TensorFlow

This repository contains Jupyter notebooks implementing AI, Machine Learning and Deep Learning algorithms in pure TensorFlow to solve different problems. The implementation follows some similar patterns as Keras and other high-level libraries. The key ingredients are:

• Layer
• Loss
• Metric
• Optimizer
• Callback
• Model

The notebooks use Tensorflow 2.1.

Table of contents

• 1 - Linear Regression
• 2 - Logistic Regression
• 3 - Polynomial Regression
• 4 - k-Nearest Neighbors
• 5 - Support Vector Machines
• 6 - Linear Discriminant Analysis
• 7 - Radial Basis Functions
• 8 - Neural Networks
• 9 - Regularization
• 10 - Dropout
• 11 - Bagging
• 12 - Boosting
• 13 - Recommender Systems
• 14 - k-Means
• 15 - Principal Component Analysis