gjogonzalezc/datacamp-deep-learning

DataCamp's Deep Learning in Python 🐍 & Keras

Tools :

• Python 3.5
• Jupyter Notebook
• Keras (with Tensorflow Backend)
• Scipy
• Numpy
• Matplotlib

Chatper 1 - Basics of deep learning and neural networks

In this chapter, you'll become familiar with the fundamental concepts and terminology used in deep learning, and understand why deep learning techniques are so powerful today. You'll build simple neural networks yourself and generate predictions with them.

Chapter 2 - Optimizing a neural network with backward propagation

Here, you'll learn how to optimize the predictions generated by your neural networks. You'll do this using a method called backward propagation, which is one of the most important techniques in deep learning. Understanding how it works will give you a strong foundation to build from in the second half of the course.

Chapter 3 - Building deep learning models with keras

In this chapter, you'll use the keras library to build deep learning models for both regression as well as classification! You'll learn about the Specify-Compile-Fit workflow that you can use to make predictions and by the end of this chapter, you'll have all the tools necessary to build deep neural networks!

Chapter 4 - Fine-tuning keras models

Here, you'll learn how to optimize your deep learning models in keras. You'll learn how to validate your models, understand the concept of model capacity, and experiment with wider and deeper networks. Enjoy!