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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🔥 Awesome-Multimodal-Large-Language-Models

Latest Papers and Datasets on Multimodal Large Language Models, and Their Evaluation.

🖥 Github: https://github.com/bradyfu/awesome-multimodal-large-language-models

📕 Paper: https://arxiv.org/abs/2306.13394v1

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

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🗒 Machine Learning Cheat Sheets

https://sites.google.com/view/datascience-cheat-sheets

Machine Learning Animations: https://sites.google.com/view/mlingifs#h.341bzfgiuxfx

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⚡ LightGlue. Local Feature Matching at Light Speed

LightGlue a lightweight feature matcher with high accuracy and adaptive pruning techniques, both in the width and depth of the network, for blazing fast inference.

git clone https://github.com/cvg/LightGlue.git && cd LightGlue
python -m pip install -e .

🖥 Github: https://github.com/cvg/lightglue

📕 Paper: https://arxiv.org/abs/2306.13643v1

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

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This channel is for Programmers, Coders, Software Engineers.

1- Data Science
2- Machine Learning
3- Data Visualization
4- Artificial Intelligence
5- Data Analysis
6- Statistics
7- Deep Learning

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🚶‍♂️ MotionGPT: Human Motion
as Foreign Language

MotionGPT consists of a motion tokenizer responsible for converting raw motion data into discrete motion tokens, as well as a motion-aware language model that learns to understand the motion tokens from large language pre-training models by corresponding textual denoscriptions.

⏩ Project: https://motion-gpt.github.io/

🖥 Github: https://github.com/openmotionlab/motiongpt

📕 Paper: https://arxiv.org/pdf/2306.14795.pdf

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

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🖥 Free Courses on Large Language Models

▪ChatGPT Prompt Engineering for Developers

▪LangChain for LLM Application Development

▪Building Systems with the ChatGPT API

▪Google Cloud Generative AI Learning Path

▪Introduction to Large Language Models with Google Cloud

▪LLM University

▪Full Stack LLM Bootcamp

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PANet: LiDAR Panoptic Segmentation with Sparse Instance Proposal and Aggregation

🖥 Github: https://github.com/jieqianyu/panet

⏩ Paper: https://arxiv.org/pdf/2306.15348v1.pdf

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

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💬 3D-Speaker: A Large-Scale Multi-Device, Multi-Distance, and Multi-Dialect Corpus for Speech Representation Disentanglement

A large-scale speech corpus to facilitate the research of speech representation

🖥 Github: https://github.com/alibaba-damo-academy/3D-Speaker

📕 Paper: https://arxiv.org/abs/2306.15354v1

🔗Dataset: https://3dspeaker.github.io/

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What the Brain Sees

How a text-to-image model generates images from brain scans

https://www.deeplearning.ai/the-batch/how-a-text-to-image-model-generates-images-from-brain-scans/

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The source code for DragGAN has been released! 🔥🔥🔥

We can finally play with that marvel!

🔗 GitHub repository: https://github.com/XingangPan/DragGAN

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📕 Constrained-Text-Generation-Studio

AI writing assistant for recreational linguists, poets, creative writers, and/or researchers to use and study the ability of large-scale language models.

🖥 Github: https://github.com/hellisotherpeople/constrained-text-generation-studio

📕 Paper: https://arxiv.org/abs/2306.15926v1

🔗Dataset: https://huggingface.co/datasets/Hellisotherpeople/Lipogram-e

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CellViT: Vision Transformers for Precise Cell Segmentation and Classification

🖥 Github: https://github.com/tio-ikim/cellvit

⏩ Paper: https://arxiv.org/pdf/2306.15350v1.pdf

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

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A special and important channel to download the most important books to learn programming and data science

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💬 GLIGEN: Open-Set Grounded Text-to-Image Generation

GLIGEN’s zero-shot performance on COCO and LVIS outperforms that of existing supervised layout-to-image baselines by a large margin. Code comming soon.

⭐️ Project: https://gligen.github.io/

⭐️ Demo: https://aka.ms/gligen

✅️ Paper: https://arxiv.org/abs/2301.07093

🖥 Github: https://github.com/gligen/GLIGEN

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🖥 RL4CO

🖥 Github: https://github.com/kaist-silab/rl4co

📕 Paper: https://arxiv.org/abs/2306.17100v1

🖥 Colab: https://colab.research.google.com/github/kaist-silab/rl4co/blob/main/notebooks/1-quickstart.ipynb

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🧍‍♂ BEDLAM: Bodies Exhibiting Detailed Lifelike Animated Motion

BEDLAM is useful for a variety of tasks and all images, ground truth bodies, 3D clothing, support code, and more are available for research purposes.

🖥 Github: https://github.com/pixelite1201/BEDLAM

📕 Paper: https://bedlam.is.tuebingen.mpg.de/media/upload/BEDLAM_CVPR2023.pdf

🔗Render code: https://github.com/PerceivingSystems/bedlam_render

🎞 Video: https://youtu.be/OBttHFwdtfI

👑 Dataset: https://paperswithcode.com/dataset/bedlam

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Python Machine Learning Projects (2023)

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⭐️ ManimML: Communicating Machine Learning Architectures with Animation

An open-source Python library for easily generating animations of ML algorithms directly from code.

from manim_ml.neural_network import NeuralNetwork, Convolutional2DLayer, FeedForwardLayer
# Make nn
nn = NeuralNetwork([
Convolutional2DLayer(1, 7, filter_spacing=0.32),
Convolutional2DLayer(3, 5, 3, filter_spacing=0.32, activation_function="ReLU"),
FeedForwardLayer(3, activation_function="Sigmoid"),
],
layer_spacing=0.25,
)
self.add(nn)
# Play animation
forward_pass = nn.make_forward_pass_animation()
self.play(forward_pass)

🖥 Github: https://github.com/helblazer811/manimml

📕 Paper: https://arxiv.org/abs/2306.17108v1

📌 Project: https://www.manim.community/

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