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##","Other ##","Transformers ##","Lidar ##"],"sub_categories":[],"readme":"# Awesome-Sources #\n\nLists of libraries, blogs, book and more that are found useful.\nThis list is inspired by [awesome](https://github.com/sindresorhus/awesome).\nTo build this list i used [Markdown Cheatsheet](https://github.com/tchapi/markdown-cheatsheet/blob/master/README.md), [Mastering Markdown](https://guides.github.com/features/mastering-markdown/) and the offical github [Basic writing and formatting syntax](https://docs.github.com/en/github/writing-on-github/basic-writing-and-formatting-syntax).\n\nBasic and common sources or libraries such as [Pytorch](https://pytorch.org/) are not listed.\n\n# Books, Articles and Codes #\n* [Papers with code](https://paperswithcode.com/): The mission of Papers with Code is to create a free and open resource with Machine Learning papers, code and evaluation tables.\n## Audio ##\n* [Audio labeling guide for audicity](https://towardsdatascience.com/how-to-label-audio-for-deep-learning-in-4-simple-steps-6a2c33b343e6): How to Label Audio for Deep Learning in 4 Simple Steps + code that i didnt noticed until i finished mine....\n* [Audio fingerprinting](https://willdrevo.com/fingerprinting-and-audio-recognition-with-python/): Audio Fingerprinting with Python and Numpy [dejavu](https://github.com/worldveil/dejavu).\n* [Freezam](https://github.com/Lizzi-Busy/freezam): A Shazam-like Audio Recognition Algorithm.\n* [AudioSearch](https://github.com/lukemcraig/AudioSearch): Python implementation of \"[An Industrial-Strength Audio Search Algorithm](https://www.ee.columbia.edu/~dpwe/papers/Wang03-shazam.pdf)\"\n* [kaggle Bird audio detection 1st place solution](https://www.kaggle.com/c/bird-audio-detection/discussion/202149): Detect if a short audio segment has a bird sound in it.\n* [Introduction to Audio Classification with Deep Neural Networks](https://github.com/IliaZenkov/sklearn-audio-classification): This notebook serves as an introduction to working with audio data for classification problems; it is meant as a learning resource rather than a demonstration of the state-of-the-art.\n* [music informatio nretrieval](https://musicinformationretrieval.com/): musicinformationretrieval.com is a collection of instructional materials for music information retrieval (MIR). These materials contain a mix of casual conversation, technical discussion, and Python code.\n\n# Python Libraires #\n* [Plotly](https://plotly.com/python/): A Python Open Source Graphing Library.\n* [CleanLab](https://docs.cleanlab.ai/v2.0.0/index.html): cleanlab automatically finds and fixes label issues in your ML datasets.\n* [Dataclasses JSON](https://lidatong.github.io/dataclasses-json/): This library provides a simple API for encoding and decoding dataclasses to and from JSON. It's very easy to get started.\n* [loguru](https://github.com/Delgan/loguru): Loguru is a library which aims to bring enjoyable logging in Python.\n* [dataclasses](https://docs.python.org/3/library/dataclasses.html): This module provides a decorator and functions for automatically adding generated special methods such as __init__() and __repr__() to user-defined classes. It was originally described in PEP 557.\n* [ClearML](https://github.com/allegroai/clearml): ClearML - Auto-Magical Suite of tools to streamline your ML workflow Experiment Manager, ML-Ops and Data-Management. [Doc](https://allegro.ai/clearml/docs/).\n## Audio ##\n* [Librosa](https://librosa.org/doc/0.8.0/index.html): python package for music and audio analysis. \n* [Asteroid](https://asteroid-team.github.io/): The PyTorch-based audio source separation toolkit for researchers [Asteroid-github](https://github.com/asteroid-team/asteroid).\n* [Pyroomacoustics](https://pyroomacoustics.readthedocs.io/en/pypi-release/index.html): a software package aimed at the rapid development and testing of audio array processing algorithms.\n* [pyAudioAnalysis](https://github.com/tyiannak/pyAudioAnalysis): pyAudioAnalysis is a Python library covering a wide range of audio analysis tasks. Extract audio features and representations (e.g. mfccs, spectrogram, chromagram).\n* [Python for Scientific Audio](https://github.com/faroit/awesome-python-scientific-audio): The aim of this repository is to create a comprehensive, curated list of python software/tools related and used for scientific research in audio/music applications.\n* [noise reduce](https://github.com/timsainb/noisereduce): Noise reduction in python using spectral gating.\n* [Auditok](https://github.com/amsehili/auditok): Audio Activity Detection tool that can process online data (read from an audio device or from standard input) as well as audio files. It can be used as a command-line program or by calling its API\n* \n## Lidar ##\n* [Lidar](https://pypi.org/project/lidar/): lidar is Python package for delineating the nested hierarchy of surface depressions in digital elevation models (DEMs).\n* [LaserChicicken](https://github.com/ElsevierSoftwareX/SOFTX_2019_325): oolkit for handling point clouds created using airborne laser scanning (ALS). [Laserchicken—A tool for distributed feature calculation from massive LiDAR point cloud datasets](https://www.sciencedirect.com/science/article/pii/S2352711020303393).\n## Other ##\n# Machine Learning #\n* [Awesome Machine Learning](https://github.com/josephmisiti/awesome-machine-learning#python-computer-vision): A curated list of awesome machine learning frameworks, libraries and software (by language). \n# Deep Learning #\n## Transformers ##\n* [How the Vision Transformer (ViT) works in 10 minutes](https://theaisummer.com/vision-transformer/): an image is worth 16x16 words\n* [Lil'Log](https://lilianweng.github.io/lil-log/): Great blog by Lilian Wengweng from OpenAI about transforners and deep learning.\n* [Attention? Attention!](https://lilianweng.github.io/posts/2018-06-24-attention/): Attention is, to some extent, motivated by how we pay visual attention to different regions of an image or correlate words in one sentence\n* [UvA Deep Learning Tutorials!](https://uvadlc-notebooks.readthedocs.io/en/latest/index.html): Great tutorials on deep learning!!! with code!\n* [Vision Transformers from Scratch (PyTorch): A step-by-step guide](https://medium.com/mlearning-ai/vision-transformers-from-scratch-pytorch-a-step-by-step-guide-96c3313c2e0c): Vision Transformers from Scratch (PyTorch): A step-by-step guide.\n* [Multi-head attention mechanism](https://data-science-blog.com/blog/2021/04/07/multi-head-attention-mechanism/): Great blog and visualiztions! \n* [Illustrated: Self-Attention](https://towardsdatascience.com/illustrated-self-attention-2d627e33b20a): Transformer with nice visualiztion.\n* [VISION TRANSFORMERS (VIT)](https://dkamatblog.home.blog/2021/08/05/vision-transformers-vit/): Amazing blog with code in ptyorch!\n* [Colab geart ViT tutorial](https://colab.research.google.com/github/hirotomusiker/schwert_colab_data_storage/blob/master/notebook/Vision_Transformer_Tutorial.ipynb#scrollTo=txIf-L2Fwoxy): great notebook about ViT!\n* [Transformers Everywhere - Patch Encoding Technique for Vision Transformers(ViT) Explained](https://gowrishankar.info/blog/transformers-everywhere-patch-encoding-technique-for-vision-transformersvit-explained/): Great blog with code!\n* [Deep Learning Visuals patch embbeding](https://dvgodoy.github.io/dl-visuals/Patch%20Embeddings/): Great patch embbeding visualiztion.\n* [Pytorch book](https://github.com/dvgodoy/PyTorchStepByStep): Book about pyTorch with code and images.\n* [Nice ViT summry](https://www.analyticsvidhya.com/blog/2021/03/an-image-is-worth-16x16-words-transformers-for-image-recognition-at-scale-vision-transformers/): OK summery with code.\n* [Implementing Vision Transformer (ViT) in PyTorch](https://towardsdatascience.com/implementing-visualttransformer-in-pytorch-184f9f16f632): ViT with code\n* [The Illustrated Transformer](https://jalammar.github.io/illustrated-transformer/): very good blog and visualzition.\n* [Vision Transformers (ViT) in Image Recognition – 2022 Guide](https://viso.ai/deep-learning/vision-transformer-vit/): Really nice blog!\n* [Google I BLOG ABOT TOKENS](https://ai.googleblog.com/2021/12/improving-vision-transformer-efficiency.html): Improving Vision Transformer Efficiency and Accuracy by Learning to Tokenize.\n* \n## Audio ##\n* [Deep Learning for Music (DL4M)](https://github.com/ashishpatel26/Best-Audio-Classification-Resources-with-Deep-learning): TL;DR Non-exhaustive list of scientific articles on deep learning for music: summary (Article title, pdf link and code), details (table - more info), details (bib - all info)\n# Annotation Tools #\n* [Label Studio](https://labelstud.io/): Open Source Data Labeling Tool (Images, audio, text, timeseries and multi-domain).\n* [Label Audio for Deep Learning](https://towardsdatascience.com/how-to-label-audio-for-deep-learning-in-4-simple-steps-6a2c33b343e6): How to Label Audio for Deep Learning in 4 Simple Steps.\n# Lists of useful sources #\n* [Bird recognition - review of useful resources](https://github.com/AgaMiko/bird-recognition-review): A list of useful resources in the bird sound recognition - bird songs \u0026 calls\n","projects_url":"https://awesome.ecosyste.ms/api/v1/lists/tomlevron%2Fawesome-sources/projects"}