IBM
Building Deep Learning Models with TensorFlow
IBM

Building Deep Learning Models with TensorFlow

This course is part of IBM AI Engineering Professional Certificate

Taught in English

Some content may not be translated

Samaya Madhavan
JEREMY NILMEIER
Romeo Kienzler

Instructors: Samaya Madhavan

27,006 already enrolled

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Course

Gain insight into a topic and learn the fundamentals

4.4

(798 reviews)

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

What you'll learn

  • Explain foundational TensorFlow concepts such as the main functions, operations and the execution pipelines.

  • Describe how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions.

  • Analyze different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders.

  • Apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained.

Details to know

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Assessments

5 quizzes

Course

Gain insight into a topic and learn the fundamentals

4.4

(798 reviews)

Intermediate level
Some related experience required
7 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 AI Engineering 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 5 modules in this course

In this module, you will learn about TensorFlow, and use it to create Linear and Logistic Regression models. You will also learn about the fundamentals of Deep Learning.

What's included

5 videos2 readings1 quiz4 app items

In this module, you will learn about about Convolutional Neural Networks, and the building blocks of a convolutional neural network, such as convolution and feature learning. You will also learn about the popular MNIST database. Finally, you will learn how to build a Multi-layer perceptron and convolutional neural networks in Python and using TensorFlow.

What's included

3 videos1 quiz1 app item

In this module, you will learn about the recurrent neural network model, and special type of a recurrent neural network, which is the Long Short-Term Memory model. Also, you will learn about the Recursive Neural Tensor Network theory, and finally, you will apply recurrent neural networks to language modelling.

What's included

4 videos1 quiz2 app items

In this module, you will learn about the applications of unsupervised learning. You will learn about Restricted Boltzmann Machines (RBMs), and how to train an RBM. Finally, you will apply Restricted Boltzmann Machines to build a recommendation system.

What's included

2 videos1 quiz1 app item

In this module, you will mainly learn about autoencoders and their architecture.

What's included

2 videos1 reading1 quiz1 app item

Instructors

Instructor ratings
4.5 (105 ratings)
Samaya Madhavan
IBM
1 Course27,006 learners
JEREMY NILMEIER
IBM
1 Course27,006 learners

Offered by

IBM

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Showing 3 of 798

4.4

798 reviews

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ZR
5

Reviewed on Jul 2, 2020

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5

Reviewed on Nov 12, 2023

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4

Reviewed on Jul 25, 2020

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