Deep Learning: Artificial Neural Networks with Tensorflow 2

In this self-paced course, you will learn how to use Tensorflow 2 to build deep neural networks. You'll learn the basics of machine learning, classification, and regression. We will also discuss the connection between artificial neural networks and biological neural networks and how that inspires our thinking in the field of deep learning. The course includes video presentations, coding lessons, hands-on exercises, and links to further resources.

This course is intended for:

• Anyone interested in deep learning and machine learning
• Anyone who wants to implement deep neural networks in Tensorflow 2
• Anyone interested in building a foundation for convolutional neural networks, recurrent neural networks, LSTMs, and transformers

Suggested prerequisites:

• Decent Python programming skill
• Comfortable with data science libraries like Numpy and Matplotlib

In this course, we will cover:

• what machine learning is
• classification and regression
• application to many real-world datasets
• how to build linear models with Tensorflow 2
• how to build deep neural networks with Tensorflow 2
• how neural networks learn
• loss functions like the mean-squared error and cross-entropy loss
• stochastic gradient descent, momentum, and adam optimization