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Machine Learning#

Reproducibility in Machine Learning blog series

This technical blog series titled "Reproducibility in Machine Learning" is going to be divided into three parts: 1. Reproducibility in Machine Learning - Research and Industry 2. Realizing reproducible Machine Learning - with Tensorflow 3. End-to-end reproducible Machine Learning pipelines on Kubernetes

Some of the content of this blog series has been covered in KubeCon US 2019 - a Kubernetes conference 2019. Details of this talk can be found here with recording available here.

Part 1: Reproducibility in Machine Learning - Research and Industry

In Part 1, the objective will be to discuss the importance of reproducibility in machine learning. It will also cover where both research and industry are stand in writing reproducible ML. This blog can be accessed here.

Part 2: Realizing reproducible Machine Learning - with Tensorflow

The focus of Part 2 will be writing reproducible machine learning code. Tensorflow is being used as a machine learning stack for demonstration purposes. This blog can be accessed here.

Part 3: End-to-end reproducible Machine Learning pipelines on Kubernetes

Part 3 is all about realizing end-to-end machine learning pipelines on kubernetes. This blog can be accessed here.

Reading book list

This list includes books that a) I have truly enjoyed reading and highly admire or b) Eagerly looking forward to reading. It has six sections:

  • Abstract Programming/AI
  • Kubernetes
  • Machine Learning, AI, Deep Learning
  • Statistics
  • Parenting
  • Miscellaneous

1. Abstract Programming

Real-World Bug Hunting Authored by Peter Yaworski

Code: The Hidden Language of Computer Hardware and Software Authored by Charles Petzold

The Pragmatic Programmer: your journey to mastery Authored by Andrew Hunt, David Thomas

Coders at Work: Reflections on the Craft of Programming Authored by Peter Seibel

The Book of Why: The New Science of Cause and Effect Authored by Judea Pearl

Clean Code Authored by Robert C. Martin

2. Kubernetes

Kubernetes: Up and Running Authored by Kelsey Hightower, Joe Beda, Brendan Burns

*Kubernetes Security Authored by Michael Hausenblas, Liz Rice *

*Container Security Authored by Liz Rice *

Kubernetes for Developers Authored by Joseph Heck

3. Machine Learning, AI, Deep Learning

The Quest for Artificial Intelligence: A History of Ideas and Achievements Authored by Nils J. Nilsson*

[Machine Learning: A Probabilistic Perspective] Authored by Kevin P Murphy

Neural Networks and Deep Learning Authored by Michael Nielsen

[Deep Learning] Authored by Ian Goodfellow et al.

[Machine Learning Yearning] Authored by Andrew Ng

4. Statistics

Linear Algebra Done Right, Authored by Sheldon Axler

Mathematical Statistics and Data Analysis, Authored by John A. Rice

Elements of Statistical Learning, Authored by Trevor Hastie et al.

Introduction to Statistical Learning Authored by Trevor Hastie et al.

5. Parenting

Becoming Brilliant: What Science Tells Us About Raising Successful Children Authored by Roberta Golinkoff, Kathryn Hirsh-Pasek

Thinking Parent, Thinking Child: Turning Everyday Problems into Solutions Authored by Myrna B. Shure

The Psychology of Babies: How relationships support development from birth to two Authored by Lynne Murray

6. Miscellaneous

The End of Ice: Bearing Witness and Finding Meaning in the Path of Climate Disruption Authored by Dahr Jamail

The Second Kind of Impossible: The Extraordinary Quest for a New Form of Matter Authored by Paul Steinhardt

[Machine Learning: A Probabilistic Perspective]: https://www.amazon.com.au/Machine Learning-Probabilistic-Kevin-Murphy/dp/0262018020

[Machine Learning Yearning]: https://www.deeplearning.ai/Machine Learning-yearning/

[Wikipedia ML dataset]: https://en.wikipedia.org/wiki/List_of_datasets_for_Machine Learning_research

[Hackernoon Rare dataset]: https://hackernoon.com/rare-datasets-for-computer-vision-every-Machine Learning-expert-must-work-with-2ddaf52ad862