diff --git a/README.md b/README.md index de70cdd..cb7b032 100644 --- a/README.md +++ b/README.md @@ -348,7 +348,6 @@ Further resources: #### General-Purpose Machine Learning * [Disco](https://github.com/discoproject/disco/) - Map Reduce in Erlang. **[Deprecated]** -* [Yanni](https://bitbucket.org/nato/yanni/overview) - ANN neural networks using Erlangs leightweight processes. ## Fortran @@ -463,7 +462,6 @@ Read the paper [here](https://arxiv.org/abs/1902.06714). * [Stanford Tokens Regex](https://nlp.stanford.edu/software/tokensregex.shtml) - A tokenizer divides text into a sequence of tokens, which roughly correspond to "words". * [Stanford Temporal Tagger](https://nlp.stanford.edu/software/sutime.shtml) - SUTime is a library for recognizing and normalizing time expressions. * [Stanford SPIED](https://nlp.stanford.edu/software/patternslearning.shtml) - Learning entities from unlabeled text starting with seed sets using patterns in an iterative fashion. -* [Stanford Topic Modeling Toolbox](https://nlp.stanford.edu/software/tmt) - Topic modeling tools to social scientists and others who wish to perform analysis on datasets. * [Twitter Text Java](https://github.com/twitter/twitter-text/tree/master/java) - A Java implementation of Twitter's text processing library. * [MALLET](http://mallet.cs.umass.edu/) - A Java-based package for statistical natural language processing, document classification, clustering, topic modeling, information extraction, and other machine learning applications to text. * [OpenNLP](https://opennlp.apache.org/) - a machine learning based toolkit for the processing of natural language text. @@ -953,7 +951,7 @@ be * [Detectron](https://github.com/facebookresearch/Detectron) - FAIR's software system that implements state-of-the-art object detection algorithms, including Mask R-CNN. It is written in Python and powered by the Caffe2 deep learning framework. **[Deprecated]** * [detectron2](https://github.com/facebookresearch/detectron2) - FAIR's next-generation research platform for object detection and segmentation. It is a ground-up rewrite of the previous version, Detectron, and is powered by the PyTorch deep learning framework. * [albumentations](https://github.com/albu/albumentations) - А fast and framework agnostic image augmentation library that implements a diverse set of augmentation techniques. Supports classification, segmentation, detection out of the box. Was used to win a number of Deep Learning competitions at Kaggle, Topcoder and those that were a part of the CVPR workshops. -* [pytessarct](https://github.com/madmaze/pytesseract) - Python-tesseract is an optical character recognition (OCR) tool for python. That is, it will recognize and "read" the text embedded in images.Python-tesseract is a wrapper for [Google's Tesseract-OCR Engine](https://github.com/tesseract-ocr/tesseract)>. +* [pytessarct](https://github.com/madmaze/pytesseract) - Python-tesseract is an optical character recognition (OCR) tool for python. That is, it will recognize and "read" the text embedded in images. Python-tesseract is a wrapper for [Google's Tesseract-OCR Engine](https://github.com/tesseract-ocr/tesseract). * [imutils](https://github.com/jrosebr1/imutils) - A library containg Convenience functions to make basic image processing operations such as translation, rotation, resizing, skeletonization, and displaying Matplotlib images easier with OpenCV and Python. * [PyTorchCV](https://github.com/donnyyou/PyTorchCV) - A PyTorch-Based Framework for Deep Learning in Computer Vision. * [Self-supervised learning](https://pytorch-lightning-bolts.readthedocs.io/en/latest/self_supervised_models.html) @@ -1058,14 +1056,13 @@ be * [CoverTree](https://github.com/patvarilly/CoverTree) - Python implementation of cover trees, near-drop-in replacement for scipy.spatial.kdtree **[Deprecated]** * [nilearn](https://github.com/nilearn/nilearn) - Machine learning for NeuroImaging in Python. * [neuropredict](https://github.com/raamana/neuropredict) - Aimed at novice machine learners and non-expert programmers, this package offers easy (no coding needed) and comprehensive machine learning (evaluation and full report of predictive performance WITHOUT requiring you to code) in Python for NeuroImaging and any other type of features. This is aimed at absorbing the much of the ML workflow, unlike other packages like nilearn and pymvpa, which require you to learn their API and code to produce anything useful. -* [imbalanced-learn](https://imbalanced-learn.org/en/stable/index.html) - Python module to perform under sampling and over sampling with various techniques. +* [imbalanced-learn](https://imbalanced-learn.org/stable/) - Python module to perform under sampling and over sampling with various techniques. * [Shogun](https://github.com/shogun-toolbox/shogun) - The Shogun Machine Learning Toolbox. * [Pyevolve](https://github.com/perone/Pyevolve) - Genetic algorithm framework. **[Deprecated]** * [Caffe](https://github.com/BVLC/caffe) - A deep learning framework developed with cleanliness, readability, and speed in mind. * [breze](https://github.com/breze-no-salt/breze) - Theano based library for deep and recurrent neural networks. * [Cortex](https://github.com/cortexlabs/cortex) - Open source platform for deploying machine learning models in production. * [pyhsmm](https://github.com/mattjj/pyhsmm) - library for approximate unsupervised inference in Bayesian Hidden Markov Models (HMMs) and explicit-duration Hidden semi-Markov Models (HSMMs), focusing on the Bayesian Nonparametric extensions, the HDP-HMM and HDP-HSMM, mostly with weak-limit approximations. -* [mrjob](https://pythonhosted.org/mrjob/) - A library to let Python program run on Hadoop. * [SKLL](https://github.com/EducationalTestingService/skll) - A wrapper around scikit-learn that makes it simpler to conduct experiments. * [neurolab](https://github.com/zueve/neurolab) * [Spearmint](https://github.com/HIPS/Spearmint) - Spearmint is a package to perform Bayesian optimization according to the algorithms outlined in the paper: Practical Bayesian Optimization of Machine Learning Algorithms. Jasper Snoek, Hugo Larochelle and Ryan P. Adams. Advances in Neural Information Processing Systems, 2012. **[Deprecated]** diff --git a/courses.md b/courses.md index 3f61469..69cdcf0 100644 --- a/courses.md +++ b/courses.md @@ -38,4 +38,6 @@ The following is a list of free or paid online courses on machine learning, stat * [Transfer Learning for Natural Language Processing](https://www.manning.com/books/transfer-learning-for-natural-language-processing) - $ * [Grokking Artificial Intelligence Algorithms](https://www.manning.com/books/grokking-artificial-intelligence-algorithms) - $ * [Machine Learning for Business](https://www.manning.com/books/machine-learning-for-business) - $ -* [Transfer Learning for Natural Language Processing](https://www.manning.com/books/transfer-learning-for-natural-language-processing?utm_source=github&utm_medium=organic&utm_campaign=book_azunre_transfer_3_10_20) - $ +* [Transfer Learning for Natural Language Processing](https://www.manning.com/books/transfer-learning-for-natural-language-processing) - $ +* [In-depth introduction to machine learning in 15 hours of expert videos (by Prof. Trevor Hastie, Prof. Rob Tibshirani, Stanford)](https://www.dataschool.io/15-hours-of-expert-machine-learning-videos/) - free +