Merge branch 'master' into referit3d_dataset

README.md

@@ -91,7 +91,8 @@
 40. [AWS Machine Learning](https://aws.training/machinelearning) Machine Learning and Deep Learning Courses from Amazon's Machine Learning unviersity
 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
 ### Videos and Lectures
 
 1.  [How To Create A Mind](https://www.youtube.com/watch?v=RIkxVci-R4k) By Ray Kurzweil
@@ -172,6 +173,8 @@
 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)
 
 ### Tutorials
 
@@ -358,6 +361,7 @@
 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
 
@@ -482,7 +486,8 @@
 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. [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.
+141. [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. 
+142. [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.
 
 ### Conferences
 
@@ -568,13 +573,15 @@
 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)
 
 ### 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()`
@@ -626,6 +633,7 @@
 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