Flow: The Psychology of Optimal Experience (Harper Perennial Modern Classics)
Mihaly Csikszentmihalyi
4.5 on Amazon
64 HN comments
Flowers for Algernon
Daniel Keyes, Jeff Woodman, et al.
4.7 on Amazon
39 HN comments
The Principles of Product Development Flow: Second Generation Lean Product Development
Donald G. Reinertsen
4.7 on Amazon
36 HN comments
Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2, 3rd Edition
Sebastian Raschka and Vahid Mirjalili
4.5 on Amazon
12 HN comments
Flowers
Carolyne Roehm
4.6 on Amazon
7 HN comments
Team Topologies: Organizing Business and Technology Teams for Fast Flow
Matthew Skelton , Manuel Pais , et al.
4.6 on Amazon
4 HN comments
The Perks of Being a Wallflower
Stephen Chbosky
4.7 on Amazon
2 HN comments
Making Work Visible: Exposing Time Theft to Optimize Work & flow
Dominica Degrandis, Erin Bennett, et al.
4.6 on Amazon
2 HN comments
Project to Product: How to Survive and Thrive in the Age of Digital Disruption with the Flow Framework
Mik Kersten
4.6 on Amazon
1 HN comments
Rich Dad's CASHFLOW Quadrant: Rich Dad's Guide to Financial Freedom
Robert T. Kiyosaki
4.7 on Amazon
1 HN comments
kriroonJune 23, 2016
dasbothonJuly 19, 2016
krosaenonJuly 14, 2017
https://sebastianraschka.com/
the author of Python Machine Learning. He's great at explaining things, wrote the bets intro to ML book IMHO and is a good Twitter follow.
I also humbly submit my own guide, from the perspective of how to approach studying it amidst all the resources available:
http://karlrosaen.com/ml/
madrafionMay 7, 2017
This is mostly ML if you would like to dive into deep learning I think fast.ai is the best course for anyone with programming experience and you can also use the deeplearning.net Tutorial as a side reference.
If you have a practical experience and would love to understand the theory behinds it then Deep Learning Book is the Bible.
master_yoda_1onMay 5, 2017
Once you figure that out then search for books for answer.
Here is a good book to start with Python Machine Learning.
Also don't read any book because it is free, Barber book is heavy in maths, you need at least a college level calculus and advanced statistics/ probability course to understand it.
krosaenonMar 18, 2017
- The Master Algorithm: made for a general audience, gives you a lay of the land
- Python Machine Learning by Sebastian Raschka: gives you practical skills using python, scikit-learn, numpy, jupyter notebooks, pandas etc. From zero to kaggle in 4 chapters, goes deeper after that. Also goes into enough theory you aren't flying completely blind.
After that, I'm afraid I think you do need to go "academic", if by that you mean learning some of the underlying math to approach AI / ML from a more rigorous probabilistic perspective. I'd recommend studying probability theory and then working your way through Bishop's Pattern Recognition and Machine Learning. After that a lot more doors open up too more specialized topics like computer vision, reinforcement learning etc.
I've written up a lot more about this here:
http://karlrosaen.com/ml/
malsheonAug 12, 2021
dandershonDec 5, 2016
Thus far Udacity's course fits your explanation of using sklearn docs, tweaking inputs, etc. while PML provides a technical explanation of weighting with perceptrons and then you hand-write a perceptron learning algorithm.
a_bonoboonDec 8, 2017
Wickham's R for Data Science came out in December 2016, I'd like to pretend that counts as 2017: http://r4ds.had.co.nz/
It's a very complete introduction to the tidyverse which makes working in R much more pleasurable.
The second edition of Python for Data Science came out in October 2017 - that one focuses on Pandas, numpy and Jupyter notebooks, reasonably good introduction to those libraries.
The second edition of Sebastian Raschka's Python Machine Learning came out 2017 too - that one focuses more on scikit-learn and tensorflow, have only heard good things but haven't read much in it.
crazybitonJune 3, 2018
1) 16 notebooks from the book "Python Machine Learning" by Raschka & Mirjalili
https://github.com/rasbt/python-machine-learning-book-2nd-ed...
2) Linear Regression, Logistic Regression, Random Forests, and k-Means Clustering notebooks by Nitin Borwankar
https://github.com/nborwankar/LearnDataScience
3) scikit-learn tutorial notebooks by Jake VanderPlas
https://github.com/jakevdp/sklearn_tutorial
4) Lots of deep learning notebooks from the book "Deep Learning with Python" by François Chollet
https://github.com/fchollet/deep-learning-with-python-notebo...
Bonus) Jupyter notebook on AWS tutorial (when your local computer just won't handle your notebook requirements):
http://efavdb.com/deep-learning-with-jupyter-on-aws/
Please share your jupyter notebook recommendations.
navalsainionAug 24, 2017
agigaoonJuly 13, 2018
* Faust - Johann Wolfgang von Goethe
* How To Read a Book - Mortimer J. Adler
* Python Machine Learning 2nd Edition - Sebastian Raschka
* Economics 19e - Samuelson/Nordhaus
Next in line:
* The Sleepwalkers - Hermann Broch
* The Will to Power - Friedrich Nietzsche
* Oxford Handbook of Business and Government - David Coen(and other dozens of lecturers)