Data science Python notebooks—a collection of Jupyter notebooks on machine learning, deep learning, statistical inference, data analysis and visualization.

This repo contains various Python Jupyter notebooks I have created to experiment and learn with the core libraries essential for working with data in Python and work through exercises, assignments, course works, and explore subjects that I find interesting such as machine learning and deep learning. Familiarity with Python as a language is assumed.

The essential core libraries that I will be focusing on for working with data are NumPy, Pandas, Matplotlib, PyTorch, TensorFlow, Keras, Caffe, scikit-learn, spaCy, NLTK, Gensim, and related packages.

• Data Science Notebooks
  • Table of Contents
  • How to Use this Repo
  • About
  • Software
  • Deep Learning
    • Projects
    • DL Assignments, Exercises or Course Works
      • fast.ai's Deep Learning Part 1: Practical Deep Learning for Coders 2018 (v2): Oct - Dec 2017
      • fast.ai's Deep Learning Part 1: Practical Deep Learning for Coders 2019 (v3): Oct - Dec 2018
      • fast.ai's Deep Learning Part 2: Cutting Edge Deep Learning for Coders 2017 (v1): Feb - Apr 2017
      • fast.ai's Deep Learning Part 2: Cutting Edge Deep Learning for Coders 2018 (v2): Mar - May 2018
  • Machine Learning
    • ML Assignments, Exercises or Course Works
      • Andrew Ng's "Machine Learning" class on Coursera
      • fast.ai's machine learning course
  • Libraries or Frameworks
    • NumPy
    • PyTorch
    • TensorFlow
    • Keras
    • Pandas
    • Matplotlib
  • Kaggle Competitions
  • License
    • Code
    • Text

• Run the code using the Jupyter notebooks available in this repository's notebooks directory.
• Launch a live notebook server with these notebooks using binder:

The notebooks were written and tested with Python 3.6, though other Python versions (including Python 3.x) should work in nearly all cases.

See index.ipynb for an index of the notebooks available.

The code in the notebook was tested with Python 3.6, though most (but not all) will also work correctly with Python 3.x.

The packages I used to run the code in the notebook are listed in requirements.txt (Note that some of these exact version numbers may not be available on your platform: you may have to tweak them for your own use). To install the requirements using conda, run the following at the command-line:

$ conda install --file requirements.txt

To create a stand-alone environment named DSN with Python 3.6 and all the required package versions, run the following:

$ conda create -n DSN python=3.5 --file requirements.txt

You can read more about using conda environments in the Managing Environments section of the conda documentation.

Notebooks for trying out transformer and large language models.

Deep Learning Part 1: 2019 Edition

Deep Learning Part 2: 2017 Edition

Deep Learning Part 2: 2018 Edition

• Exercise 1 - Linear Regression
• Exercise 2 - Logistic Regression

• Lesson 1 - Random Forest
• Lesson 2 - Random Forest Interpretation
• Lesson 3 - Random Forest Foundations
• Lesson 4 - MNIST SGD
• Lesson 5 - Natural Language Processing (NLP)

WIP

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This repository contains a variety of content; some developed by Cedric Chee, and some from third-parties. The third-party content is distributed under the license provided by those parties.

I am providing code and resources in this repository to you under an open source license. Because this is my personal repository, the license you receive to my code and resources is from me and not my employer.

The content developed by Cedric Chee is distributed under the following license:

The code in this repository, including all code samples in the notebooks listed above, is released under the MIT license. Read more at the Open Source Initiative.

The text content of the book is released under the CC-BY-NC-ND license. Read more at Creative Commons.