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Deep Learning and Reinforcement Learning
  • Level Professional
  • Duration 32 hours
  • Course by IBM
  • Offered by

About

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.

Modules

Introduction to Neural Networks

    • 11 Videos
    • 2 ExternalTool
    • 1 Assignment
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1 Assignment

  • Practice: Introduction to Neural Networks

2 External Tool

  • Neural Networks with Sklearn
  • Introduction to Neural Networks Demo (Activity)

11 Videos

  • Course Introduction
  • Introduction to Neural Networks
  • Basics of Neurons
  • Neural Networks with Sigmoid Function
  • Neuron in Action
  • Neural Networks with SKlearn
  • Forward Propagation
  • Matrix Representation of Forward Propagation
  • Main Types of Deep Neural Network
  • (Optional) Introduction to Neural Networks Notebook - Part 1
  • (Optional) Introduction to Neural Networks Notebook - Part 2

Optimization and Gradient Descent

    • 5 Videos
    • 1 ExternalTool
    • 1 Assignment
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1 Assignment

  • Practice: Optimization and Gradient Descent

1 External Tool

  • Gradient Descent Demo (Activity)

5 Videos

  • Gradient Descent Basics
  • Compare Different Gradient Descent Methods
  • (Optional) Gradient Descent Notebook - Part 1
  • (Optional) Gradient Descent Notebook - Part 2
  • (Optional) Gradient Descent Notebook - Part 3

End of module review

    • 1 Readings
    • 1 Assignment
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1 Assignment

  • End of Module Quiz

1 Readings

  • Summary/Review

Back Propagation, Activation Functions

    • 7 Videos
    • 1 ExternalTool
    • 1 Assignment
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1 Assignment

  • Practice: Back Propagation, Activation Functions

1 External Tool

  • Backpropagation Demo (Activity)

7 Videos

  • How to Train a Neural Network
  • Backpropagation
  • (Optional) Backpropagation Notebook - Part 1
  • (Optional) Backpropagation Notebook - Part 2
  • The Sigmoid Activation Function
  • Other Popular Activation Functions
  • (Optional) Backpropagation Notebook - Part 3

Keras Library

    • 6 Videos
    • 3 ExternalTool
    • 1 Assignment
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1 Assignment

  • Practice: Keras Library

3 External Tool

  • Keras Demo (Activity)
  • Regression with Keras
  • (Optional) Loading Images with Keras

6 Videos

  • Popular Deep Learning Library
  • A Typical Keras Workflow
  • Implementing an Example Neural Network in Keras
  • (Optional) Keras Notebook - Part 1
  • (Optional) Keras Notebook - Part 2
  • (Optional) Keras Notebook - Part 3

End of module review

    • 1 Readings
    • 1 Assignment
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1 Assignment

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1 Readings

  • Summary/Review

Optimizers and Data Shuffling

    • 6 Videos
    • 2 ExternalTool
    • 1 Plugin
    • 1 Assignment
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1 Assignment

  • Practice: Optimizers and Data Shuffling

2 External Tool

  • Optimizers
  • Grid Search with Keras

6 Videos

  • Optimizers and Momentum
  • Regularization Techniques for Deep Learning
  • Popular Optimizers
  • Details of Training Neural Networks
  • Data Shuffling
  • Transforms

End of module review

    • 1 Readings
    • 1 Assignment
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1 Assignment

  • End of Module Quiz

1 Readings

  • Summary/Review

Convolutional Neural Networks

    • 9 Videos
    • 6 ExternalTool
    • 1 Assignment
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1 Assignment

  • Practice: Convolutional Neural Networks

6 External Tool

  • Categorical Cross Entropy
  • Images Convolution
  • Padding, Pooling, and Stride
  • Channels and Flattening
  • Training the Network
  • Convolutional Neural Networks Demo (Activity)

9 Videos

  • Categorical Cross Entropy
  • Introduction to Convolutional Neural Networks (CNN)
  • Images Dataset
  • Kernels
  • Convolution for Color Images
  • Convolutional Settings - Padding and Stride
  • Convolutional Settings - Depth and Pooling
  • (Optional) Demo CNN Notebook - Part 1
  • (Optional) Demo CNN Notebook - Part 2

End of module review

    • 1 Readings
    • 1 Assignment
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1 Assignment

  • End of Module Quiz

1 Readings

  • Summary/Review

Transfer Learning

    • 3 Videos
    • 1 ExternalTool
    • 1 Assignment
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1 Assignment

  • Practice: Transfer Learning

1 External Tool

  • Transfer Learning Demo (Activity)

3 Videos

  • Introduction to Transfer Learning
  • Transfer Learning and Fine Tuning
  • (Optional) Transfer Learning Notebook

Convolutional Neural Network Architectures

    • 5 Videos
    • 2 ExternalTool
    • 1 Assignment
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1 Assignment

  • Practice: Convolutional Neural Network Architectures

2 External Tool

  • Types of Model APIs in Keras
  • Transfer Learning Examples with Existing Architectures

5 Videos

  • Convolutional Neural Network Architectures - LeNet
  • Convolutional Neural Network Architectures - AlexNet
  • VGG
  • Convolutional Neural Network Architectures - Inception
  • Convolutional Neural Network Architectures - ResNet

Regularization

    • 1 ExternalTool
    • 1 Plugin
    • 1 Assignment
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1 Assignment

  • Practice: Regularization

1 External Tool

  • Regularization Techniques

End of module review

    • 1 Readings
    • 1 Assignment
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1 Assignment

  • End of Module Quiz

1 Readings

  • Summary/Review

Recurrent Neural Networks

    • 5 Videos
    • 4 ExternalTool
    • 1 Assignment
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1 Assignment

  • Practice: Recurrent Neural Networks

4 External Tool

  • (Optional) Introduction to Sequential Data
  • Existing Recurrent Neural Networks
  • Word Embeddings
  • Recurrent Neural Networks Demo (Activity)

5 Videos

  • Recurrent Neural Networks (RNNs)
  • State and Recurrent Neural Networks
  • Details Recurrent Neural Networks
  • (Optional) Recurrent Neural Networks Notebook - Part 1
  • (Optional) Recurrent Neural Networks Notebook - Part 2

LSTM Networks

    • 4 Videos
    • 1 ExternalTool
    • 1 Assignment
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1 Assignment

  • Practice: LSTM and GRU

1 External Tool

  • LSTM and GRU Demo (Activity)

4 Videos

  • Long-Short Term Memory (LSTM) Networks
  • LSTM Explanation
  • Gated Recurrent Unit
  • Gated Recurrent Unit Details

End of module review

    • 1 Readings
    • 1 Assignment
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1 Assignment

  • End of Module Quiz

1 Readings

  • Summary/Review

Autoencoders

    • 2 Videos
    • 1 ExternalTool
    • 1 Plugin
    • 1 Assignment
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1 Assignment

  • Practice: Autoencoders

1 External Tool

  • Autoencoders

2 Videos

  • Introduction to Autoencoders
  • Autoencoders

Autoencoders Lab

    • 5 Videos
    • 1 ExternalTool
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1 External Tool

  • Autoencoders Demo (Activity)

5 Videos

  • (Optional) Autoencoders Notebook - Part 1
  • (Optional) Autoencoders Notebook - Part 2
  • (Optional) Autoencoders Notebook - Part 3
  • (Optional) Autoencoders Notebook - Part 4
  • (Optional) Autoencoders Notebook - Part 5

End of module review

    • 1 Readings
    • 1 Assignment
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1 Assignment

  • End of Module Quiz

1 Readings

  • Summary/Review

Variational Autoencoders

    • 2 Videos
    • 1 ExternalTool
    • 1 Assignment
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1 Assignment

  • Practice: Variational Autoencoders

1 External Tool

  • Variational Autoencoder

2 Videos

  • What is a Variational Autoencoder
  • How Variational Autoencoders Work

Generative Adversarial Networks

    • 5 Videos
    • 3 ExternalTool
    • 1 Assignment
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1 Assignment

  • Practice: Generative Adversarial Networks

3 External Tool

  • GANS Lab 1
  • GANS Lab 2
  • GPU with Keras

5 Videos

  • Introduction to GANs
  • How GANS Work
  • Issues with Training GANS
  • Additional Topics in Deep Learning
  • Model Agnostic Explainable AI

End of module review

    • 1 Readings
    • 1 Assignment
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1 Assignment

  • End of Module Quiz

1 Readings

  • Summary/Review

Reinforcement Learning

    • 5 Videos
    • 1 ExternalTool
    • 1 Assignment
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1 Assignment

  • Practice: Reinforcement Learning

1 External Tool

  • Reinforcement Learning Demo (Activity)

5 Videos

  • Reinforcement Learning (RL)
  • (Optional) Reinforcement Learning Notebook - Part 1
  • (Optional) Reinforcement Learning Notebook - Part 2
  • (Optional) Reinforcement Learning Notebook - Part 3
  • (Optional) Reinforcement Learning Notebook - Part 4

End of module review

    • 1 Readings
    • 1 Assignment
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1 Assignment

  • End of Module Quiz

1 Readings

  • Summary/Review

Final Project

    • 1 PeerReview
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1 Peer Review

  • Final Project

Auto Summary

Dive into the world of Data Science and AI with this professional course on Deep Learning and Reinforcement Learning, guided by an expert instructor. Gain in-depth knowledge of neural networks, modern architectures, and hands-on experience with these advanced Machine Learning techniques. Ideal for aspiring data scientists, this 1920-hour course on Coursera offers Starter and Professional subscription options. Enhance your Python skills and foundational understanding of Data Cleaning, EDA, and various Machine Learning methods. Become proficient in clustering, dimensionality-reduction algorithms, and more. Join now and elevate your AI expertise!

Mark J Grover
Instructors

Mark J Grover

Joseph Santarcangelo
Instructors

Joseph Santarcangelo

Xintong Li
Instructors

Xintong Li

Kopal Garg
Instructors

Kopal Garg

Miguel Maldonado
Instructors

Miguel Maldonado