• Part I: Introducción

  • Basic Variables
  • Basic Operations

• Part II: Machine Learning

  • Lineal Regression
  • Logistic Regression
  • Neural Networks

• Part III: Deep Learning for Classification

  • Convolutional Neural Networks
  • CNNs Arquitectures
  • Transfer Learning
  • CNN using custom data
  • Basic RNN (LSTM)

• Part IV: Deep Learning for Object Detection

  • Faster RCNN using Pre-trained Model

• Part V: Deep Learning for Pose Estimation

  • Body Estimation using pre-trained model

Note: Use environment.yml to download libraries in order to run every notebook.