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Keras and Tensorflow for Deep Learning

Premium
IntermediateCourse

Enhance your deep learning skills with Keras and Tensorflow! Learn advanced CNN techniques, build transformers and GANs, dive into reinforcement learning, and optimize models. Apply your knowledge with hands-on labs and a final project using transfer learning.

Language

  • English

Topic

  • Deep Learning

Skills You Will Learn

  • Convolutional Neural Networks, Tensorflow, Keras, Deep Learning, Natural Language Processing, Unsupervised Learning

Offered By

  • IBMSkillsNetwork

Estimated Effort

  • 23 Hours

Platform

  • SkillsNetwork

Last Update

  • February 6, 2026
About this Course

About this course

In this Intermediate course, you will gain an in-depth understanding of Keras and its functionalities. Learn how to use the Keras Functional API to design flexible and complex models, and explore subclassing to create custom layers and models. You will become proficient in building custom deep learning architectures, utilizing TensorFlow 2.x to enhance model performance.
 
You will dive into advanced techniques in convolutional neural networks (CNNs) and data augmentation. Learn how to augment data effectively to improve model accuracy and performance, and apply transfer learning using pre-trained models for image classification. You’ll also explore the use of transpose convolutions in image processing tasks.
 
The course also covers unsupervised learning techniques, such as autoencoders and diffusion models, allowing you to create and train generative models like GANs. You’ll discover how to use these methods to generate new data and work with complex unsupervised tasks, laying the foundation for powerful machine learning applications.
 
You will further enhance your skills by mastering advanced Keras techniques, such as custom training loops, hyperparameter tuning, and model optimization. Through hands-on labs, you’ll learn how to fine-tune models and optimize them for better accuracy and efficiency, equipping you with the tools to improve any deep learning model.
 
The course also introduces reinforcement learning, where you will learn the principles behind Q-learning and deep Q-networks (DQNs). Gain practical experience in building and implementing these models, exploring their potential in solving decision-making problems and real-world applications.
 
Finally, you will apply your new knowledge by tackling a real-world deep learning project. Using transfer learning, you will classify waste products, gaining hands-on experience in practical model deployment and problem-solving. By the end of the course, you will have a strong grasp of the latest advancements in deep learning and the skills needed to build and deploy cutting-edge models in Keras.

What you will learn:

After completing this course, you will be able to: 
  • Define advanced deep learning concepts, including Keras APIs, CNNs, transformers, GANs, and reinforcement learning principles.
  • Explain the inner workings of custom layers, data augmentation, transfer learning, and reinforcement learning algorithms.
  • Implement advanced techniques like custom training loops, model optimization, and generative models in Keras.
  • Compare and contrast CNNs, transformers, and unsupervised learning models, assessing their strengths for different use cases.
  • Optimize deep learning models through hyperparameter tuning and reinforcement learning strategies.
  • Design and develop advanced deep learning models using Keras, including transformers, GANs, and reinforcement learning models, and implement them in practical scenarios.

Course Syllabus

Module 1: Advanced Keras Functionalities  

Welcome to the Course
  • Video: Course Introduction
  • Reading: Course Overview
  • Plugin/Reading: Helpful Tips for Course Completion
Advanced Keras Functional API 
  • Video: Introduction to Advanced Keras 
  • Video: Keras Functional API  and Subclassing API
  • Lab: Implementing the Functional API in Keras
  • Practice Quiz: Advanced Keras Functional API 
Custom Layers with Keras
  • Video: Creating Custom Layers in Keras 
  • Video: Overview of TensorFlow 2.x 
  • Lab: Creating Custom Layers and Models 
  • Practice Quiz: Custom Layers with Keras
Advanced Keras Functionalities  Summary
  • Reading: Summary and Highlights: Advanced Keras Functionalities 
  • Reading: Glossary: Advanced Keras Functionalities 
  • Graded Quiz: Advanced Keras Functionalities  
  • Discussion Prompt: Meet and Greet [ ungraded]
Module 2: Advanced CNNs in Keras 

Advanced CNNs and Data Augmentation
  • Video: Advanced CNNs in Keras 
  • Video: Data Augmentation Techniques 
  • Lab: Advanced Data Augmentation with Keras 
  • Practice Quiz: Advanced CNNs and Data Augmentation
Transfer Learning on Pre-trained Models and Image Processing
  • Video: Transfer Learning in Keras 
  • Video: Using Pre-trained Models 
  • Lab: Transfer Learning Implementation 
  • Video: TensorFlow for Image Processing 
  • Practice Quiz: Transfer Learning on Pre-trained Models and Image Processing
Introducing Transpose Convolution
  • Video: Introducing Transpose Convolution 
  • Lab: Practical Application of Transpose Convolution  
  • Practice Quiz: Introducing Transpose Convolution 
Advanced CNNs in Keras Summary
  • Reading:  Summary and Highlights: Advanced CNNs in Keras
  • Reading: Glossary: Advanced CNNs in Keras
  • Graded Quiz: Advanced CNNs in Keras 
  • Discussion Prompt: Data Augmentation and Transfer Learning
Module 3: Transformers in Keras  

Transformers in Keras  
  • Video: Introduction to Transformers in Keras 
  • Video: Building Transformers for Sequential Data 
  • Lab: Building Advanced Transformers   
  • Practice Quiz: Transformers in Keras 
Advanced Transformers and Sequential Data using TensorFlow
  • Video: Advanced Transformer Applications 
  • Video: Transformers for Time Series Prediction 
  • Video: TensorFlow for Sequential Data 
  • Lab: Implementing Transformers for Text Generation
  • Practice Quiz: Advanced Transformers and Sequential Data using TensorFlow 
Transformers in Keras Summary
  • Reading: Summary and Highlight: Transformers in Keras  
  • Reading: Glossary: Transformers in Keras  
  • Graded Quiz: Transformers in Keras  
  • Discussion Prompt: Transforming Sequential Data with Transformers
Module 4: Unsupervised Learning and Generative Models in Keras 

Unsupervised Learning, Autoencoders, and Diffusion Models 
  • Video: Introduction to Unsupervised Learning in Keras 
  • Video: Building Autoencoders in Keras 
  • Lab: Building Autoencoders 
  • Video: Diffusion Models 
  • Lab: Implementing Diffusion Models 
  • Practice Quiz: Unsupervised Learning, Autoencoders, and Diffusion Models 
GANs and TensorFlow
  • Video: Generative Adversarial Networks (GANs) 
  • Video: TensorFlow for Unsupervised Learning 
  • Lab: Develop GANs using Keras 
  • Practice Quiz: GANs and TensorFlow 
Unsupervised Learning and Generative Models in Keras Summary
  • Reading: Summary and Highlight: Unsupervised Learning and Generative Models in Keras
  • Reading: Glossary: Unsupervised Learning and Generative Models in Keras
  • Graded Quiz: Unsupervised Learning and Generative Models in Keras 
  • Discussion Prompt: Exploring Autoencoders and GANs
Module 5: Advanced Keras Techniques 

Advanced Keras techniques and Custom Training Loops  
  • Video: Advanced Keras Techniques 
  • Video: Custom Training Loops in Keras 
  • Lab: Custom Training Loops in Keras 
  • Practice Quiz: Advanced Keras techniques and Custom Training Loops 
Hyperparameter and Model Optimization
  • Video: Hyperparameter Tuning with Keras Tuner 
  • Lab: Hyperparameter Tuning with Keras Tuner 
  • Video: Model Optimization 
  • Video: TensorFlow for Model Optimization 
  • Practice Quiz: Hyperparameter and Model Optimization 
Advanced Keras Techniques Summary
  • Reading: Summary and Highlight: Advanced Keras Techniques 
  • Reading: Glossary: Advanced Keras Techniques 
  • Graded Quiz: Advanced Keras Techniques and Custom Training Loops
  • Discussion Prompt: Custom Training Loops and Hyperparameter Optimization
Module 6: Introduction to Reinforcement Learning with Keras 

Reinforcement Learning, Q-Learning, Q-Networks (DQNs) 
  • Video: Introduction to Reinforcement Learning
  • Video: Q-Learning with Keras 
  • Lab: Implementing Q-Learning in Keras 
  • Video: Deep Q-Networks (DQNs) with Keras 
  • Lab: Building a Deep Q-Network with Keras
  • Practice Quiz: Reinforcement Learning, Q-Learning, Q-Networks (DQNs) 
Module Summary
  • Reading: Summary and Highlight: Introduction to Reinforcement Learning with Keras
  • Reading: Glossary: Introduction to Reinforcement Learning with Keras
  • Graded Quiz: Introduction to Reinforcement Learning with Keras  
  • Discussion Prompt: The Promise and Challenge of Reinforcement Learning
Module 7: Final Project and Assignment
  • Reading: Practice Project Overview: Fruit Classification Using Transfer Learning
  • Lab:Practice Project: Fruit Classification Using Transfer Learning
  • Reading: Final Project: Classify Waste Products Using Transfer Learning
  • Final Project:  Classify Waste Products Using Transfer Learning
  • Project: Peer-graded Assignment:Classify Waste Products Using Transfer Learning
Course Wrap Up
  • Video: Course Wrap-up 
  • Reading: Congratulations and Next Steps
  • Reading: Thanks from the Course Team

General Information

  • This course is self-paced. 
  • This platform works best with current versions of Chrome, Edge, Firefox, Internet Explorer, or Safari. 

Recommended Skills Prior to Taking this Course

The following skills are required to be successful in this course: 
  • Working knowledge of Python.
  • Machine Learning with Python
  • Fundamentals of Deep Learning with Keras