IBM
Deep Learning and Reinforcement Learning
IBM

Deep Learning and Reinforcement Learning

This course is part of IBM Machine Learning Professional Certificate

Taught in English

Some content may not be translated

Mark J Grover
Joseph Santarcangelo
Xintong Li

Instructors: Mark J Grover

27,583 already enrolled

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Course

Gain insight into a topic and learn the fundamentals

4.5

(181 reviews)

|

95%

Intermediate level
Some related experience required
31 hours (approximately)
Flexible schedule
Learn at your own pace

Details to know

Shareable certificate

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Assessments

24 quizzes

Course

Gain insight into a topic and learn the fundamentals

4.5

(181 reviews)

|

95%

Intermediate level
Some related experience required
31 hours (approximately)
Flexible schedule
Learn at your own pace

See how employees at top companies are mastering in-demand skills

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Build your Machine Learning expertise

This course is part of the IBM Machine Learning Professional Certificate
When you enroll in this course, you'll also be enrolled in this Professional Certificate.
  • Learn new concepts from industry experts
  • Gain a foundational understanding of a subject or tool
  • Develop job-relevant skills with hands-on projects
  • Earn a shareable career certificate from IBM
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There are 9 modules in this course

This module introduces Deep Learning, Neural Networks, and their applications. You will go through the theoretical background and characteristics that they share with other machine learning algorithms, as well as characteristics that make them stand out as great modeling techniques for specific scenarios. You will  also gain some hands-on practice on Neural Networks and key concepts that help these algorithms converge to robust solutions.

What's included

16 videos1 reading3 quizzes3 app items

In this module, you will learn about the maths behind the popular Back Propagation algorithm used to optimize neural networks. In the Back Propagation notebook, you will also see and understand the use of activation functions. The main purpose of most activation function is to introduce non-linearity in the network so it would be capable of learning more complex patterns. Last, but not least, you will learn to use functions and APIs from the Keras library to solve tasks that involve neural networks, and these tasks start with loading images.

What's included

13 videos1 reading3 quizzes4 app items

You can leverage several options to prioritize the training time or the accuracy of your neural network and deep learning models. In this module you learn about key concepts that intervene during model training, including optimizers and data shuffling. You will also gain hands-on practice using Keras, one of the go-to libraries for deep learning. 

What's included

6 videos1 reading2 quizzes2 app items1 plugin

In this module you become familiar with convolutional neural networks, also known as space invariant artificial neural networks, a type of deep neural networks, frequently used in image AI applications. There are several CNN architectures, you will learn some of the most common ones to add to your toolkit of Deep Learning Techniques.

What's included

9 videos1 reading2 quizzes6 app items

In this module, you will understand what is transfer learning and how it works. You will implement transfer learning in 5 general steps using a variety of popular pre-trained CNN architectures, such as VGG-16 and ResNet-50. You will study the differences among those CNN architectures and see how the invention of each solves the problem of its predecessors. Last, but not least, as we are moving to working with deeper neural networks, you will also be equipped with regularization techniques to prevent overfitting of complex models and networks.

What's included

7 videos1 reading4 quizzes4 app items1 plugin

In this module you become familiar with Recursive Neural Networks (RNNs) and Long-Short Term Memory Networks (LSTM), a type of RNN considered the breakthrough for speech to text recongintion. RNNs are frequently used in most AI applications today, and can also be used for supervised learning. 

What's included

9 videos1 reading3 quizzes5 app items

In this module you become familiar with Autoencoders, an useful application of Deep Learning for Unsupervised Learning. Autoencoders are a neural network architecture that forces the learning of a lower dimensional representation of data, commonly images. In this module you will learn some Deep learning-based techniques for data representation, how autoencoders work, and to describe the use of trained autoencoders for image applications

What's included

7 videos1 reading2 quizzes2 app items1 plugin

In this module, you will learn about two types of generative models, which are Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs). We will look at the theory behind each model and then implement them in Keras for generating artificial images. The goal is usually to generate images that are as realistic as possible. In the last lesson of this module, we will touch on additional topics in deep learning, namely using Keras in a GPU environment for speeding up model training.

What's included

7 videos1 reading3 quizzes4 app items

In this module you become familiar with other novel applications of Neural Networks. You will learn about Generative Adversarial Networks, frequently referred to as GANs, which are an application of Neural Networks to generate new data. Finally, you learn about Reinforcement Learning, one of the big promises for A.I., based on training algorithms by using rewards, instead of using a method to minimize error, which is what we have been using throughout the course.

What's included

5 videos1 reading2 quizzes1 peer review1 app item

Instructors

Instructor ratings
4.3 (76 ratings)
Mark J Grover
IBM
13 Courses84,630 learners
Joseph Santarcangelo
IBM
25 Courses1,353,097 learners
Xintong Li
IBM
2 Courses36,286 learners

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IBM

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