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Deep Learning Capstone Project

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Demonstrate your mastery in deep learning with PyTorch and Keras. Compare models, use pre-trained models, train new models, and optimize machine learning workflows with practical hands-on labs.

Language

  • English

Topic

  • Deep Learning

Skills You Will Learn

  • Keras, PyTorch, Train And Optimize Models, Deep Learning, Applied Machine Learning, Artificial Neural Networks

Offered By

  • IBMSkillsNetwork

Estimated Effort

  • 16 Hours

Platform

  • SkillsNetwork

Last Update

  • June 18, 2025
About this Course

About this course

In this capstone project course you will apply your deep learning skills and demonstrate your mastery with deep neural networks.
 
You’ll begin by loading data in PyTorch and Keras. You will explore the various methods and tools to efficiently handle datasets in both libraries, providing a foundation for model development.
 
Next, the course covers data preprocessing. You’ll prepare images and other data for model training in both PyTorch and Keras. Through labs, you will gain experience in applying different preprocessing techniques such as scaling, normalization, and augmentation.
 
The course then focuses on model training. You’ll apply the concept of pre-trained models and how to use them in both PyTorch and Keras. By participating in hands-on labs and peer reviews, you will develop a deeper understanding of model training, fine-tuning, and optimization.
 
Once familiar with the training process, you will compare the performance of models built with PyTorch and Keras. In this section, you will analyze and evaluate the two frameworks, and assess the advantages and challenges of each through comparative exercises.
 
Finally, the course concludes with a section dedicated to assessing the models you’ve trained. By using pre-trained models and comparing them to custom-built models, you’ll refine your evaluation techniques, gaining insight into the strengths and weaknesses of different model architectures.

What you will learn: 

After completing this course, you will be able to: 
  • Demonstrate mastery in deep learning.
  • Describe the process of loading data into PyTorch and Keras, and the methods available for preprocessing data.
  • Apply data loading and preprocessing techniques in PyTorch and Keras to prepare datasets for machine learning tasks.
  • Analyze and compare the performance of models trained in PyTorch and Keras, evaluating the impact of different architectures and preprocessing methods.
  • Evaluate pre-trained models and assess their performance for real-world machine learning tasks.
  • Create workflows to train, compare, and assess models using PyTorch and Keras.

Course Syllabus

Chapter 1 - Loading Data

Getting Started
  • Getting Started
  • Learning Objectives
1.1 Loading Data - PyTorch
  • Video: Loading Data - PyTorch
  • Lab: Loading Data - PyTorch
  • Quiz: Loading Data - PyTorch
1.2 Loading Data - Keras
  • Video: Loading Data -Keras
  • Lab: Loading Data - Keras
  • Quiz: Loading Data - Keras

Chapter 2 - Data Preparation
  • Learning Objectives
2.1 Processing Data - PyTorch
  • Video: Image Preprocessing PyTorch
  • Lab: Processing Data - PyTorch
  • Quiz: Processing Data - PyTorch
2.2 Processing Data - Keras
  • Video: Processing Data - Keras
  • Lab: Processing Data - Keras
  • Quiz: Processing Data - Keras

Chapter 3 - Assessment Training Model
  • Learning Objectives
3.1 Training Models - PyTorch
  • Video: Pre-trained Models PyTorch
  • Lab: Training Model - PyTorch
  • Peer Review: Training Models - PyTorch
3.2 Training Models - Keras
  • Video: Pre-trained Models in Keras
  • Lab: Training Models - Keras
  • Peer Review: Training Models - Keras

Chapter 4 - Compare Two Models
  • Learning Objectives
4.1 Compare Two Models - PyTorch
  • Instruction: Compare Two Models - PyTorch
  • Lab: Compare Two Models - PyTorch
  • Quiz: Compare Two Models - PyTorch
4.2 Compare Two Models - Keras
  • Instruction: Compare Two Models - Keras
  • Lab: Compare Two Models - Keras
  • Quiz: Compare Two Models - Keras
Course Rating and Feedback
  • Course Rating and Feedback
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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 with this course: 
  • High School level Mathematics
  • Working knowledge of Python.
  • Machine Learning with Python
  • Fundamentals of Deep Learning
  • Working knowledge of PyTorch
  • Working knowledge of Keras/Tensorflow

Instructors

Joseph Santarcangelo

Senior Data Scientist at IBM

Joseph has a Ph.D. in Electrical Engineering, his research focused on using machine learning, signal processing, and computer vision to determine how videos impact human cognition. Joseph has been working for IBM since he completed his PhD.

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