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TensorFlow Tutorial for Time Series Prediction
머신러닝 · GitHub
모델 학습, 평가, 추론 프레임워크를 찾고 커뮤니티의 관심도를 비교하세요.
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Cloud native, scalable storage engine for various types of energy data.
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Build, train, deploy, scale and maintain deep learning models. Understand ML infrastructure and MLOps using hands-on examples.
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PIXIU
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This repository introduces PIXIU, an open-source resource featuring the first financial large language models (LLMs), instruction tuning data, and evaluation benchmarks to holistically assess financial LLMs. Our goal is to continually push forward the open-source development of financial artificial intelligence (AI).
- Open resources: PIXIU openly provides the financial LLM, instruction tuning data, and datasets included in the evaluation benchmark to encourage open research and transparency.
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Lab materials for the Full Stack Deep Learning Course
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Object tracking implemented with YOLOv4, DeepSort, and TensorFlow.
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:octocat: A curated awesome list of AI Startups in India & Machine Learning Interview Guide. Feel free to contribute!
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Multiple implementations for abstractive text summurization , using google colab
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Intelligent file organization with computer vision, audio analysis, chunking, proactive AI-powered analysis, interactive classification, and adaptive learning.
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Flexible Inference for Predictive Coding Networks in JAX.
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Toolkit for training and evaluating Self-Supervised Learning (SSL) frameworks for Speaker Verification (SV).
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conifer
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Fast inference of Boosted Decision Trees in FPGAs
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Awesome machine learning for combinatorial optimization papers.
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This my 3d printed robot arm project
tbp.monty
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GeoCode
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GeoCode maps 3D shapes to a human-interpretable parameter space, allowing to intuitively edit the recovered 3D shapes from a point cloud or sketch input.
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A probabilistic programming library for Bayesian deep learning, generative models, based on Tensorflow
SwissArmyTransformer
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SwissArmyTransformer is a flexible and powerful library to develop your own Transformer variants.
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- GPT and other auto-regressive models act differently during training and inference. During inference, text is generated token-by-token and we need to cache previous states for efficiency. With our lib, you only need to consider the behavior during training (teacher-forcing) and transform it to a cached auto-regressive model via adding a mixin:
Anomaly-Transformer
thuml
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About Code release for "Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy" (ICLR 2022 Spotlight), https://openreview.net/forum?id=LzQQ89U1qm_
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About Code release for "Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting" (NeurIPS 2021), https://arxiv.org/abs/2106.13008
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A curated list of DATASETS, CODEBASES and PAPERS on Multi-Task Learning (MTL), from Machine Learning perspective.