stanford-CS229 VS cracking-the-data-science-interview

Compare stanford-CS229 vs cracking-the-data-science-interview and see what are their differences.

stanford-CS229

Python solutions to the problem sets of Stanford's graduate course on Machine Learning, taught by Prof. Andrew Ng [UnavailableForLegalReasons - Repository access blocked] (by ccombier)
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stanford-CS229 cracking-the-data-science-interview
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10.0 0.0
11 months ago 10 months ago
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stanford-CS229

Posts with mentions or reviews of stanford-CS229. We have used some of these posts to build our list of alternatives and similar projects.

cracking-the-data-science-interview

Posts with mentions or reviews of cracking-the-data-science-interview. We have used some of these posts to build our list of alternatives and similar projects.

What are some alternatives?

When comparing stanford-CS229 and cracking-the-data-science-interview you can also consider the following projects:

cs229-solution - CS229 Solution (summer 2019, 2020).

edward2 - A simple probabilistic programming language.

coursera-deep-learning-specialization - Notes, programming assignments and quizzes from all courses within the Coursera Deep Learning specialization offered by deeplearning.ai: (i) Neural Networks and Deep Learning; (ii) Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization; (iii) Structuring Machine Learning Projects; (iv) Convolutional Neural Networks; (v) Sequence Models

probability - Probabilistic reasoning and statistical analysis in TensorFlow

cs229-2018-autumn - All notes and materials for the CS229: Machine Learning course by Stanford University

wordlescraper - Combine wordle statistics metrics from various locations, data science to correlate scores with words, and a front end to display the results.

cs231n - Note and Assignments for CS231n: Convolutional Neural Networks for Visual Recognition

condo-adapter - Confounded Domain Adapter

Deep-Learning-Computer-Vision - My assignment solutions for Stanford’s CS231n (CNNs for Visual Recognition) and Michigan’s EECS 498-007/598-005 (Deep Learning for Computer Vision), version 2020.