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Top 23 Jupyter Notebook neural-network Projects
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nn
🧑🏫 60 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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machine_learning_complete
A comprehensive machine learning repository containing 30+ notebooks on different concepts, algorithms and techniques.
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WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
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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
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cs229-2018-autumn
All notes and materials for the CS229: Machine Learning course by Stanford University
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tutorials
AI-related tutorials. Access any of them for free → https://towardsai.net/editorial (by towardsai)
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SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
Project mention: AI Made These Movies Sharper. Critics Say It Ruined Them. | news.ycombinator.com | 2024-04-15I watch AI enhanced movies and/or shows every day when I use Anime4K, a real-time ML upscaler for anime https://github.com/bloc97/Anime4K
If you give shit-tier blurry DVD messes, it turns them into nicer blurry messes.
If you give it the usual bitrate starved streaming quality show, it looks almost as nice as a blueray.
If you give it a blueray... It basically comes out looking exactly the same. I'll usually still add its "darken lines" filter since I'm a fan of strong linework.
Project mention: How often do you see Bayesian Statistics or Stan in the DS world? Essential skill or a nice to have? | /r/datascience | 2023-06-17TensorFlow-Probability
Project mention: Implementing a ChatGPT-like LLM from scratch, step by step | news.ycombinator.com | 2024-01-27Sorry, in that case I would rather recommend a dedicated RL book. The RL part in LLMs will be very specific to LLMs, and I will only cover what's absolutely relevant in terms of background info. I do have a longish intro chapter on RL in my other general ML/DL book (https://github.com/rasbt/machine-learning-book/tree/main/ch1...) but like others said, I would recommend a dedicated RL book in your case.
Project mention: coursera-deep-learning-specialization: NEW Courses - star count:2327.0 | /r/algoprojects | 2023-11-21
is used to capture the power of a fully-trained deep net of infinite width.
https://openreview.net/pdf?id=rkl4aESeUH, https://github.com/google/neural-tangents
> It has long been known that a single-layer fully-connected neural network with an i.i.d. prior over its parameters is equivalent to a Gaussian process (GP), in the limit of infinite network width.
https://arxiv.org/abs/1711.00165
And of course, one needs to look back at SVMs applying a kernel function and separating with a line, which looks a lot like an ANN with a single hidden layer followed by a linear mapping.
https://stats.stackexchange.com/questions/238635/kernel-meth...
Retrieval using a single vector is called dense passage retrieval (DPR), because an entire passage (dozens to hundreds of tokens) is encoded as a single vector. ColBERT instead encodes a vector-per-token, where each vector is influenced by surrounding context. This leads to meaningfully better results; for example, here’s ColBERT running on Astra DB compared to DPR using openai-v3-small vectors, compared with TruLens for the Braintrust Coda Help Desk data set. ColBERT easily beats DPR at correctness, context relevance, and groundedness.
NOTE:
The open source projects on this list are ordered by number of github stars.
The number of mentions indicates repo mentiontions in the last 12 Months or
since we started tracking (Dec 2020).
Jupyter Notebook neural-networks related posts
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trulens VS agenta - a user suggested alternative
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- cs229-2018-autumn: NEW Courses - star count:949.0
- cs229-2018-autumn: NEW Courses - star count:949.0
- cs229-2018-autumn: NEW Courses - star count:949.0
- [P] TruLens-Eval is an open source project for eval & tracking LLM experiments.
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A note from our sponsor - WorkOS
workos.com | 24 Apr 2024
Index
What are some of the best open-source neural-network projects in Jupyter Notebook? This list will help you:
| Project | Stars | |
|---|---|---|
| 1 | nn | 48,004 |
| 2 | Anime4K | 17,723 |
| 3 | introtodeeplearning | 6,841 |
| 4 | machine_learning_complete | 4,501 |
| 5 | machine_learning_basics | 4,199 |
| 6 | probability | 4,131 |
| 7 | ML-Workspace | 3,324 |
| 8 | machine-learning-book | 2,843 |
| 9 | coursera-deep-learning-specialization | 2,679 |
| 10 | Andrew-NG-Notes | 2,228 |
| 11 | neural-tangents | 2,219 |
| 12 | ZoeDepth | 1,939 |
| 13 | trulens | 1,589 |
| 14 | Knet.jl | 1,418 |
| 15 | cs229-2018-autumn | 1,389 |
| 16 | geomstats | 1,148 |
| 17 | DFL-Colab | 1,034 |
| 18 | tutorials | 959 |
| 19 | stylegan-encoder | 732 |
| 20 | gdrl | 699 |
| 21 | edward2 | 663 |
| 22 | lava | 498 |
| 23 | mango | 310 |
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