Jumanji: a Diverse Suite of Scalable Reinforcement Learning Environments in JAX

Jumanji is helping pioneer a new wave of hardware-accelerated research and development in the field of RL.

🖥 Github: https://github.com/instadeepai/jumanji

📕 Paper: https://arxiv.org/abs/2306.09884v1

🔗 Dataset: https://paperswithcode.com/dataset/mujoco

https://t.iss.one/DataScienceT

Google just dropped Generative AI learning path with 9 courses:

🤖: Intro to Generative AI
🤖: Large Language Models
🤖: Responsible AI
🤖: Image Generation
🤖: Encoder-Decoder
🤖: Attention Mechanism
🤖: Transformers and BERT Models
🤖: Create Image Captioning Models
🤖: Intro to Gen AI Studio

🌐 Link: https://www.cloudskillsboost.google/paths/118

https://t.iss.one/DataScienceT

YOLOv8 is the newest state-of-the-art YOLO model that can be used for object detection, image classification, and instance segmentation tasks. YOLOv8 includes numerous architectural and developer experience changes and improvements over YOLOv5.

Code:
https://github.com/ultralytics/ultralytics

What's New in YOLOv8 ?
https://blog.roboflow.com/whats-new-in-yolov8/

Yolov8 Instance Segmentation (ONNX):
https://github.com/ibaiGorordo/ONNX-YOLOv8-Instance-Segmentation

https://t.iss.one/DataScienceT

⭐️ 15 Best Machine Learning Cheat Sheet ⭐️

1- Supervised Learning

https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-supervised-learning.pdf

2- Unsupervised Learning

https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-unsupervised-learning.pdf

3- Deep Learning

https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-deep-learning.pdf

4- Machine Learning Tips and Tricks

https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/cheatsheet-machine-learning-tips-and-tricks.pdf

5- Probabilities and Statistics

https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/refresher-probabilities-statistics.pdf

6- Comprehensive Stanford Master Cheat Sheet

https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/super-cheatsheet-machine-learning.pdf

7- Linear Algebra and Calculus

https://github.com/afshinea/stanford-cs-229-machine-learning/blob/master/en/refresher-algebra-calculus.pdf

8- Data Science Cheat Sheet

https://s3.amazonaws.com/assets.datacamp.com/blog_assets/PythonForDataScience.pdf

9- Keras Cheat Sheet

https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Keras_Cheat_Sheet_Python.pdf

10- Deep Learning with Keras Cheat Sheet

https://github.com/rstudio/cheatsheets/raw/master/keras.pdf

11- Visual Guide to Neural Network Infrastructures

https://www.asimovinstitute.org/wp-content/uploads/2016/09/neuralnetworks.png

12- Skicit-Learn Python Cheat Sheet

https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Scikit_Learn_Cheat_Sheet_Python.pdf

13- Scikit-learn Cheat Sheet: Choosing the Right Estimator

https://scikit-learn.org/stable/tutorial/machine_learning_map/

14- Tensorflow Cheat Sheet

https://github.com/kailashahirwar/cheatsheets-ai/blob/master/PDFs/Tensorflow.pdf

15- Machine Learning Test Cheat Sheet

https://www.cheatography.com/lulu-0012/cheat-sheets/test-ml/pdf/

https://t.iss.one/DataScienceT

REBEL: Relation Extraction By End-to-end Language generation

REBEL is a seq2seq model that simplifies Relation Extraction.

🖥 Github: https://github.com/Babelscape/rebel

⭐️Demo: https://huggingface.co/spaces/Babelscape/rebel-demo

⭐️ Hugging face: https://huggingface.co/Babelscape/rebel-large

📕 Paper: https://arxiv.org/abs/2306.09802v1

🔗Dataset: https://huggingface.co/Babelscape/rebel-large

https://t.iss.one/DataScienceT

Semi-supervised learning made simple with self-supervised clustering [CVPR 2023]

🖥 Github: https://github.com/Ruixinhua/ExplainableNRS

⏩ Paper: https://arxiv.org/pdf/2306.07506v1.pdf

💨 Dataset: https://paperswithcode.com/dataset/mind

https://t.iss.one/DataScienceT

Get started in Data Science with Microsoft's FREE course for beginners.

- 10 weeks
- 20 lessons
- Lecture notes
- 100% FREE

https://microsoft.github.io/Data-Science-For-Beginners/

https://t.iss.one/DataScienceT

Fine-tuning MMS Adapter Models for Multi-Lingual ASR

MMS' Adapter training is both more memory efficient, more robust and yields better performance for low-resource languages.

🤗 Post: https://huggingface.co/blog/mms_adapters

🖥 Colab: https://colab.research.google.com/github/patrickvonplaten/notebooks/blob/master/Fine_Tune_MMS_on_Common_Voice.ipynb

🖥 Github: https://github.com/facebookresearch/fairseq/tree/main/examples/mms/asr

⭐️ Demo: https://huggingface.co/spaces/facebook/MMS

📕 Paper: https://huggingface.co/papers/2305.13516

https://t.iss.one/DataScienceT

Building Transformer Models with Attention Crash Course. Build a Neural Machine Translator in 12 Days

https://machinelearningmastery.com/building-transformer-models-with-attention-crash-course-build-a-neural-machine-translator-in-12-days/

https://t.iss.one/DataScienceT

⭐️ Text-Guided Adversarial Makeup 🫣

Novel facial privacy protection via adversarial latent codes. Makeup vs Face Recognition.

🌐 Review: https://t.ly/pBCP

🌐 Paper: arxiv.org/pdf/2306.10008.pdf

🔥 Code: github.com/fahadshamshad/Clip2Protect

https://t.iss.one/DataScienceT

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⭐️ LMFlow: An Extensible Toolkit for Finetuning and Inference of Large Foundation Models

Extensible and lightweight toolkit, LMFlow, which aims to simplify the finetuning and inference of general large foundation models.

🖥 Github: https://github.com/optimalscale/lmflow

⭐️ Demo: https://lmflow.com/

📕 Paper: https://arxiv.org/abs/2306.12420v1

🔗Dataset: https://paperswithcode.com/dataset/pubmedqa

Official channel:
https://t.iss.one/DataScienceM

Top Large Language Models based on the Elo rating, MT-Bench, and MMLU

https://t.iss.one/DataScienceT

CBOGlobalConvergenceAnalysis

🖥 Github: https://github.com/efficienttraining/labelbench

⏩ Paper: https://arxiv.org/pdf/2306.09778v1.pdf

https://t.iss.one/DataScienceT

Automatically find issues in image datasets and practice data-centric computer vision.

CleanVision automatically detects potential issues in image datasets like images that are: blurry, under/over-exposed, (near) duplicates, etc. This data-centric AI package is a quick first step for any computer vision project to find problems in the dataset, which you want to address before applying machine learning. CleanVision is super simple -- run the same couple lines of Python code to audit any image dataset!

https://github.com/cleanlab/cleanvision

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The Attention Mechanism from Scratch

https://machinelearningmastery.com/the-attention-mechanism-from-scratch/

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Introduction to Computer Architecture, IIT Delhi

🆓 Free Online Course
💻 38 Lecture Videos
⏰ 1 Module
🏃‍♂️ Self paced
Teacher 👨‍🏫 : Prof. Anshul Kumar

🔗 https://nptel.ac.in/courses/106102062

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