Learning machine learning

In this repository will be added exercises, practices and theory about machine learning and deep learning.

For a better understanding, the Jupyter environment will be used, which allows step-by-step execution of the program by adding theory.

• Machine Learning.
• Deep Learning.
• Reinforcement Learning.
• Computer Vision
• Natural Language Processing

The library

A library of the library:

150 repositories about Machine learning, NLP, and more

Machine Learning

• Basic notebooks.
• Basic notebooks II.
• Basic Libraries for Data Science.
• Tutorial Sklearn (Spanish).
• Basic Sklearn
• Learn machine learning concepts in two hours (highly recommended). Jason Mayes.

Deep Learning

• FastAI Book. Jeremy Howard Deep Learning Book.
• FastAI book 2
• Dive into Deep Learning

Computer Vision

• Learn OpenCV.
• Face Recognition System.
• How a CNN works

Reinforcement Learning

• Basic Reinforcement Learning.
• Packt Hands-on (PyTorch).

Natural Language Processing

• NLP in Python using DeepLearning 1.
• NLP PyLadies
• Different resources about NLP using PyTorch.
• NLP Notebooks
• Best similarity algorithms

Jetson Nano

• Getting started in AI and computer vision.
• Using PiCam in JetsonNano.
• FaceNet (PyTorch).

Utils

Matplotlib

• Matplotlib simple guide

• Awesome Python Applications.

• Thousand of projects in Python

• Dataset search engine

Installation

Jupyter Notebook

If you have Python 3 installed (which is recommended):

python3 -m pip install --upgrade pip
python3 -m pip install jupyter

To run Jupyter Lab, type:

pip3 install jupyter lab
jupyter lab

To run the notebook, run the following command at the Terminal (Mac/Linux) or Command Prompt (Windows):

jupyter notebook

References

• Jupyter site.
• TensorFlow.
  • TensorFlow other platforms.
• Keras Documentation.
• Spanish Documentation to install.