Deep learning is revolutionizing many fields, including computer vision, natural language processing, and robotics. In addition, Keras, a high-level neural networks API written in Python, has become an essential part of TensorFlow, making deep learning accessible and straightforward. Mastering these techniques will open many opportunities in research and industry.
Building Deep Learning Models with TensorFlow
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
Instructors: Samaya Madhavan
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Course
Gain insight into a topic and learn the fundamentals
4.4
(844 reviews)
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93%
Intermediate level
Recommended experience
7 hours (approximately)
Flexible schedule
Learn at your own pace
What you'll learn
Create custom layers and models in Keras and integrate Keras with TensorFlow 2.x
Develop advanced convolutional neural networks (CNNs) using Keras
Develop Transformer models for sequential data and time series prediction
Explain key concepts of Unsupervised learning in Keras, Deep Q-networks (DQNs), and reinforcement learning
Skills you'll gain
Details to know
Shareable certificate
Add to your LinkedIn profile
Assessments
5 quizzes
Course
Gain insight into a topic and learn the fundamentals
4.4
(844 reviews)
|
93%
Intermediate level
Recommended experience
7 hours (approximately)
Flexible schedule
Learn at your own pace
See how employees at top companies are mastering in-demand skills
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
Earn a career certificate
Add this credential to your LinkedIn profile, resume, or CV
Share it on social media and in your performance review
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 quiz2 app items
Instructors
Instructor ratings
4.5 (113 ratings)Offered by
Recommended if you're interested in Machine Learning
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