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Description

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)