Book: The Little Book of
Deep Learning
Authors: François Fleuret
ISBN: Null
year: 2023
pages: 168
Tags:#DL
@Machine_learn

Human-Guided Complexity-Controlled Abstractions

🖥 Github: https://github.com/mycal-tucker/human-guided-abstractions

📕 Paper: https://arxiv.org/pdf/2310.17550v1.pdf

🔥 Datasets: https://paperswithcode.com/dataset/fashion-mnist

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🕵️ Detecting Pretraining Data from Large Language Models


🖥 Github: https://github.com/swj0419/detect-pretrain-code

📕 Paper: https://arxiv.org/pdf/2310.16789.pdf

📘 WikiMIA Benchmark:

⏩ Project: https://swj0419.github.io/detect-pretrain.github.io/

@Machine_learn

DialogLLMScenic

🖥 Github: https://github.com/avmb/dialogllmscenic

📕 Paper: https://arxiv.org/pdf/2310.17372v1.pdf

🔥 Datasets: https://paperswithcode.com/dataset/carla

⭐ Tasks: https://paperswithcode.com/task/self-driving-cars

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Book: Foundation Python for Data Scientist
Authors: Kennedy R
ISBN: Null
year: 202
pages: 686
Tags:#Python
@Machine_learn

⚡️ LLMRec: Large Language Models with Graph Augmentation for Recommendation

LLMRec - это новый фреймворк и датасет, улучшающий рекомендательные системы путем применения простых, но эффективных стратегий дополнения графов на основе LLM.

🖥 Github: https://github.com/hkuds/llmrec

📕 Paper: https://arxiv.org/abs/2311.00423v1

⏩ Project: https://llmrec.github.io/

🌐 Dataset: https://llmrec.github.io/#

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Book: Deep Learning Foundations and Concepts
Authors: Christopher M. Bishop
ISBN: 978-3-031-45468-4
year: 2023
pages: 656
Tags: #DL
@Machine_learn

🚀 Introducing YOLO-NAS Pose : A Game-Changer in Pose Estimation 🚀


🖥 Github: https://github.com/Deci-AI/super-gradients

📕 Notebook: https://colab.research.google.com/drive/1O4N5Vbzv0rfkT81LQidPktX8RtoS5A40

🚀 Demo: https://huggingface.co/spaces/Deci/YOLO-NAS-Pose-Demo

🌐 Colab: https://colab.research.google.com/drive/1agLj0aGx48C_rZPrTkeA18kuncack6lF

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✨ Feature Selection for Deep Tabular Models


🐱 Github: https://github.com/vcherepanova/tabular-feature-selection

📕 Paper: https://arxiv.org/pdf/2311.05877v1.pdf

⭐ Tasks: https://paperswithcode.com/task/feature-selection

@Machine_learn

🏆 LLaMA2-Accessory: An Open-source Toolkit for LLM Development

🐱 Github: https://github.com/alpha-vllm/llama2-accessory

🚀 Demo: http://imagebind-llm.opengvlab.com/

📕 Paper: https://arxiv.org/abs/2311.07575v1

⏩ Project: llama2-accessory.readthedocs.io/

⭐ Dataset: https://paperswithcode.com/dataset/vsr

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Quantized Distillation for Driver Activity Recognition

🖥 Github: https://github.com/calvintanama/qd-driver-activity-reco

📕 Paper: https://arxiv.org/pdf/2311.05970v1.pdf

🔥 Datasets: https://paperswithcode.com/dataset/drive-act

✨ Tasks: https://paperswithcode.com/task/activity-recognition

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Book: A brief introduction to Neural Networks
Authors: David Kriesel
ISBN: Null
year: 2005
pages: 242
Tags: #ANN
@Machine_learn

Hyperparameter Optimization and Combined Data Sampling Techniques in Machine Learning for Customer Churn Prediction: A Comparative Analysis


📕 Paper: https://www.preprints.org/manuscript/202308.1478/v4

🔥 Dataset: https://www.kaggle.com/code/rinichristy/customer-churn-prediction-2020
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🇺🇿 Introducing Emu Video and Emu Edit, our latest generative AI research milestones


🚀 Blog: https://ai.meta.com/blog/emu-text-to-video-generation-image-editing-research/

⭐️Project page: https://emu-edit.metademolab.com

📌Paper: https://emu-edit.metademolab.com/assets/emu_edit.pdf

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🪐 ARES: An Automated Evaluation Framework for Retrieval-Augmented Generation Systems


🐱 Github: https://github.com/stanford-futuredata/ares

📕 Paper: https://arxiv.org/abs/2311.09476

⏩ Dataset: https://paperswithcode.com/dataset/kilt

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🌦 Makani: Massively parallel training of machine-learning based weather and climate models


🐱Github: https://github.com/NVIDIA/makani

📕Blog: https://developer.nvidia.com/blog/modeling-earths-atmosphere-with-spherical-fourier-neural-operators/

⏩Dataset: https://github.com/NVIDIA/makani/tree/main/datasets

@Machine_learn