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Top 23 Jupyter Notebook Tutorial Projects
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digital_video_introduction
A hands-on introduction to video technology: image, video, codec (av1, vp9, h265) and more (ffmpeg encoding). Translations: 🇺🇸 🇨🇳 🇯🇵 🇮🇹 🇰🇷 🇷🇺 🇧🇷 🇪🇸
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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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pytorch-seq2seq
Tutorials on implementing a few sequence-to-sequence (seq2seq) models with PyTorch and TorchText.
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pytorch-sentiment-analysis
Tutorials on getting started with PyTorch and TorchText for sentiment analysis.
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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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notebooks
Examples and tutorials on using SOTA computer vision models and techniques. Learn everything from old-school ResNet, through YOLO and object-detection transformers like DETR, to the latest models like Grounding DINO and SAM.
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100-plus-Python-programming-exercises-extended
The repository is about 100+ python programming exercise problem discussed, explained, and solved in different ways
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uvadlc_notebooks
Repository of Jupyter notebook tutorials for teaching the Deep Learning Course at the University of Amsterdam (MSc AI), Fall 2023
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DeepLearningForNLPInPytorch
An IPython Notebook tutorial on deep learning for natural language processing, including structure prediction.
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tutorials
AI-related tutorials. Access any of them for free → https://towardsai.net/editorial (by towardsai)
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reinforcement_learning_course_materials
Lecture notes, tutorial tasks including solutions as well as online videos for the reinforcement learning course hosted by Paderborn University
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pytorch-image-classification
Tutorials on how to implement a few key architectures for image classification using PyTorch and TorchVision.
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JustEnoughScalaForSpark
A tutorial on the most important features and idioms of Scala that you need to use Spark's Scala APIs.
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get-started-with-JAX
The purpose of this repo is to make it easy to get started with JAX, Flax, and Haiku. It contains my "Machine Learning with JAX" series of tutorials (YouTube videos and Jupyter Notebooks) as well as the content I found useful while learning about the JAX ecosystem.
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SaaSHub
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There's a great introduction to video tech, including codecs, at https://github.com/leandromoreira/digital_video_introduction
Yeah, inference[1] is our open source package for running locally (either directly in Python or via a Docker container). It works with all the models on Universe, models you train yourself (assuming we support the architecture; we have a bunch of notebooks available[2]), or train in our platform, plus several more general foundation models[3] (for things like embeddings, zero-shot detection, question answering, OCR, etc).
We also have a hosted API[4] you can hit for most models we support (except some of the large vision models that are really GPU-heavy) if you prefer.
[1] https://github.com/roboflow/inference
[2] https://github.com/roboflow/notebooks
[3] https://inference.roboflow.com/foundation/about/
[4] https://docs.roboflow.com/deploy/hosted-api
Project mention: I miss the old days where people asked me to recreate “Facebook” or “Twitter” | /r/ProgrammerHumor | 2023-06-04So, I don’t have anything simple that’s readily available, and I don’t know how much you’d get from the code itself without some background. But I would recommend the UVA Deep Learning tutorials. Particularly, I’d recommend trying the autoencoder as a good start (tutorial 9). Autoencoders are very easy and fast models to train.
Project mention: The programming languages I learned in my Quantum Computing job | dev.to | 2024-03-15JuliaLang.org “Getting Started” Guide: The official Julia documentation provides a concise introduction to the language and syntax. https://julialang.org/learning/
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).
Project mention: How to stay up-to-date with the latest AI company announcements and events? | /r/singularity | 2023-05-12
To be clear on this: DataFrames, like most of the Julia ecosystem, follows SemVer. DataFrames 1.0 was released over two years ago (March 2021), and the API has been stable ever since.
Furthermore, Bogumil Kaminski, one of the main developers behind DataFrames, makes sure that the DataFrames tutorials he has created here (https://github.com/bkamins/Julia-DataFrames-Tutorial) are updated on every new release.
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 Tutorial related posts
- The programming languages I learned in my Quantum Computing job
- Breakdown of AV1 Video Codec
- Ask HN: Resources to brush up from 'Intro to ML' to current LLMs/generative AI?
- Building a Llama2 Langchain powered Simple Chat Bot hosted on Napptive
- A Hands-On Introduction to Video Codec Technology and FFmpeg
- I have open-sourced code to reduce your ChatGPT api costs by 50%
- Does CS 1301 cover topics like NumPy and pandas?
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A note from our sponsor - WorkOS
workos.com | 27 Apr 2024
Index
What are some of the best open-source Tutorial projects in Jupyter Notebook? This list will help you:
| Project | Stars | |
|---|---|---|
| 1 | TensorFlow-Examples | 43,200 |
| 2 | digital_video_introduction | 15,111 |
| 3 | nlp-tutorial | 13,691 |
| 4 | pandas_exercises | 10,188 |
| 5 | TensorFlow-Tutorials | 9,250 |
| 6 | pytorch-seq2seq | 5,150 |
| 7 | pytorch-sentiment-analysis | 4,225 |
| 8 | notebooks | 4,134 |
| 9 | 100-plus-Python-programming-exercises-extended | 2,648 |
| 10 | uvadlc_notebooks | 2,133 |
| 11 | DeepLearningForNLPInPytorch | 1,901 |
| 12 | JuliaTutorials | 1,207 |
| 13 | tutorials | 959 |
| 14 | reinforcement_learning_course_materials | 900 |
| 15 | pytorch-image-classification | 899 |
| 16 | intro-to-python | 871 |
| 17 | zero_to_gpt | 743 |
| 18 | JustEnoughScalaForSpark | 673 |
| 19 | get-started-with-JAX | 557 |
| 20 | Julia-DataFrames-Tutorial | 507 |
| 21 | sports | 438 |
| 22 | psi4numpy | 323 |
| 23 | pyroad | 305 |
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