Merge branch 'master' into master

README.md

@@ -54,7 +54,8 @@
 21. [Deep Learning with R, Second Edition](https://www.manning.com/books/deep-learning-with-r-second-edition) - by François Chollet with Tomasz Kalinowski and J. J. Allaire
 22. [Regularization in Deep Learning](https://www.manning.com/books/regularization-in-deep-learning) - by Liu Peng
 23. [Jax in Action](https://www.manning.com/books/jax-in-action) - by Grigory Sapunov
- 
+24. [Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow](https://www.knowledgeisle.com/wp-content/uploads/2019/12/2-Aur%C3%A9lien-G%C3%A9ron-Hands-On-Machine-Learning-with-Scikit-Learn-Keras-and-Tensorflow_-Concepts-Tools-and-Techniques-to-Build-Intelligent-Systems-O%E2%80%99Reilly-Media-2019.pdf) by Aurélien Géron  | Oct 15, 2019
+
 ### Courses
 
 1.  [Machine Learning - Stanford](https://class.coursera.org/ml-005) by Andrew Ng in Coursera (2010-2014)
@@ -97,9 +98,12 @@
 37. [Grokking Deep Learning in Motion](https://www.manning.com/livevideo/grokking-deep-learning-in-motion) by Beau Carnes (2018)
 38. [Face Detection with Computer Vision and Deep Learning](https://www.udemy.com/share/1000gAA0QdcV9aQng=/) by Hakan Cebeci
 39. [Deep Learning Online Course list at Classpert](https://classpert.com/deep-learning) List of Deep Learning online courses (some are free) from Classpert Online Course Search
-40. [AWS Machine Learning](https://aws.training/machinelearning) Machine Learning and Deep Learning Courses from Amazon's Machine Learning unviersity
+40. [AWS Machine Learning](https://aws.training/machinelearning) Machine Learning and Deep Learning Courses from Amazon's Machine Learning university
 41. [Intro to Deep Learning with PyTorch](https://www.udacity.com/course/deep-learning-pytorch--ud188) - A great introductory course on Deep Learning by Udacity and Facebook AI
 42. [Deep Learning by Kaggle](https://www.kaggle.com/learn/deep-learning) - Kaggle's  free course on Deep Learning
+43. [Yann LeCun’s Deep Learning Course at CDS](https://cds.nyu.edu/deep-learning/) - DS-GA 1008 · SPRING 2021 
+44. [Neural Networks and Deep Learning](https://webcms3.cse.unsw.edu.au/COMP9444/19T3/) - COMP9444 19T3
+45. [Deep Learning A.I.Shelf](http://aishelf.org/category/ia/deep-learning/)
 
 ### Videos and Lectures
 
@@ -190,6 +194,10 @@
 46. [Siamese Neural Networks for One-shot Image Recognition](https://www.cs.cmu.edu/~rsalakhu/papers/oneshot1.pdf)
 47. [Unsupervised Translation of Programming Languages](https://arxiv.org/pdf/2006.03511.pdf)
 48. [Matching Networks for One Shot Learning](http://papers.nips.cc/paper/6385-matching-networks-for-one-shot-learning.pdf)
+49. [VOLO: Vision Outlooker for Visual Recognition](https://arxiv.org/pdf/2106.13112.pdf)
+50. [ViT: An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://arxiv.org/pdf/2010.11929.pdf)
+51. [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](http://proceedings.mlr.press/v37/ioffe15.pdf)
+52. [DeepFaceDrawing: Deep Generation of Face Images from Sketches](http://geometrylearning.com/paper/DeepFaceDrawing.pdf?fbclid=IwAR0colWFHPGBCB1APZq9JVsWeWtmeZd9oCTNQvR52T5PRUJP_dLOwB8pt0I)
 
 ### Tutorials
 
@@ -214,7 +222,7 @@
 18. [Deep Learning with Python](https://www.manning.com/books/deep-learning-with-python)
 19. [Grokking Deep Learning](https://www.manning.com/books/grokking-deep-learning)
 20. [Deep Learning for Search](https://www.manning.com/books/deep-learning-for-search)
-21. [Keras Tutorial: Content Based Image Retrieval Using a Convolutional Denoising Autoencoder](https://blog.sicara.com/keras-tutorial-content-based-image-retrieval-convolutional-denoising-autoencoder-dc91450cc511)
+21. [Keras Tutorial: Content Based Image Retrieval Using a Convolutional Denoising Autoencoder](https://medium.com/sicara/keras-tutorial-content-based-image-retrieval-convolutional-denoising-autoencoder-dc91450cc511)
 22. [Pytorch Tutorial by Yunjey Choi](https://github.com/yunjey/pytorch-tutorial)
 23. [Understanding deep Convolutional Neural Networks with a practical use-case in Tensorflow and Keras](https://ahmedbesbes.com/understanding-deep-convolutional-neural-networks-with-a-practical-use-case-in-tensorflow-and-keras.html)
 24. [Overview and benchmark of traditional and deep learning models in text classification](https://ahmedbesbes.com/overview-and-benchmark-of-traditional-and-deep-learning-models-in-text-classification.html)
@@ -357,7 +365,7 @@
 16.  [nrl.navy.mil/itd/aic](http://www.nrl.navy.mil/itd/aic/)
 17.  [hips.seas.harvard.edu](http://hips.seas.harvard.edu/)
 18.  [AI Weekly](http://aiweekly.co)
-19.  [stat.ucla.edu](http://www.stat.ucla.edu/~junhua.mao/m-RNN.html)
+19.  [stat.ucla.edu](http://statistics.ucla.edu/)
 20.  [deeplearning.cs.toronto.edu](http://deeplearning.cs.toronto.edu/i2t)
 21.  [jeffdonahue.com/lrcn/](http://jeffdonahue.com/lrcn/)
 22.  [visualqa.org](http://www.visualqa.org/)
@@ -376,6 +384,7 @@
 35.  [AI Hub - supported by AAAI, NeurIPS](https://aihub.org/)
 36.  [CatalyzeX: Machine Learning Hub for Builders and Makers](https://www.catalyzeX.com)
 37.  [The Epic Code](https://theepiccode.com/)
+38.  [all AI news](https://allainews.com/)
 
 ### Datasets
 
@@ -423,7 +432,7 @@
 47. [German Fingerspelling Database](http://www-i6.informatik.rwth-aachen.de/~dreuw/database.html) - The database contains 35 gestures and consists of 1400 image sequences that contain gestures of 20 different persons recorded under non-uniform daylight lighting conditions. (Formats: mpg,jpg)  
 48. [Language Processing and Pattern Recognition](http://www-i6.informatik.rwth-aachen.de/)
 50. [Groningen Natural Image Database](http://hlab.phys.rug.nl/archive.html) - 4000+ 1536x1024 (16 bit) calibrated outdoor images (Formats: homebrew)
-51. [ICG Testhouse sequence](http://www.icg.tu-graz.ac.at/~schindler/Data) -  2 turntable sequences from ifferent viewing heights, 36 images each, resolution 1000x750, color (Formats: PPM)
+51. [ICG Testhouse sequence](http://www.icg.tu-graz.ac.at/~schindler/Data) -  2 turntable sequences from different viewing heights, 36 images each, resolution 1000x750, color (Formats: PPM)
 52. [Institute of Computer Graphics and Vision](http://www.icg.tu-graz.ac.at)
 54. [IEN Image Library](http://www.ien.it/is/vislib/) - 1000+ images, mostly outdoor sequences (Formats: raw, ppm)  
 55. [INRIA's Syntim images database](http://www-rocq.inria.fr/~tarel/syntim/images.html) - 15 color image of simple objects (Formats: gif)
@@ -500,6 +509,15 @@
 138. [Fashion-MNIST](https://github.com/zalandoresearch/fashion-mnist) - MNIST like fashion product dataset consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image, associated with a label from 10 classes.
 139. [Large-scale Fashion (DeepFashion) Database](http://mmlab.ie.cuhk.edu.hk/projects/DeepFashion.html) - Contains over 800,000 diverse fashion images.  Each image in this dataset is labeled with 50 categories, 1,000 descriptive attributes, bounding box and clothing landmarks
 140. [FakeNewsCorpus](https://github.com/several27/FakeNewsCorpus) - Contains about 10 million news articles classified using [opensources.co](http://opensources.co) types
+141. [LLVIP](https://github.com/bupt-ai-cz/LLVIP) - 15488 visible-infrared paired images (30976 images) for low-light vision research, [Project_Page](https://bupt-ai-cz.github.io/LLVIP/)
+142. [MSDA](https://github.com/bupt-ai-cz/Meta-SelfLearning) - Over over 5 million images from 5 different domains for multi-source ocr/text recognition DA research, [Project_Page](https://bupt-ai-cz.github.io/Meta-SelfLearning/)
+143. [SANAD: Single-Label Arabic News Articles Dataset for Automatic Text Categorization](https://data.mendeley.com/datasets/57zpx667y9/2) - SANAD Dataset is a large collection of Arabic news articles that can be used in different Arabic NLP tasks such as Text Classification and Word Embedding. The articles were collected using Python scripts written specifically for three popular news websites: AlKhaleej, AlArabiya and Akhbarona. 
+144. [Referit3D](https://referit3d.github.io) - Two large-scale and complementary visio-linguistic datasets (aka Nr3D and Sr3D) for identifying fine-grained 3D objects in ScanNet scenes. Nr3D contains 41.5K natural, free-form utterances, and Sr3d contains 83.5K template-based utterances.
+145. [SQuAD](https://rajpurkar.github.io/SQuAD-explorer/) - Stanford released ~100,000 English QA pairs and ~50,000 unanswerable questions
+146. [FQuAD](https://fquad.illuin.tech/) - ~25,000 French QA pairs released by Illuin Technology
+147. [GermanQuAD and GermanDPR](https://www.deepset.ai/germanquad) - deepset released ~14,000 German QA pairs
+148. [SberQuAD](https://github.com/annnyway/QA-for-Russian) - Sberbank released ~90,000 Russian QA pairs
+149. [ArtEmis](http://artemisdataset.org/) - Contains 450K affective annotations of emotional responses and linguistic explanations for 80,000 artworks of WikiArt.
 
 ### Conferences
 
@@ -585,20 +603,30 @@
 64.  [Trax — Deep Learning with Clear Code and Speed](https://github.com/google/trax)
 65.  [Flax - a neural network ecosystem for JAX that is designed for flexibility](https://github.com/google/flax)
 66.  [QuickVision](https://github.com/Quick-AI/quickvision)
+67.  [Colossal-AI - An Integrated Large-scale Model Training System with Efficient Parallelization Techniques](https://github.com/hpcaitech/ColossalAI)
+68.  [haystack: an open-source neural search framework](https://haystack.deepset.ai/docs/intromd)
+69.  [Maze](https://github.com/enlite-ai/maze) - Application-oriented deep reinforcement learning framework addressing real-world decision problems.
+70.  [InsNet - A neural network library for building instance-dependent NLP models with padding-free dynamic batching](https://github.com/chncwang/InsNet)
 
 ### Tools
 
-1.  [Netron](https://github.com/lutzroeder/netron) - Visualizer for deep learning and machine learning models
+1.  [Nebullvm](https://github.com/nebuly-ai/nebullvm) - Easy-to-use library to boost deep learning inference leveraging multiple deep learning compilers.
+2.  [Netron](https://github.com/lutzroeder/netron) - Visualizer for deep learning and machine learning models
 2.  [Jupyter Notebook](http://jupyter.org) - Web-based notebook environment for interactive computing
 3.  [TensorBoard](https://github.com/tensorflow/tensorboard) - TensorFlow's Visualization Toolkit
-4.  [Visual Studio Tools for AI](https://visualstudio.microsoft.com/downloads/ai-tools-vs) - Develop, debug and deploy deep learning and AI solutions
+4.  [Visual Studio Tools for AI](https://www.microsoft.com/en-us/research/project/visual-studio-code-tools-ai/) - Develop, debug and deploy deep learning and AI solutions
 5.  [TensorWatch](https://github.com/microsoft/tensorwatch) - Debugging and visualization for deep learning
 6. [ML Workspace](https://github.com/ml-tooling/ml-workspace) - All-in-one web-based IDE for machine learning and data science.
 7.  [dowel](https://github.com/rlworkgroup/dowel) - A little logger for machine learning research. Log any object to the console, CSVs, TensorBoard, text log files, and more with just one call to `logger.log()`
-8.  [Neptune](https://neptune.ml/) - Lightweight tool for experiment tracking and results visualization. 
+8.  [Neptune](https://neptune.ai/) - Lightweight tool for experiment tracking and results visualization. 
 9.  [CatalyzeX](https://chrome.google.com/webstore/detail/code-finder-for-research/aikkeehnlfpamidigaffhfmgbkdeheil) - Browser extension ([Chrome](https://chrome.google.com/webstore/detail/code-finder-for-research/aikkeehnlfpamidigaffhfmgbkdeheil) and [Firefox](https://addons.mozilla.org/en-US/firefox/addon/code-finder-catalyzex/)) that automatically finds and links to code implementations for ML papers anywhere online: Google, Twitter, Arxiv, Scholar, etc.
 10. [Determined](https://github.com/determined-ai/determined) - Deep learning training platform with integrated support for distributed training, hyperparameter tuning, smart GPU scheduling, experiment tracking, and a model registry.
 11. [DAGsHub](https://dagshub.com/) - Community platform for Open Source ML – Manage experiments, data & models and create collaborative ML projects easily.
+12. [hub](https://github.com/activeloopai/Hub) - Fastest unstructured dataset management for TensorFlow/PyTorch by activeloop.ai. Stream & version-control data. Converts large data into single     numpy-like array on the cloud, accessible on any machine.
+13. [DVC](https://dvc.org/) - DVC is built to make ML models shareable and reproducible. It is designed to handle large files, data sets, machine learning models, and metrics as well as code.
+14. [CML](https://cml.dev/) - CML helps you bring your favorite DevOps tools to machine learning.
+15. [MLEM](https://mlem.ai/) - MLEM is a tool to easily package, deploy and serve Machine Learning models. It seamlessly supports a variety of scenarios like real-time serving and batch processing.
+
 
 ### Miscellaneous
 
@@ -643,6 +671,7 @@
 40. [toolbox: Curated list of ML libraries](https://github.com/amitness/toolbox)
 41. [CNN Explainer](https://poloclub.github.io/cnn-explainer/)
 42. [AI Expert Roadmap](https://github.com/AMAI-GmbH/AI-Expert-Roadmap) - Roadmap to becoming an Artificial Intelligence Expert
+43. [Awesome Drug Interactions, Synergy, and Polypharmacy Prediction](https://github.com/AstraZeneca/awesome-polipharmacy-side-effect-prediction/)
 
 -----
 ### Contributing