AI-For-Beginners
| sign_language_detector | AI-For-Beginners | |
|---|---|---|
| 2 | 8 | |
| 3 | 31,046 | |
| - | 2.4% | |
| 0.0 | 6.7 | |
| almost 3 years ago | 11 days ago | |
| Jupyter Notebook | Jupyter Notebook | |
| - | MIT License |
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sign_language_detector
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How to customize the dataset in this code (mediapipe)
Hello. I am very new to Jupyter Notebook and programming in general and things got a little confusing for me. So I'm trying to run this program from this GitHub link https://github.com/prp-e/sign_language_detector So far, it works for me and it was able to do the task intended. However, I was wondering how could I customize the dataset to my liking (i.e. using different words rather than the ones already included) for translation? Steps 5 and 6 of the notebook are data gathering and adding data to the CSV file, respectively, but when I ran it, it didn't change the dataset.
- Sign language detector/translator using mediapipe and scikit-learn (if you give me a star on github, I'll appreciate your kindness)
AI-For-Beginners
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FREE AI Course By Microsoft: ZERO to HERO! 🔥
🔗 https://github.com/microsoft/AI-For-Beginners 🔗 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?
machine-learning-experiments - 🤖 Interactive Machine Learning experiments: 🏋️models training + 🎨models demo
GAN-RNN_Timeseries-imputation - Recurrent GAN for imputation of time series data. Implemented in TensorFlow 2 on Wikipedia Web Traffic Forecast dataset from Kaggle.
nlpaug - Data augmentation for NLP
DeepLearning - Contains all my works, references for deep learning
CameraTraps - PyTorch Wildlife: a Collaborative Deep Learning Framework for Conservation.
gan-vae-pretrained-pytorch - Pretrained GANs + VAEs + classifiers for MNIST/CIFAR in pytorch.
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.
CodeSearchNet - Datasets, tools, and benchmarks for representation learning of code.
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).
TSAI-DeepNLP-END2.0
LLVIP - LLVIP: A Visible-infrared Paired Dataset for Low-light Vision
CAH - Code used for Cards Against Humanity EMNLP paper