You're going to learn the most popular library to build networks and machine learning algorithms.

In this hands-on, practical course, you will be working your way through with Python, Tensorflow, and Jupyter notebooks.

What you will learn:

• Basics of Tensorflow

• Artificial Neurons

• Feed Forward Neural Networks

• Activations and Softmax Output

• Gradient Descent

• Backpropagation

• Loss Function

• MSE

• Model Optimization

• Cross-Entropy

• Linear Regression

• Logistic Regression

• Convolutional Neural Networks (with examples)

• Text and Sequence Data

• Recurrent Neural Networks (with examples)

• Neural Style Transfer (in progress)