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- 10-414/714: Deep Learning Systems
- Well-documented Python demonstrations for spatial data analytics, geostatistical and machine learning to support my courses. - PythonNumericalDemos/Interactive_Gibbs_Sampler.ipynb at master · GeostatsGuy/PythonNumericalDemos
- The Grid Corpus is a large multitalker audiovisual sentence corpus designed to support joint computational-behavioral studies in speech perception. In brief, the corpus consists of high-quality audio and video (facial) recordings of 1000 sentences spoken by each of 34 talkers (18 male, 16 female), for a total of 34000 sentences. Sentences are of the form "put red at G9 now". audio_25k.zip contains the wav format utterances at a 25 kHz sampling rate in a separate directory per talker alignments.zip provides word-level time alignments, again separated by talker s1.zip, s2.zip etc contain .jpg videos for each talker [note that due to an oversight, no video for talker t21 is available] The Grid Corpus is described in detail in the paper jasagrid.pdf included in the dataset.
- Python implementations of the Boruta all-relevant feature selection method. - GitHub - scikit-learn-contrib/boruta_py: Python implementations of the Boruta all-relevant feature selection method.
- IPython notebooks with demo code intended as a companion to the book "Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control" by Steven L. Brunton and J. Nathan Kutz - GitHub - dynamicslab/databook_python: IPython notebooks with demo code intended as a companion to the book "Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control" by Steven L. Brunton and J. Nathan Kutz
- Port of OpenAI's Whisper model in C/C++. Contribute to ggerganov/whisper.cpp development by creating an account on GitHub.
- Deep Learning Fundamentals -- Code material and exercises - GitHub - Lightning-AI/dl-fundamentals: Deep Learning Fundamentals -- Code material and exercises
- STAT 451: Intro to Machine Learning @ UW-Madison (Fall 2020) - GitHub - rasbt/stat451-machine-learning-fs20: STAT 451: Intro to Machine Learning @ UW-Madison (Fall 2020)
- A recipe to study Graph Neural Networks (GNNs). Contribute to dair-ai/GNNs-Recipe development by creating an account on GitHub.
- This workshop teaches you the fundamental tools and techniques for running GPU-accelerated Python applications using CUDA.
- Teaching material for the course of Deep Learning and Applied AI, 2nd semester 2020, Sapienza University of Rome
- A free course in biological modeling at multiple scales.
- notebook du papier https://arxiv.org/pdf/2004.08531.pdf
- A data augmentations library for audio, image, text, and video. - facebookresearch/AugLy
- Pytorch implementation of the paper : A Global-local Attention Framework for Weakly Labelled Audio Tagging. - WangHelin1997/GL-AT
- Lectures for INFO8010 - Deep Learning, ULiège. Contribute to glouppe/info8010-deep-learning development by creating an account on GitHub.
- CVXPY creates powerful new PyTorch and TensorFlow layers
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