mit-deep-learning-book-pdf
zero_to_gpt
| mit-deep-learning-book-pdf | zero_to_gpt | |
|---|---|---|
| 3 | 3 | |
| 12,323 | 772 | |
| - | - | |
| 2.7 | 6.3 | |
| 7 months ago | 10 months ago | |
| Java | Jupyter Notebook | |
| - | GNU General Public License v3.0 or later |
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mit-deep-learning-book-pdf
- Deep Learning Course
- Is supervised machine learning the same as linear regression?
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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)
zero_to_gpt
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Deep Learning Course
The deep learning book is a great choice, as many have mentioned.
I've been making a course that has a little less theory, and a little more application here - https://github.com/VikParuchuri/zero_to_gpt . Videos are all optional (cover the same content as the text).
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Ask HN: Resources to brush up from 'Intro to ML' to current LLMs/generative AI?
I've been putting a course together that teaches deep learning from the ground up - https://github.com/VikParuchuri/zero_to_gpt . It includes theory and code, and tries to strike a balance between the two.
It focuses on text models over image models (rnn, transformer, etc).
It's not 100% finished, but has enough to get you very far.
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Ask HN: Tell us about your project that's not done yet but you want feedback on
I'm in the process of creating a deep learning course called Zero to GPT - https://github.com/VikParuchuri/zero_to_gpt .
It teaches you everything you need to train your own LLM, including the basics of deep learning and linear algebra. You learn the theory and the application, so you have a strong grounding in what you're doing. It includes written explanations, diagrams, and videos.
I'm up to transformers now - only a few more lessons to go. It's been fun to write, but balancing time spent training models with writing the course has been hard. Hopefully I will get the time to finish it soon.
What are some alternatives?
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.
logseq - A local-first, non-linear, outliner notebook for organizing and sharing your personal knowledge base. Use it to organize your todo list, to write your journals, or to record your unique life.
jblas - Linear Algebra for Java
url2epub - Create ePub files from URLs
spidr - Accelerated machine learning with dependent types
paisa - Paisa – Personal Finance Manager. https://paisa.fyi demo: https://demo.paisa.fyi
Machine-Learning-Tutorials - machine learning and deep learning tutorials, articles and other resources
pls - `pls` is a prettier and powerful `ls(1)` for the pros.
jcohere - jCohere is a java client for accessing the Cohere.ai platform
divedb - This is the source repository for the DiveDB site
javaparser-visited - Code samples for the book "JavaParser: Visited" https://leanpub.com/javaparservisited
nitter - Alternative Twitter front-end