Frank's Jupyter Notebooks

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Below is a list of jupyter notebooks that I created using nbconvert, my markdown to HTML system , and using highlight.js . I created this to keep track of notes on machine learning primarily - it is much quicker to upload a jupyter notebook, convert it to html, and edit the resulting html a little than to copy all the output of each cell in a jupyter notebook, put it into the right format, copy and convert the python code, and copy and convert the markdown.

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Note: In the Hands On Machine Learning notes below, unless encapsulated in summation notion, the superscript ⁱ usually refers to an instance, not to an exponent. The conventional notation is to wrap the i in parentheses, ⁽ⁱ⁾, but I sometimes failed to do that.

Jupyter Notebooks

October, 2024

Reading the XGBoost Docs

I wanted to familiarize myself with xgboost because I read that it is used to win a lot of Kaggle competitions that involve tabular data.

Reading the Pillow Docs

I just launched a Flask server where I used the Python Image Library to do some stuff, and I had some trouble initially getting it to work correctly. I quickly read the docs and took some notes so I have a better idea of how the library works / what ...

Neural Style Transfer

Going through some TensorFlow tutorials on style transfer to prepare to deploy it on a newly created Flask backend.

September, 2024

Deep Reinforcement Learning Hands-On Ch1-5

I started reading Deep Reinforcement Learning Hands-On by Maxim Lapan. These first 5 chapters go introduce Reinforcement Learning, the Cross-Entropy method, and the Bellman Equation.

Hands-On Machine Learning Chapter 18 - Reinforcement Learning

This chapter introduces and goes over some reinforcement learning concepts. I am going to read a book on RL next, so I just skimmed this chapter.

Hands On Machine Learning Chapter 16/17 - NLP with RNN and Attention / Autoencoder, GAN, Diffusion

These chapters goes over NLP (text generation, classification), introduces attention mechanism and the transformer model, and retviews Autoencoders, Generative Adverserial Networks, and Diffusion models for image generation.

Hands-On Machine Learning Chapter 15 - Processing Sequences Using RNNs and CNNs

This chapter goes over processing sequence data with recurrent neural networks and convolutional neural networks - it introduces LSTMs, GRUs, simple RNNs and explains them all.

Question Answering, Making Transformers Efficient, Dealing with Few Labels, and Future Directions

These last Chapters of Natural Language Processing with Transformers go over question answering concerns, making transformers efficient through weight pruning and quantization, methods for dealing with few labels, and review some future direction abo...

Natural Language Processing w/ Transformers: Text Generation and Text Summarization

These two chapters in Natural Language Processing with Transformers go over text generation / how to best search for the next best word and text summairzation / how to evaluate text generation/summarization models.

Natural Language Processing w/ Transformers: Transformer Anatomy and Multilingual NER

Chapters 3 and 4 of Natural Language Processing with Transformers goes into depth on the architecture of the Transformer and walks through a multilingual Named Entity Recognition task.

Natural Language Processing w/ Transformers: Hello Transformers and Text Classification

The first two chapters of Natural Language Processing with Transformers introduce the Transformer architecture with some history and surface-level explanation of transformers and walks though a text classification example of utilizing transformers wi...

Hugging Face NLP Course Chapters 1-2

I am taking the Hugging Face NLP Course becuase it is considered a prerequisite for "Natural Language Processing with Transformers". These first two chapters go over the basics of Hugging Face APIs used for natural language processing.

Generative Deep Learning - Compose, Play, and The Future of Generative Modeling

The last three chapters of Generative Deep Learning go over encoder-decoder networks, reinforcement learning, and the future of generative modeling (mainly the Transformer) respectively.

Generative Deep Learning - Paint and Write

Chapters 5 and 6 of Generative Deep Learning go into style transfer for images - transforming a base image to have the style of a style image - and text generation using RNNs and LSTM networks.

Generative Deep Learning - Variational Autoencoders and Generative Adverserial Networks

Chapters 3 and 4 of Generative Deep Learning review Variational Autoencoders and Generative Adverserial Networks and how these deep learning architectures can be used for image generation.

Generative Deep Learning - Generative Modeling and Deep Learning

Jupyter notebook for chapters 1 and 2 of Generative Deep Learning by David Foster. These first two chapters go over some generative modeling terminology and give a quick overview of deep learning - Dense Layers, CNNs, etc.

Natural Langauge Processing with PyTorch Chapters 1-4

I decidied to read this textbook to become more familiar with PyTorch. This has ended up being a review of Machine Learning / Deep Learning concepts mainly (I don't like the way the code is presented in the book). These forst 4 chapters go through so...

The Python Tutorial

I wanted to go through the Python Tutorial to review base Python. In this notebook, I mainly took notes on things I wanted to review or things I wanted to get a clearer defintion of.

Introduction to PyTorch

After reading Chapter 1 of Natural Language Processing with PyTorch, I decided that I wanted to learn more about the library before continuing reading the rest of the book. I read through the Introduction to PyTorch and Learning PyTorch tutorials on ...

Deep Learning with Python - Chapters 12 and 13

"Generative Deep Learning" and "Best Practices for the Real World" go over text generation and image generation algorithms and best practices for Deep Learning in the real world.

Deep Learning with Python - Chapter 11

The Chapter "Deep Learning for Text" goes over bag of words models and sequence models for text classification and sequence to sequence tasks. The chapter reviews the Transformer architecture, neural attention, and word embeddings.

Deep Learning with Python - Chapters 9 and 10

"Advanced Deep Learning for Computer Vision" and "Deep Learning for Timerseries" go over image segmentation and recurrent neural networks (primarily in the context of timeseries data), respectively.

Deep Learning with Python - Chapters 7 and 8

The chapters "Working with Keras: A Deep Dive" and "Introduction to Deep Learning for Computer Vision" go over the Keras library somewhat in-depth and give an introduction to convolutional neural nets for image classification respectively.

Deep Learning with Python - Chapters 5 and 6

The "Fundamentals of Machine Learning" and "Universal Workflow of Machine Learning" chapters go over many things to keep in mind when doing a machine learning project - both when building the model and when preparing data/deploying model.

Deep Learning with Python Chapters 3 and 4

These chapters give an "Introduction to Keras and TensorFlow" and an introduction to Classification and Regresison with Keras.

Deep Learning with Python - Chapters 1 and 2

The first two chapters go over what is deep learning / the mathematical building blocks of neural networks.

Deep Computer Vision Using Convolutional Neural Networks Exercises Answers

This chpater goes into what CNNs are and reviews some CNN architectures.

Training Deep Neural Networks Exercises Answers

This chapter goes over the considerations training deep neural networks with Keras.

Introduction to Artificial Neural Networks with Keras Exercises Answers

This chapter introduces artificial neural networks and introduces Keras.

Dimensionality Reduction Exercises Answers

This chpapter goes over dimensionality reduction for training models and visualization.

Ensemble Learning and Random Forests Exercises Answers

This chapter goes over Bagging Classifiers, Voting Classifiers, Random Forests, Extra-Trees, and Boosting algorithms.

Decision Trees Exercises Answers

This chapter goes over decision trees and their considerations.

Support Vector Machines Exercises Answers

This chapter goes into Support Vector Classification and Regression.

Training Models Exercises Answers

This chapter goes into various graident descent algoirthms and various linear regression algorithms. It discusses polynomial regression and regularization as well.

Classification Exercises Answers

These exercises involve training classifiers on the MNIST, titanic, and span/ham data sets. I learned a good bit from this.

August, 2024

End-to-End Machine Learning Project Exercises Answers

I don't think these answers are the best - I had a hard time reviewing feature engineering, but I think I learned it pretty well now. I trained a lot of different models on the California Housing Dataset because I was getting frustrated by how by the...

The Machine Learning Landscape Exercises Answers

Going through the exercises in Hands-On Machine Learning to improve machine learning knowledge.

Scikit-Learn Docs

Wanted to learn more about scikit-learn before continuing to learn about machine learning. These notes aren't comprehensive, and I should return to the docs to take more detailed notes on available classes/functions eventually.

Scikit-Image Docs

I wanted to explore the scikit-image docs to see what this library has to offer in the ways of image processing.

Matplotlib Review

I wanted to review Matplotlib before continuing learning about Machine Learning. There were some code snippets in Hands On Machine Learning that I didn't understand well do to my lack of knowledge about Matplotlib.

Pandas Review

I wanted to review Pandas before continuing learning about Machine Learning. There were some code snippets in Hands On Machine Learning that I didn't understand well do to my lack of knowledge about pandas - mainly indexing.

NumPy Review

I wanted to review NumPy before continuing learning about Machine Learning. There were some code snippets in Hands On Machine Learning that I didn't understand well do to my lack of knowledge about numpy.

Hands On Machine Learning Chapter 14 - Deep Computer Vision using Convolutional Neural Networks

I am going to re-read Hands-On Machine Learning with Scikit-learn Keras & TensorFlow because I don't feel that I got a good grasp of machine learning the first time I read it, and I skipped neural networks the first time I read the book. Since the fi...

Hands On Machine Learning Chapter 13 - Loading and Preprocessing Data with TensorFlow

Hands On Machine Learning Chapter 12 - Custom Models and Training with TensorFlow

Hands On Machine Learning Chapter 11 - Training Deep Neural Networks

Hands On Machine Learning Chapter 10 - Introduction to Neural Networks with Keras

Hands On Machine Learning Chapter 9 - Unsupervised Learning Techniques

Hands On Machine Learning Chapter 5 - Support Vector Machines

Hands On Machine Learning Chapter 8 - Dimensionality Reduction

Hands On Machine Learning Chapter 7 - Ensemble Learning and Random Forests

Hands-On Machine Learning with Scikit Learn and Tensor Flow Chapter 6 - Decision Trees

Hands On Machine Learning Chapter 2 - End To End Machine Learning Project

Hands On Machine Learning Chapter 4 - Training Models

Hands On Machine Learning Chapter 3 - Classification

Hands On Machine Learning Chapter 1

May, 2024

Titanic Dataset

Notebook from the Titanic - Machine Learning from Disaster challenge on Kaggle.

April, 2024

Hands-On Machine Learning with Scikit Learn and Tensor Flow Chapter 4

This is a jupyter notebook representing some of the code in Hands-On Machine Learning with Scikit Learn and Tensor Flow Chapter 4. In the future, I'm sure that there will be more markdown interspersed with code, but this notebook is primarily code.