Overview

This course introduces deep learning and neural networks with the Keras library. In this course, you’ll be equipped with foundational knowledge and practical skills to build and evaluate deep learning models. You’ll begin this course by gaining foundational knowledge of neural networks, including forward and backpropagation, gradient descent, and activation functions. You will explore the challenges of deep network training, such as the vanishing gradient problem, and learn how to overcome them using techniques like careful activation function selection. The hands-on labs in this course allow you to build regression and classification models, dive into advanced architectures, such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), transformers, and autoencoders, and utilize pretrained models for enhanced performance. The course culminates in a final project where you’ll apply what you’ve learned to create a model that classifies images and generates captions. By the end of the course, you’ll be able to design, implement, and evaluate a variety of deep learning models and be prepared to take your next steps in the field of machine learning.

Syllabus

Introduction to Deep Learning and Neural Networks

In this module, you will explore the foundational concepts of deep learning and neural networks using Keras. This module introduces you to the significance and applications of deep learning. You’ll delve into the structure and function of neurons and neural networks. Further, you’ll explore artificial neural networks, detailing their architecture and operation. Finally, you’ll evaluate the forward propagation process, understanding how data moves through a network to produce outputs. Additionally, you’ll gain a comprehensive understanding of how deep learning models are constructed and function.

Basics of Deep Learning

In this module, you’ll delve into the core mechanisms of neural networks. You'll explain how models optimize gradient descent algorithms and explore backpropagation. Further, you’ll demonstrate how to address challenges using the vanishing gradient problem. Finally, this module introduces you to the activation functions as solutions. Through hands-on exercises, you’ll observe how different activation functions impact learning, equipping you with the knowledge to design and train effective deep learning models.

Keras and Deep Learning Libraries

In this module, you will explore the applications of deep learning using the Keras library. You’ll also gain insights into the role of Keras and other deep learning libraries in model development. This module guides you through building and training regression and classification models using Keras. The hands-on labs in this module provide real-world datasets to implement and evaluate deep learning models for various predictive tasks.

Deep Learning Models

In this module, you’ll delve into advanced deep learning architectures and techniques using the Keras library. You’ll distinguish between shallow and deep neural networks, understanding their respective complexities and applications. You’ll also explore convolutional neural networks (CNNs) for image processing tasks and gain guidance for implementing CNNs using Keras. You’ll explore recurrent neural networks (RNNs) for sequential data and transformer models that have revolutionized natural language processing (NLP). Additionally, you’ll explore autoencoders for unsupervised learning and pretrained models to enhance performance and reduce training time. The hands-on labs in this module provide you with a practical understanding of various deep learning models and transformers in Keras.

Final Project and Course Wrap-Up

In this final module, you will apply and demonstrate the full range of skills you have gained throughout the course. In this module, you will consolidate your learning through a final project integrating core deep learning concepts such as image classification and caption generation using Keras. After completing the project, you will reflect on your journey through the course and understand the next steps for continued growth in deep learning.