates deep learning into the system that allows agents to make decisions based on input data that is unstructured without the intervention of humans. Deep RL algorithms can take in huge amounts of data (e.g., each pixel displayed to the player's screen during a game) and identify the best ways to accomplish an outcome (e.g., achieve the highest game score).
Deep RL is used in a myriad of applications, which include but not only gaming, videogames, and gas natural language processing retail, computer vision transport, education, and healthcare.
The comprehensive course consists of the following topics:
i. What is Reinforcement Learning?
ii. How is it different from other Machine Learning Frameworks?
iii. History of Reinforcement Learning
iv. Why Reinforcement Learning?
v. Real-world examples
vi. Scope of Reinforcement Learning
vii. Limitations of Reinforcement Learning
viii. Exercises and Thoughts
b. Terminologies of RL with Case Studies and Real-World Examples
x. Exercises and Thoughts
2. Hands-on to Basic Concepts
a. Naïve/Random Solution
i. Intro to game
ii. Rules of the game
iv. Implementation using Python
b. RL-based Solution
i. Intro to Q Table
ii. Dry Run of states
iii. How RL works
iv. Implementing RL-based solution using Python
v. Comparison of solutions
3. Different types of RL Solutions
a. Hyper Parameters and Concepts
I. Intro to Epsilon
II. How to update epsilon
IV. Gamma, Discount Factor
VI. Alpha, Learning Rate
VIII. Do’s and Don’ts of Alpha
IX. Q Learning Equation
X. Optimal Value for number of Episodes
XI. When to Stop Training
b. Markov Decision Process
i. Agent-environment interaction
v. Value functions
vi. Optimization of policy
vii. Optimization of the value function
ix. Exercises and Thoughts
i. Intro to QL
ii. Equation Explanation
iii. Implementation using Python
iv. Off-Policy Learning
i. Intro to SARSA
ii. State, Action, Reward, State, Action
iii. Equation Explanation
iv. Implementation using Python
v. On-Policy Learning
e. Q-Learning vs. SARSA
i. Difference in Equation
ii. Difference in Implementation
iii. Pros and Cons
iv. When to use SARSA
v. When to use Q Learning
4. Mini Project Using the Above Concepts (Frozen Lake)
a. Intro to GYM
b. Gym Environment
c. Intro to Frozen Lake Game
e. Implementation using Python
f. Agent Evaluation
5. Deep Learning/Neural Networks
a. Deep Learning Framework
i. Intro to Pytorch
ii. Why Pytorch?
v. Auto Differentiation
vi. Pytorch Practice
b. Architecture of DNN
i. Why DNN?
ii. Intro to DNN
v. Feed Forward
vii. Activation Function
viii. Loss Function
ix. Gradient Descent
x. Weight Initialization
xii. Learning Rate
xiii. Batch Normalization
xvi. Early Stopping
c. Implementing DNN for CIFAR Using Python
6. Deep RL / Deep Q Network (DQN)
a. Getting to DQN
i. Intro to Deep Q Network
ii. Need of DQN
iii. Basic Concepts
iv. How DQN is related to DNN
v. Replay Memory
vi. Epsilon Greedy Strategy
viii. Policy Network
ix. Target Network
x. Weights Sharing/Target update
b. Implementing DQN
i. DQN Project – Cart and Pole using Pytorch
ii. Moving Averages
iii. Visualizing the agent
iv. Performance Evaluation
7. Car Racing Project
a. Intro to game
b. Implementation using DQN
8. Trading Project
a. Stable Baseline
b. Trading Bot using DQN
9. Interview Preparation