Book7_Visualizations-for-Machine-Learning
Visualize-ML
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Book_7_《机器学习》 | 鸢尾花书：从加减乘除到机器学习；欢迎批评指正
Maschinelles Lernen · GitHub
Finde Frameworks zum Trainieren, Bewerten und Ausführen von Modellen und vergleiche das Community-Interesse.
Nützliche GitHub-Projekte
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„Mathematik ist nicht schwer“: *Lineare Algebra ist nicht schwer*, Band 1 und 2, mit insgesamt 66 Themen. Kritik und Korrekturvorschläge sind willkommen.
sdnext
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SD.Next: All-in-one WebUI for AI generative image and video creation, captioning and processing
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modern-llm-notebook
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A hands-on course on modern LLM architectures, training, and inference, with step-by-step PyTorch implementations and runnable notebooks.
webdataset
webdataset
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Ein leistungsstarkes, Python-basiertes E/A-System für große (und kleine) Deep-Learning-Probleme mit umfassender PyTorch-Unterstützung.
Xsdsh-purge
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沿用了此项目：https://github.com/YuJunZhiXue/dsh-purge 专注于deepseek harness破甲|逆向|渗透| 适配最新版deepseek harness ：deepseek v4.1 flash 最新版 更新q群562341656
projectlearn-project-based-learning
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Eine kuratierte Liste von Projekttutorials für projektbasiertes Lernen.
DIS
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This is the repo for our new project Highly Accurate Dichotomous Image Segmentation
ml-road
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Grafisches Werkzeug und Deep-Learning-Framework zum Extrahieren fest ins Video eingebrannter Untertitel und Erstellen von SRT-Dateien. Es benötigt keine API eines Drittanbieters und führt die Texterkennung lokal aus. Es umfasst die Erkennung von Untertitelbereichen und die Extraktion der Untertitelinhalte.
QuantMuse
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A comprehensive quantitative trading system with AI-powered analysis, real-time data processing, and advanced risk management
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OpenMLDB
4paradigm
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OpenMLDB is an open-source machine learning database that provides a feature platform computing consistent features for training and inference.
pytorch-nested-unet
4uiiurz1
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PyTorch implementation of UNet++ (Nested U-Net).
approachingalmost
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mace
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MACE - Fast and accurate machine learning interatomic potentials with higher order equivariant message passing.
adapters
adapter-hub
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Machine-Learning-Links-And-Lessons-Learned
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List of all the lessons learned, best practices, and links from my time studying machine learning
- It's also model dependent. Normalization will likely help for linear models like linear/logistic reg, but won't be as helpful for deep learning models and neural networks.
- In order to determine whether it will help or not, visualize your data to see what type of features you're dealing with. Do you have some features that have values around 1000 or 2000? Do you have some that are only 0.1 or 0.2? If so, normalizing those inputs is likely a good idea.
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A modular, JAX-based framework for Physics-Informed Neural Networks, designed for scalable PDE-constrained learning with support for domain decomposition, attention mechanisms, and performance-oriented training.
face_recognition
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Die weltweit einfachste API zur Gesichtserkennung für Python und die Kommandozeile
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Plant-Disease-Classification
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A ResNet50-DCBAM deep learning framework for plant disease classification using the PlantVillage dataset with reproducible training and evaluation pipelines.
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Detailed and tailored guide for undergraduate students or anybody want to dig deep into the field of AI with solid foundation.
awesome-robotics
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A curated list of awesome links and software libraries that are useful for robots.
BloomBee
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Decentralized LLMs fine-tuning and inference with offloading
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