Data Science Notebooks

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.

Table of Contents

• 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

How to Use this Repo

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

About

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.

Software

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.

Deep Learning

Projects

Notebook	Description
Deep Painterly Harmonization	Implement Deep Painterly Harmonization paper in PyTorch
Language modelling in Malay language for downstream NLP tasks	Implement Universal Language Model Fine-tuning for Text Classification (ULMFiT) in PyTorch
Not Hotdog AI Camera mobile app	Asia virtual study group project for fast.ai deep learning part 1, v3 course. Ship a convolutional neural network on Android/iOS with PyTorch and Android Studio/Xcode

Language Models

Notebooks for trying out transformer and large language models.

Notebook	Description
Flan-UL2 20B	Flan 20B with UL2 code walkthrough. This shows how you can get it running on 1x A100 40GB GPU with the HuggingFace library and using 8-bit inference. Using CoT, zeroshot (logical reasoning, story writing, common sense reasoning, speech writing). Testing large (2048) token input.

DL Assignments, Exercises or Course Works

fast.ai's Deep Learning Part 1: Practical Deep Learning for Coders 2018 (v2): Oct - Dec 2017

Notebook	Description
lesson1, lesson1-vgg, lesson1-rxt50, keras_lesson1	Lesson 1 - Recognizing Cats and Dogs
lesson2-image_models	Lesson 2 - Improving Your Image Classifier
lesson3-rossman	Lesson 3 - Understanding Convolutions
lesson4-imdb	Lesson 4 - Structured Time Series and Language Models
lesson5-movielens	Lesson 5 - Collaborative Filtering; Inside the Training Loop
lesson6-rnn, lesson6-sgd	Lesson 6 - Interpreting Embeddings; RNNs from Scratch
lesson7-cifar10, lesson7-CAM	Lesson 7 - ResNets from Scratch

fast.ai's Deep Learning Part 1: Practical Deep Learning for Coders 2019 (v3): Oct - Dec 2018

Deep Learning Part 1: 2019 Edition

Notebook	Description
00_notebook_tutorial.ipynb, lesson1-pets.ipynb	Lesson 1 - Image Recognition
lesson2-download.ipynb lesson2-sgd.ipynb	Lesson 2 - Computer Vision: Deeper Applications
lesson3-planet.ipynb lesson3-camvid.ipynb lesson3-head-pose.ipynb lesson3-imdb.ipynb	Lesson 3 - Multi-label, Segmentation, Image Regression, and More
lesson4-tabular.ipynb lesson4-collab.ipynb	Lesson 4 - NLP, Tabular, and Collaborative Filtering

fast.ai's Deep Learning Part 2: Cutting Edge Deep Learning f