What you'll learn

Deep Learning using Keras and TensorFlow
Artificial Neural Networks
How Artificial Neural Networks Work
How Artificial Neural Networks Learn
Loss Functions used in Artificial Neural Networks
Activation Functions used in Artificial Neural Networks
Cost Functions used in Artificial Neural Networks
Optimizer Functions used in Artificial Neural Networks
What Backpropagation is
Different Types of Gradient Descent
How to Choose an Activation Function
Preparing your Data for Deep Learning Models
Monitoring Loss Functions
Monitoring Model Metrics
Use of CallBacks in Deep Learning
Fighting overfitting in TensorFlow
Convolutional Neural Networks

Requirements

A little Python programming experience wouldn't hurt

Description

In this course, you'll learn how to use Google TensorFlow to build your own deep learning models. We'll take you from the basics of artificial neural networks and later show you how to build them using Keras and TensorFlow. We'll walk through practical examples so that you can get a hand-on experience at working with TensorFlow and Keras.

Who this course is for:

Individual who would like to learn about deep learning

Citación :

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