ISL-python
paip-lisp
ISL-python | paip-lisp | |
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4 | 67 | |
181 | 7,012 | |
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0.0 | 0.8 | |
over 1 year ago | 7 months ago | |
Jupyter Notebook | Common Lisp | |
- | MIT License |
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ISL-python
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Andrew Ng's Machine Learning Specialization or Introduction to Statistical Learning? For someone who's comfortable with mathematics.
https://github.com/emredjan/ISL-python this GitHub has the exercises in python but I am so pumped the python version is coming out this summer.
- Hey I wanna learn Statistics with python can anyone suggest me a good book and a good YouTube tutorial because i am really poor at it I don't know the basic concepts about it
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ESL vs ISLR books?
Here or here for the Python versions of ISLR.
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The Hundred-Page Machine Learning Book
I typically recommend a few different books to everyone who finishes the bootcamp, based on a self-assessment they take. I recommend some books based on their strengths, so they can find a career path sooner, and some books based on their weaknesses, so they can widen their cone of oppportunity within ML.
In our consultancy, data science is done in Python and SQL (and PySpark, but I don't hand out books on that during bootcamp!), and ML delivery is a combination of math, software engineering, and architecture/product owner disciplines.
If you're strong in software engineering, I recommend Machine Learning Mastery with Python by Jason Brownlee as it's very hands-on in Python and helps you run code to "see" how ML works.
If you're weak in software engineering and Python, I recommend A Whirlwind Tour Of Python by Jake VanderPlas, and its companion book Python Data Science Handbook.
If you're strong in architecting / product management, I recommend Building Machine Learning Powered Applications by Emmanuel Ameisen since it explains it more from an SDLC perspective, including things like scoping, design, development, testing, general software engineering best practices, collaboration, etc.
If you're weak in architecting / product management, I typically recommend User Story Mapping by Jeff Patton and Making Things Happen by Scott Berkun, which are both excellent how-tos with great examples to build on.
If you're strong in math, I recommend Understanding Machine Learning from Theory to Algorithm by Shalev-Shwartz and Ben-David, as it has all the mathematics for ML and actually has some pseudocode for implementation which helps bridge the gap into actual software development (the book's title is very accurate!)
For someone who is weak in the math of ML, I recommend Introduction to Statistical Learning by Hastie et al (along with the Python port of the code https://github.com/emredjan/ISL-python ) which I think does just enough hand holding to move someone from "did high school math 20 years ago" to "I understand what these hyperparameters are optimizing for."
Anyway, I've spent a lot of time reading and reviewing books about ML, and my key takeaway is ones that get you closer to writing actual code to solving actual problems for actual people are the ones to focus on.
paip-lisp
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The Loudest Lisp Program
Have you seen https://stevelosh.com/blog/2018/08/a-road-to-common-lisp/ ? "Kludges" everywhere is applicable. On the other hand, having a function like "row-major-aref" that allows accessing any multi-dimensional array as if it were one dimensional is "sweeter than the honeycomb".
I still think CL code can be beautiful. Norvig's in PAIP https://github.com/norvig/paip-lisp is nice.
As for the inside-out remark, while technically you do it, you don't have to, and it's very convenient to not do. Clojure has its semi-famous arrow macro that lets you write things in a more sequential style, it exists in CL too, and there's always the venerable let* binding. e.g. 3 options:
(loop (print (eval (read))))
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Ask HN: Guide for Implementing Common Lisp
PAIP by Peter Norvig, Chapter 23, Compiling Lisp
https://github.com/norvig/paip-lisp/blob/main/docs/chapter23...
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The Meeting of the Minds That Launched AI
Emacs is so much more than a text editor! But I need to stay on topic...
I believe your assessment of LISP (and therefore of MacArthy)'s impact on AI to be unfair. Just a few days ago https://github.com/norvig/paip-lisp was discussed on this site, for example.
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Towards a New SymPy
Sounds like a great project idea to make a toy demo of this direction you'd like to see. Maybe comparable to https://github.com/norvig/paip-lisp/blob/main/docs/chapter15... and https://github.com/norvig/paip-lisp/blob/main/docs/chapter8.... which are a few hundred lines of Lisp each, but do enough to be interesting.
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A few newbie questions about lisp
You could look into Paradigms of AI Programming by Peter Norvig which might interest you regardless of Lisp content.
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Mathematical paradigm?
Lisp has great power, examine PAIP, part II chapters 7 and 8.
- Peter Norvig – Paradigms of AI Programming Case Studies in Common Lisp
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Evidence that GPT-4 has a level of understanding
A computer running Prolog reasons, and that only requires a couple of pages of code. So it seems feasible that the network could have learned some ability to reason within its network.
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Conversation with Larry Masinter about Standardizing Common Lisp
IMHO it's because lisp shines to manipulate symbols whereas the current AI trend is crunching matrices.
When AI was about building grammars, trees, developing expert systems builds rules etc. symbol manipulation was king. Look at PAIP for some examples: https://github.com/norvig/paip-lisp
This paradigm has changed.
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A lispy book on databases
Origen: Conversación con Bing, 4/4/2023(1) gigamonkey/monkeylib-binary-data - GitHub. https://github.com/gigamonkey/monkeylib-binary-data Con acceso 4/4/2023. (2) paip-lisp/chapter4.md at main · norvig/paip-lisp · GitHub. https://github.com/norvig/paip-lisp/blob/main/docs/chapter4.md Con acceso 4/4/2023. (3) bibliography.md · GitHub. https://gist.github.com/gigamonkey/6151820 Con acceso 4/4/2023.
What are some alternatives?
ISLR-python - An Introduction to Statistical Learning (James, Witten, Hastie, Tibshirani, 2013): Python code
mal - mal - Make a Lisp
the-elements-of-statistical-learning - My notes and codes (jupyter notebooks) for the "The Elements of Statistical Learning" by Trevor Hastie, Robert Tibshirani and Jerome Friedman
30-days-of-elixir - A walk through the Elixir language in 30 exercises.
fecon235 - Notebooks for financial economics. Keywords: Jupyter notebook pandas Federal Reserve FRED Ferbus GDP CPI PCE inflation unemployment wage income debt Case-Shiller housing asset portfolio equities SPX bonds TIPS rates currency FX euro EUR USD JPY yen XAU gold Brent WTI oil Holt-Winters time-series forecasting statistics econometrics
Crafting Interpreters - Repository for the book "Crafting Interpreters"
ydata-profiling - 1 Line of code data quality profiling & exploratory data analysis for Pandas and Spark DataFrames.
coalton - Coalton is an efficient, statically typed functional programming language that supercharges Common Lisp.
ML-foundations - Machine Learning Foundations: Linear Algebra, Calculus, Statistics & Computer Science
picolisp-by-example - The source code of the free book "PicoLisp by Example"
slime - The Superior Lisp Interaction Mode for Emacs
pytudes - Python programs, usually short, of considerable difficulty, to perfect particular skills.