Deep Learning Fundamentals with Keras (edX)

You will learn about some of the exciting applications of deep learning, the basics fo neural networks, different deep learning models, and how to build your first deep learning model using the easy yet powerful library Keras.
This course will present simplified explanations to some of today's hottest topics in data science, including:

• What is deep learning?
• How do neural networks learn and what are activation functions?
• What are deep learning libraries and how do they compare to one another?
• What are supervised and unsupervised deep learning models?
• How to use Keras to build, train, and test deep learning models?

The demand for deep learning skills-- and the job salaries of deep learning practitioners -- are continuing to grow, as AI becomes more pervasive in our societies. This course will help you build the knowledge you need to future-proof your career.
This course is part of the Deep Learning Professional Certificate.

What you'll learn

• You will learn about exciting applications of deep learning and why it is really rewarding to learn how to leverage deep learning skills.
• You will learn about neural networks and how they learn and update their weights and biases.
• You will learn about the vanishing gradient problem.
• You will learn about building a regression model using the Keras library.
• You will learn about building a classification model using the Keras library.
• You will learn about supervised deep learning models, such as convolutional neural networks and recurrent neural networks, and how to build a convolutional neural network using the Keras library.
• You will learn about unsupervised learning models such as autoencoders.

Syllabus

Module1 - Introduction to Deep Learning

• Introduction to Deep Learning
• Biological Neural Networks
• Artificial Neural Networks - Forward Propagation

Module2 -Artificial Neural Networks

• Gradient Descent
• Backpropagation
• Vanishing Gradient
• Activation Functions

Module3 - Deep Learning Libraries

• Introduction to Deep Learning Libraries
• Regression Models with Keras
• Classification Models with Keras

Module4 -Deep Learning Models

• Shallow and Deep Neural Networks
• Convolutional Neural Networks
• Recurrent Neural Networks
• Autoencoders

Prerequisites

• Python Programming. For example, you can complete this course on edX: Python Basics for Data Science.
• Machine Learning with Python. For example, you can complete this course on edX: Machine Learning with Python: A Practical Introduction.
• Partial Derivatives.