CodeSearchNet
AI-For-Beginners
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| CodeSearchNet | AI-For-Beginners | |
|---|---|---|
| 2 | 8 | |
| 1,904 | 30,927 | |
| - | 11.1% | |
| 0.0 | 7.1 | |
| about 2 years ago | 5 days ago | |
| Jupyter Notebook | Jupyter Notebook | |
| MIT License | MIT License |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
CodeSearchNet
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Fine tuning
The CodeSearchNet challenge provides a dataset of code documentation comments, along with pre-trained models and fine-tuning scripts. You can find the challenge and resources at https://github.com/github/CodeSearchNet.
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Speedtyper.dev: Type racing for programmers
https://github.com/github/CodeSearchNet#downloading-data-from-s3
AI-For-Beginners
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FREE AI Course By Microsoft: ZERO to HERO! 🔥
đź”— https://github.com/microsoft/AI-For-Beginners đź”— https://microsoft.github.io/AI-For-Beginners/
- AI For Beginners
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Artificial Intelligence for Beginners – A Curriculum
This is a good summary of most topics in AI/ML. The only thing that it seems to by missing (or maybe I'm just not seeing it) is a section on generative AI for images and video (DALL-E, Stable Diffusion etc).
They do cover LLMs which is generative AI for text though: https://github.com/microsoft/AI-For-Beginners/blob/main/less...
- Artificial Intelligence course
- Artificial Intelligence for Beginners course
- Microsoft's AI for Beginners
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Announcing a New Free Curriculum: Artificial Intelligence for Beginners
Students can use this curriculum to learn the basics of AI and Neural Networks. In addition to text-based lessons, there are executable Jupyter Notebooks with samples, as well as labs that you can do to deepen your knowledge. You can run notebooks either on your local computer or in the cloud. Join your peers on GitHub Discussion Boards to learn together and watch for more learning opportunities online.
What are some alternatives?
data - Data and code behind the articles and graphics at FiveThirtyEight
GAN-RNN_Timeseries-imputation - Recurrent GAN for imputation of time series data. Implemented in TensorFlow 2 on Wikipedia Web Traffic Forecast dataset from Kaggle.
awesome-speech-recognition-speech-synthesis-papers - Automatic Speech Recognition (ASR), Speaker Verification, Speech Synthesis, Text-to-Speech (TTS), Language Modelling, Singing Voice Synthesis (SVS), Voice Conversion (VC)
DeepLearning - Contains all my works, references for deep learning
pycaret - An open-source, low-code machine learning library in Python
gan-vae-pretrained-pytorch - Pretrained GANs + VAEs + classifiers for MNIST/CIFAR in pytorch.
pytorch-GAT - My implementation of the original GAT paper (Veličković et al.). I've additionally included the playground.py file for visualizing the Cora dataset, GAT embeddings, an attention mechanism, and entropy histograms. I've supported both Cora (transductive) and PPI (inductive) examples!
Twitter-sentiment-analysis - A sentiment analysis model trained with Kaggle GPU on 1.6M examples, used to make inferences on 220k tweets about Messi and draw insights from their results.
trulens - Evaluation and Tracking for LLM Experiments
conformal_classification - Wrapper for a PyTorch classifier which allows it to output prediction sets. The sets are theoretically guaranteed to contain the true class with high probability (via conformal prediction).
SimpNet-Deep-Learning-in-a-Shader - A trainable convolutional neural network inside a fragment shader
TSAI-DeepNLP-END2.0