Jupyter Notebook Deep Learning

Open-source Jupyter Notebook projects categorized as Deep Learning

Top 23 Jupyter Notebook Deep Learning Projects

Deep Learning
  • 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, ... 🧠

  • InfluxDB

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  • TensorFlow-Examples

    TensorFlow Tutorial and Examples for Beginners (support TF v1 & v2)

  • Made-With-ML

    Learn how to design, develop, deploy and iterate on production-grade ML applications.

  • Project mention: [D] How do you keep up to date on Machine Learning? | /r/learnmachinelearning | 2023-08-13

    Made With ML

  • AI-For-Beginners

    12 Weeks, 24 Lessons, AI for All!

  • Project mention: FREE AI Course By Microsoft: ZERO to HERO! 🔥 | dev.to | 2024-03-18

    🔗 https://github.com/microsoft/AI-For-Beginners 🔗 https://microsoft.github.io/AI-For-Beginners/

  • fastai

    The fastai deep learning library

  • Project mention: Notebooks Are McDonalds of Code | news.ycombinator.com | 2024-06-13

    I'd say the fastai library itself[0] is a pretty good example of how maintainable/scalable practices can come to life in notebook flows. There's something to be said IMO for an active project with 25.8k stars, 238 contributers, 2.7k commits, and 199 open vs 1.5k closed issues.

    [0] https://github.com/fastai/fastai/

  • handson-ml

    ⛔️ DEPRECATED – See https://github.com/ageron/handson-ml3 instead.

  • CLIP

    CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image

  • Project mention: Anomaly Detection with FiftyOne and Anomalib | dev.to | 2024-05-06

    pip install -U huggingface_hub umap-learn git+https://github.com/openai/CLIP.git

  • SaaSHub

    SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives

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  • shap

    A game theoretic approach to explain the output of any machine learning model.

  • Project mention: IA Explicable: Algoritmos y Métodos para Interpretar Modelos de Caja Negra | dev.to | 2024-06-19
  • fastbook

    The fastai book, published as Jupyter Notebooks

  • Project mention: The fastai book, published as Jupyter Notebooks | news.ycombinator.com | 2024-01-17
  • learnopencv

    Learn OpenCV : C++ and Python Examples

  • Project mention: YOLO-NAS Pose | /r/pytorch | 2023-11-16

    Deci's YOLO-NAS Pose: Redefining Pose Estimation! Elevating healthcare, sports, tech, and robotics with precision and speed. Github link and blog link down below! Repo: https://github.com/spmallick/learnopencv/tree/master/YOLO-NAS-Pose

  • stable-diffusion-webui-colab

    stable diffusion webui colab

  • Project mention: Stable-Diffusion-Webui-Colab | news.ycombinator.com | 2023-07-24
  • first-order-model

    This repository contains the source code for the paper First Order Motion Model for Image Animation

  • DeepLearningExamples

    State-of-the-Art Deep Learning scripts organized by models - easy to train and deploy with reproducible accuracy and performance on enterprise-grade infrastructure.

  • machine-learning-for-trading

    Code for Machine Learning for Algorithmic Trading, 2nd edition.

  • Project mention: Machine Learning for Trading: Notebooks, resources and references accompanying the book Machine Learning for Algorithmic Trading. Courses - star count:10678.0 | /r/algoprojects | 2023-11-20
  • numerical-linear-algebra

    Free online textbook of Jupyter notebooks for fast.ai Computational Linear Algebra course

  • amazon-sagemaker-examples

    Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.

  • Project mention: Thesis Project Help Using SageMaker Free Tier | /r/aws | 2023-09-23

    I need to use AWS Sagemaker (required, can't use easier services) and my adviser gave me this document to start with: https://github.com/aws/amazon-sagemaker-examples/blob/main/introduction_to_amazon_algorithms/jumpstart-foundation-models/question_answering_retrieval_augmented_generation/question_answering_langchain_jumpstart.ipynb

  • TensorFlow-Tutorials

    TensorFlow Tutorials with YouTube Videos


    LAVIS - A One-stop Library for Language-Vision Intelligence

  • Project mention: FLaNK AI for 11 March 2024 | dev.to | 2024-03-11
  • TTS

    :robot: :speech_balloon: Deep learning for Text to Speech (Discussion forum: https://discourse.mozilla.org/c/tts) (by mozilla)

  • Project mention: Coqui.ai Is Shutting Down | news.ycombinator.com | 2024-01-03

    Coqui-ai was a commercial continuation of Mozilla TTS and STT (https://github.com/mozilla/TTS).

    At the time (2018-ish), it was really impressive for on-device voice synthesis (with a quality approaching the Google and Azure cloud-based voice synthesis options) and open source, so a lot of people in the FOSS community were hoping it could be used for a privacy-respecting home assistant, Linux speech synthesis that doesn't suck, etc.

    After Mozilla abandoned the project, Coqui continued development and had some really impressive one-shot voice cloning, but pivoted to marketing speech synthesis for game developers. They were probably having trouble monetizing it, and it doesn't surprise me that they shut down.

    An equivalent project that's still in active development and doing really well is Piper TTS (https://github.com/rhasspy/piper).

  • open_clip

    An open source implementation of CLIP.

  • Project mention: Binarize Clip for Multimodal Applications | news.ycombinator.com | 2024-05-23

    The part of CLIP[1] that you need to know to understand this is that it embeds text and images into the same space. ie: the word "dog" is close to images of dogs. Normally this space is a high dimensional real space. Think 512-dimensional or 512 floating point numbers. When you want to measure "closeness" between vectors in this space cosine similarity[2] is a natural choice.

    Why would you want to quantize values? Well, instead of using a 32-bit float for each dimension, what if you could get away with 1-bit? You would save you 31x the space. Often you'll want to embed millions or billions of pieces of text or images, so the savings represent a huge speed & cost savings and if accuracy isn't impacted too much then it could be worth it.

    If you naively clip the floats of an existing model, it severely impacts accuracy. However, if you train a model from scratch that produces binary outputs, then it appears to perform better.

    There is one twist. Deep learning models rely on gradient descent to train and binary output doesn't produce useful gradients. We use cosine similarity on floating point vectors and hamming distance on bit vectors. Is there a function that behaves like hamming distance but is nicely differentiable? We can then use this function during training and then vanilla hamming distance during inference. It seems like they've done that.

    I'd suggest playing around with OpenCLIP[3]. My background is in data science but all my CLIP knowledge comes from doing a side project over the course of a couple weekends.

    1. https://huggingface.co/docs/transformers/model_doc/clip

    2. https://en.wikipedia.org/wiki/Cosine_similarity

    3. https://github.com/mlfoundations/open_clip

  • lama

    🦙 LaMa Image Inpainting, Resolution-robust Large Mask Inpainting with Fourier Convolutions, WACV 2022

  • Project mention: Can someone please help me with inpainting settings to remove the subject from this image? I want to rebuild as much of the original background as possible. | /r/StableDiffusion | 2023-07-03

    You could try to use ControlNet inpaint+lama locally, but results aren't as good in my experience. Or you could try local install of lama directly, but the setup process isn't very smooth.

  • models

    A collection of pre-trained, state-of-the-art models in the ONNX format (by onnx)

  • Project mention: Giving Odin Intelligence | dev.to | 2024-05-21

    curl https://github.com/onnx/models/raw/main/validated/vision/classification/squeezenet/model/squeezenet1.0-8.onnx -Lso squeezenet1.0-8.onnx

  • vosk-api

    Offline speech recognition API for Android, iOS, Raspberry Pi and servers with Python, Java, C# and Node

  • Project mention: Infini-Gram: Scaling unbounded n-gram language models to a trillion tokens | news.ycombinator.com | 2024-05-05
  • SaaSHub

    SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives

    SaaSHub logo
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).

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What are some of the best open-source Deep Learning projects in Jupyter Notebook? This list will help you:

Project Stars
1 nn 50,306
2 TensorFlow-Examples 43,269
3 Made-With-ML 36,457
4 AI-For-Beginners 32,467
5 fastai 25,814
6 handson-ml 25,108
7 CLIP 23,118
8 shap 22,008
9 fastbook 21,028
10 learnopencv 20,612
11 stable-diffusion-webui-colab 15,411
12 first-order-model 14,277
13 DeepLearningExamples 12,876
14 machine-learning-for-trading 12,134
15 numerical-linear-algebra 10,074
16 amazon-sagemaker-examples 9,753
17 TensorFlow-Tutorials 9,250
18 LAVIS 9,072
19 TTS 8,975
20 open_clip 8,924
21 lama 7,465
22 models 7,426
23 vosk-api 7,290

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