From-0-to-Research-Scientist-resources-guide
tuning_playbook
| From-0-to-Research-Scientist-resources-guide | tuning_playbook | |
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| 9 | 16 | |
| 7,352 | 25,053 | |
| - | 0.9% | |
| 1.9 | 4.7 | |
| about 2 months ago | 12 days ago | |
| - | GNU General Public License v3.0 or later |
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From-0-to-Research-Scientist-resources-guide
- Roadmap para Inteligência artificial.
- From Zero to Research Scientist - Detailed and tailored guide for undergraduate students or anybody want to dig deep into the field of AI with solid foundation.
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Trending ML repos of the week 📈
9️⃣ ahmedbahaaeldin/From-0-to-Research-Scientist-resources-guide
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Free ML Resources on the web
I am sharing with you this https://github.com/ahmedbahaaeldin/From-0-to-Research-Scientist-resources-guide so that you can find all the relevant books and courses that other people tried. And are insightful based upon reviews. Happy learning.
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Book Before Papers?
This guide is quite comprehensive for taking courses and reading papers From-0-to-Research-Scientist-resources-guide. Again as stated in the comments working on problems is more rewarding, and you learn more, but I would say first make sure you understand the fundamental principles.
- Mathematics and Machine Learning Free Resources
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Github ML resources list
Hello all, Iam a TA at some university and created a list for students for starting AI. I would love some feedback from experienced people on how to improve this plan so i can help as much as i can of people to learn AI. https://github.com/ahmedbahaaeldin/From-0-to-Research-Scientist-resources-guide
tuning_playbook
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When Random Numbers Are Too Random: Low Discrepancy Sequences
These are also called quasirandom numbers. Despite games, another use case is for hyperparameter search for neural networks.
https://github.com/google-research/tuning_playbook?tab=readm...
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Hyperparameter Optimization for LLMs via Scaling Laws
[2] https://github.com/google-research/tuning_playbook
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Beyond Automatic Differentiation
Batch size can be used for regularisation, but using it for that will limit training performance. From the Google Research Tuning Playbook:
> The batch size governs the training speed and shouldn't be used to directly tune the validation set performance. Often, the ideal batch size will be the largest batch size supported by the available hardware.
> […]
> As long as all hyperparameters are well-tuned (especially the learning rate and regularization hyperparameters) and the number of training steps is sufficient, the same final performance should be attainable using any batch size (see Shallue et al. 2018).
https://github.com/google-research/tuning_playbook#choosing-...
The ideal case is full-batch with tuneable regularisation, just the hardware gets expensive.
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Modeling methodology
Regarding tuning params, this is an excellent read: https://github.com/google-research/tuning_playbook
- About the hardware
- I asked an AI to create an Asmongold story and then had another AI generate voice. There it is dude
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Trending ML repos of the week 📈
3️⃣ google-research/tuning_playbook
- AI全靠偷欧美开源的
- Deep learning tuning playbook
What are some alternatives?
nanoGPT - The simplest, fastest repository for training/finetuning medium-sized GPTs.
dadaptation - D-Adaptation for SGD, Adam and AdaGrad
gpt_index - LlamaIndex (GPT Index) is a project that provides a central interface to connect your LLM's with external data. [Moved to: https://github.com/jerryjliu/llama_index]
arb - Arb has been merged into FLINT -- use https://github.com/flintlib/flint/ instead
AnkiMath - A bachelor's degree in mathematics.
nn-zero-to-hero - Neural Networks: Zero to Hero
100-Days-Of-ML-Code - 100 Days of ML Coding
ML-Papers-Explained - Explanation to key concepts in ML
awesome-chatgpt-prompts - This repo includes ChatGPT prompt curation to use ChatGPT better.
Open-Assistant - OpenAssistant is a chat-based assistant that understands tasks, can interact with third-party systems, and retrieve information dynamically to do so.
ChatGPT - 🔮 ChatGPT Desktop Application (Mac, Windows and Linux)