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Deep Learning and Reinforcement Learning

Approx. 14 hours to complete

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Course Summary

This course teaches Deep Learning and Reinforcement Learning in a comprehensive and practical manner. Students will learn the fundamentals of these two powerful techniques and apply them to real-world scenarios.

Key Learning Points

Understand the fundamentals of deep learning and reinforcement learning
Learn how to apply these techniques to real-world scenarios
Gain practical experience through assignments and projects

Learning Outcomes

Understand the fundamentals of deep learning and reinforcement learning
Apply these techniques to real-world scenarios
Gain practical experience through assignments and projects

Prerequisites or good to have knowledge before taking this course

Basic knowledge of Python programming
Understanding of linear algebra and calculus

Course Difficulty Level

Intermediate

Course Format

Online
Self-paced
Video Lectures
Assignments
Projects

Similar Courses

Machine Learning
Neural Networks and Deep Learning

Related Education Paths

Notable People in This Field

Founder, deeplearning.ai
Co-founder & CEO, DeepMind

Related Books

Description

This course introduces you to two of the most sought-after disciplines in Machine Learning: Deep Learning and Reinforcement Learning. Deep Learning is a subset of Machine Learning that has applications in both Supervised and Unsupervised Learning, and is frequently used to power most of the AI applications that we use on a daily basis. First you will learn about the theory behind Neural Networks, which are the basis of Deep Learning, as well as several modern architectures of Deep Learning. Once you have developed a few Deep Learning models, the course will focus on Reinforcement Learning, a type of Machine Learning that has caught up more attention recently. Although currently Reinforcement Learning has only a few practical applications, it is a promising area of research in AI that might become relevant in the near future.

After this course, if you have followed the courses of the IBM Specialization in order, you will have considerable practice and a solid understanding in the main types of Machine Learning which are: Supervised Learning, Unsupervised Learning, Deep Learning, and Reinforcement Learning. By the end of this course you should be able to: Explain the kinds of problems suitable for Unsupervised Learning approaches Explain the curse of dimensionality, and how it makes clustering difficult with many features Describe and use common clustering and dimensionality-reduction algorithms Try clustering points where appropriate, compare the performance of per-cluster models Understand metrics relevant for characterizing clusters Who should take this course? This course targets aspiring data scientists interested in acquiring hands-on experience with Deep Learning and Reinforcement Learning. What skills should you have? To make the most out of this course, you should have familiarity with programming on a Python development environment, as well as fundamental understanding of Data Cleaning, Exploratory Data Analysis, Unsupervised Learning, Supervised Learning, Calculus, Linear Algebra, Probability, and Statistics.

Outline

Introduction to Neural Networks
Course Introduction
Introduction to Neural Networks - Part 1
Introduction to Neural Networks - Part 2
Introduction to Neural Networks - Part 3
Introduction to Neural Networks - Part 4
Optimization and Gradient Descent
Gradient Descent Notebook - Part 1
Gradient Descent Notebook - Part 2
Gradient Descent Notebook - Part 3
Introduction to Neural Networks Notebook - Part 1
Introduction to Neural Networks Notebook - Part 2
Introduction to Backpropagation in Neural Networks - Part 1
Backpropagation - Part 2
Backpropagation Notebook - Part 1
Backpropagation Notebook - Part 2
Backpropagation Notebook - Part 3
Other Activation Functions
Regularization Techniques for Deep Learning
Introduction to Neural Networks Demo (Activity)
Introduction to Neural Networks Demo (Activity)
Backpropagation Demo (Activity)
Summary/Review
Check for Understanding
Check for Understanding
End of Module Quiz

Neural Network Optimizers and Keras
Optimizers
Details of Training Neural Networks
Data Shuffling
Keras
Keras Notebook - Part 1
Keras Notebook - Part 2
Keras Notebook - Part 3
Keras Demo (Activity)
Summary/Review
Check for Understanding
Check for Understanding
End of Module Quiz

Convolutional Neural Networks
Important Transformations
Introduction to Convolutional Neural Networks - Part 1
Introduction to Convolutional Neural Networks - Part 2
Convolutional Settings - Padding and Stride
Convolutional Settings - Depth and Pooling
Demo CNN Notebook - Part 1
Demo CNN notebook - Part 2
Transfer Learning - Part 1
Transfer Learning - Part 2
Transfer Learning Notebook
Convolutional Neural Network Architectures - Part 1
Convolutional Neural Network Architectures - Part 2
Convolutional Neural Network Architectures - Part 3
Convolutional Neural Networks Demo (Activity)
Transfer Learning Demo (Activity)
Summary/Review
Check for Understanding
Check for Understanding
Check for Understanding
End of Module Quiz

Recurrent Neural Networks and Long-Short Term Memory Networks
Recurrent Neural Networks (RNNs) - Part 1
Recurrent Neural Networks (RNNs) - Part 2
Recurrent Neural Networks Notebook - Part 1
Recurrent Neural Networks Notebook - Part 2
Long-Short Term Memory (LSTM) Networks
Gated Recurrent Unit - Part 1
Gated Recurrent Unit - Part 2
Recurrent Neural Networks Demo (Activity)
Summary/Review
Check for Understanding
Check for Understanding
End of Module Quiz

Deep Learning with Autoencoders
Autoencoders - Part 1
Autoencoders - Part 2
Variational Autoencoders - Part 1
Variational Autoencoders - Part 2
Autoencoders Notebook - Part 1
Autoencoders Notebook - Part 2
Autoencoders Notebook - Part 3
Autoencoders Notebook - Part 4
Autoencoders Notebook - Part 5
Autoencoders Demo (Activity)
Summary/Review
Check for Understanding
Check for Understanding
End of Module Quiz

Deep Learning Applications and Reinforcement Learning
Generative Adversarial Networks - Part 1
Generative Adversarial Networks - Part 2
Additional Topics in Deep Learning
Reinforcement Learning (RL)
Reinforcement Learning Notebook - Part 1
Reinforcement Learning Notebook - Part 2
Reinforcement Learning Notebook - Part 3
Reinforcement Learning Notebook - Part 4
Reinforcement Learning Demo (Activity)
Summary/Review
Check for Understanding
Check for Understanding
Module 6 Quiz

Summary of User Reviews

Discover the exciting world of deep learning and reinforcement learning with this comprehensive course from Coursera. Learners rave about the engaging and informative content, which provides a solid foundation in these cutting-edge technologies. One key aspect that many users appreciate is the hands-on approach, which allows them to apply their newfound knowledge in practical ways.

Pros from User Reviews

Engaging and informative content
Hands-on approach to learning
Solid foundation in deep learning and reinforcement learning

Cons from User Reviews

Some learners find the pace too fast
Requires a strong background in math and programming
May be too technical for beginners

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