Keras for Machine Learning Online Course
Keras for Machine Learning Online Course
This course is designed to provide a comprehensive introduction to deep learning with Python using Keras. You will learn how to develop and evaluate deep learning models using the Keras library, which simplifies the complexity of TensorFlow and Theano. Keras, a popular tool for rapid prototyping, will be your guide as you build your own neural network models. This hands-on course is structured as both a playbook and workbook, allowing you to learn by doing and apply your knowledge to real-world deep learning projects.
Key Benefits
- Gain expertise in utilizing advanced techniques to develop cutting-edge deep learning models.
- Master advanced image augmentation methods to significantly improve model performance.
- Learn to optimize model efficiency by implementing learning rate schedules for enhanced results.
Target Audience
This course is designed for developers, machine learning engineers, and data scientists who want to leverage the capabilities of the Keras library fully. While prior expertise in machine learning is not required, a basic understanding of small machine learning tasks using SciKit-Learn will be beneficial. Key concepts, such as cross-validation and one-hot encoding, are introduced and briefly explained within the context of lessons and projects. Overall, this course provides a solid introduction to the Keras library, making it suitable for beginners in the field.
Learning Objectives
- Develop and evaluate neural network models from start to finish, covering all stages of model development.
- Construct larger and more complex models for working with image and text data.
- Gain a comprehensive understanding of the structure and components of a Keras model.
- Assess the performance of deep learning models created with Keras, utilizing relevant metrics and techniques.
- Design and implement end-to-end regression and classification models in Keras.
- Master the use of checkpointing to save and restore the best model iterations during training for optimal performance.
Course Outline
The Keras for Machine Learning Exam covers the following topics -
Module 1 - Overview
- Introduction to Keras
Module 2 - Core Concepts
- Theano Overview
- Understanding TensorFlow
- Anatomy of an Artificial Neural Network
- Introduction to Deep Learning
- Understanding the Keras Workflow
- Breakdown of Keras Code Structure
- Demonstration: Pima Indian Diabetes Dataset – Data Loading
- Demonstration: Pima Indian Diabetes Dataset – Model Definition and Compilation
- Demonstration: Pima Indian Diabetes Dataset – Model Fitting and Evaluation
- Neural Network Performance Evaluation
- Demonstration: Data Segmentation Case Study
- Utilizing Scikit-Learn for General Machine Learning
- Evaluating Models Using Cross-Validation
- Hyperparameter Tuning with Grid Search in Deep Learning
- Demonstration: Multiclass Classification Case Study
- Demonstration: Multiclass Classification – Part 2
- Demonstration: Binary Classification Case Study
- Demonstration: Binary Classification – Part 2
- Demonstration: Binary Classification – Part 3
- Demonstration: Binary Classification – Part 4
- Demonstration: Regression Case Study
- Demonstration: Regression – Part 2
- Demonstration: Regression – Part 3
Module 3 - Advanced Techniques in Keras
- Model Serialization Process
- Saving Neural Networks in JSON Format
- Saving Neural Networks in YAML Format
- Demonstration: Checkpointing Case Study
- Demonstration: Checkpointing – Part 2
- Plotting and Visualizing Training History
- Demonstration: Visualizing Model Training History in Keras
- Demonstration: Implementing Dropout in Models
- Demonstration: Dropout – Part 2
- Tips for Efficient Dropout
- Defining Learning Rates
- Configuring Learning Rate Adjustments
- Demonstration: Learning Rate Case Study
- Demonstration: Learning Rates – Part 2
- Demonstration: Learning Rates – Part 3
Module 4 - Convolutional Neural Networks (CNNs)
- Introduction to Convolutional Neural Networks
- Demonstration: Handwritten Digit Recognition Case Study
- Demonstration: Handwritten Digit Recognition – Part 2
- Demonstration: Handwritten Digit Recognition – Part 3
- Demonstration: Handwritten Digit Recognition – Part 4
- Implementing Image Augmentation
- Demonstration: Image Augmentation Case Study
- Demonstration: Image Augmentation – Part 2
- Tips for Effective Image Augmentation
- Object Recognition in Keras
- Demonstration: Object Recognition Case Study
- Techniques for Enhancing Model Performance
- Sentiment Analysis with Keras
- Understanding IMDB Dataset Features
- Overview of Word Embedding Concepts
- Demonstration: Word Embedding Case Study
- Demonstration: Word Embedding – Part 2
Module 5 - Recurrent Neural Networks (RNNs)
- Introduction to Recurrent Neural Networks
- Demonstration: Time Series Prediction Case Study
- Demonstration: Time Series Prediction – Part 2
- Demonstration: Time Series Prediction – Part 3
- Demonstration: Time Series Prediction with LSTM Networks
- Demonstration: Time Series Prediction with LSTM – Part 2
- Demonstration: Time Series Prediction with LSTM – Part 3
- Demonstration: Sequence Classification Case Study
- Demonstration: Sequence Classification – Part 2
