mit-deep-learning-book-pdf
MIT Deep Learning Book in PDF format (complete and parts) by Ian Goodfellow, Yoshua Bengio and Aaron Courville (by janishar)
handson-ml2
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2. (by ageron)
| mit-deep-learning-book-pdf | handson-ml2 | |
|---|---|---|
| 3 | 12 | |
| 12,323 | 26,926 | |
| - | - | |
| 2.7 | 0.0 | |
| 7 months ago | 24 days ago | |
| Java | Jupyter Notebook | |
| - | Apache License 2.0 |
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
mit-deep-learning-book-pdf
Posts with mentions or reviews of mit-deep-learning-book-pdf.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2023-11-19.
- Deep Learning Course
- Is supervised machine learning the same as linear regression?
-
NLP resources
I remember an NLP course on DataCamp being helpful as an intro, but a resource I keep handy is Hands-On Machine Learning (Geron) which has really helpful follow along notebooks on the git. Then when you want some background: Deep Learning (Goodfellow)
handson-ml2
Posts with mentions or reviews of handson-ml2.
We have used some of these posts to build our list of alternatives
and similar projects. The last one was on 2022-03-04.
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Book recommendations for 18yo
A text on applied data science, if you like programming and diving into datasets, this could be a good thing to have, there's a pretty good one that's free on github here.
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Moving to TensorFlow from PyTorch
I'd recommend a skim through the Keras/TensorFlow portion of Hands-On-Machine-Learning-with-Scikit-Learn-Keras-and-Tensorflow (https://github.com/ageron/handson-ml2)
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Hands-on ML with Scikit-Learn, Keras and TF2 - Aurelien Geron (Details in comment)
Here's the GitHub repo for the 2nd Ed.
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It was exactly 2 years ago when I decided to self-study data analytics and now I accepted a 6-digit offer.
Hands-on machine learning (Python): Python reference for machine learning. Use their Github repo as a supplement because some codes in the book are outdated. Finish at least part 1: Fundamentals of machine learning.
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I want to relearn machine learning
You get access from the github, https://github.com/ageron/handson-ml2 Its free, but wont have much context without the book(also "free" at Libgen.is)
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Tensorflow error "W tensorflow/core/data/root_dataset.cc:163] Optimization loop failed: CANCELLED: Operation was cancelled"
Here is the repository.
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NLP resources
I remember an NLP course on DataCamp being helpful as an intro, but a resource I keep handy is Hands-On Machine Learning (Geron) which has really helpful follow along notebooks on the git. Then when you want some background: Deep Learning (Goodfellow)
-
An OpenAI Gym docker that can render on Windows
example/18_reinforcement_learning.ipynb: This is a copy from Chapter 18 in Géron, Aurélien's book: Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow. Source code is here in GitHub.
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[D] Thoughts on Hands-On Machine Learning with Scikit-Learn, Keras & Tensorflow by Geron
Have you tried looking at the accompanying github repo.
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[D] Looking for good refreshers on stats / ML to go back to the ML engineer interview game after 2 years doing mostly Software.
Much of the material from that book is publicly available in this repo maintained by the author.
What are some alternatives?
When comparing mit-deep-learning-book-pdf and handson-ml2 you can also consider the following projects:
jblas - Linear Algebra for Java
ggplot2-book - ggplot2: elegant graphics for data analysis
spidr - Accelerated machine learning with dependent types
MLflow - Open source platform for the machine learning lifecycle
Machine-Learning-Tutorials - machine learning and deep learning tutorials, articles and other resources
tests-as-linear - Common statistical tests are linear models (or: how to teach stats)
jcohere - jCohere is a java client for accessing the Cohere.ai platform
PythonDataScienceHandbook - Python Data Science Handbook: full text in Jupyter Notebooks
zero_to_gpt - Go from no deep learning knowledge to implementing GPT.
javaparser-visited - Code samples for the book "JavaParser: Visited" https://leanpub.com/javaparservisited
ultraschall-plugin - Podcasting Extension for REAPER
mit-deep-learning-book-pdf vs jblas
handson-ml2 vs ggplot2-book
mit-deep-learning-book-pdf vs spidr
handson-ml2 vs MLflow
mit-deep-learning-book-pdf vs Machine-Learning-Tutorials
handson-ml2 vs tests-as-linear
mit-deep-learning-book-pdf vs jcohere
handson-ml2 vs PythonDataScienceHandbook
mit-deep-learning-book-pdf vs zero_to_gpt
mit-deep-learning-book-pdf vs javaparser-visited
mit-deep-learning-book-pdf vs ultraschall-plugin