Is Optimization a Sufficient Language for Understanding Deep Learning?
https://www.offconvex.org/2019/06/03/trajectories/

iPython notebook for Attentive Neural Processes
https://arxiv.org/pdf/1901.05761.pdf

A special case are Neural Processes
https://arxiv.org/pdf/1807.01622.pdf

Try running the code on your browser (or phone) at:
https://colab.research.google.com/github/deepmind/neural-processes/blob/master/attentive_neural_process.ipynb

lecture notes from course on optimization for machine learning

Elad Hazan

https://drive.google.com/file/d/1GIDnw7T-NT4Do3eC0B5kYJlzwOs6nzIO/view

TensorFuzz: Debugging Neural Networks with Coverage-Guided Fuzzing

Augustus Odena and Ian Goodfellow: https://arxiv.org/abs/1807.10875

Code: https://github.com/brain-research/tensorfuzz

#deeplearning #neuralnetworks #technology #innovation

Google TensorNetwork Library Dramatically Accelerates ML & Physics Tasks

https://medium.com/syncedreview/google-tensornetwork-library-dramatically-accelerates-ml-physics-tasks-8c7011e0f7b0

Here are the COMPLETE Lecture notes on Professor Andrew Ng's
Stanford Machine Learning Lecture: https://www.holehouse.org/mlclass/

Deep Learning Tools
PDF Version: https://drive.google.com/file/d/1XhngKISDpQgwGlvU-hjXWZb_qfyIYjqN/view

A curated list of decision, classification and regression tree research papers from the last 30 years with implementations. It covers NeurIPS, ICML, ICLR, KDD, ICDM, CIKM, AAAI etc.
https://github.com/benedekrozemberczki/awesome-decision-tree-papers

A key conference quality indicator is low paper acceptance rates. The CVPR 2019 paper acceptance rate dropped to 25.1 percent from last year’s 29.6 percent 🤓☝️

The list of all 1300 research papers accepted for CVPR 2019 is available here: https://openaccess.thecvf.com/CVPR2019.py

Given you spend 1 hour to read 1 article and the rate of 8 articles per day, it will take you about 6 months to read all of them. You'd better start right now 🙃

#CVPR2019 #computervision #patternrecognition #deeplearning #machinelearning

ICYMI: Prof. Strang wants to tell you about his newest course 18.065 "Matrix Methods in Data Analysis, Signal Processing, and Machine Learning." It's all free on OCW--->

https://ocw.mit.edu/courses/mathematics/18-065-matrix-methods-in-data-analysis-signal-processing-and-machine-learning-spring-2018/

Text2Scene: Generating Compositional Scenes from Textual Descriptions
Tan et al.: https://arxiv.org/abs/1809.01110
#ArtificialIntelligence #DeepLearning #MachineLearning

Tackling Climate Change with Machine Learning
Rolnick et al.: https://arxiv.org/abs/1906.05433
#artificialintelligence #climatechange #climatecrisis #machinelearning

Colab notebooks tutorials for Swift for TensorFlow
GitHub by Zaid Alyafeai: https://github.com/zaidalyafeai/Swift4TF
#artificialintelligence #machinelearning #swift #tensorflow

Efficient Exploration via State Marginal Matching
Lee et al.: https://arxiv.org/abs/1906.05274
#ArtificialIntelligence #Robotics #MachineLearning

Best research paper award at our Debugging ML workshop -- "Similarity of Neural Network Representations Revisited" by Geoffrey Hinton , Mohammad Norouzi, Honglak Lee, and Simon Kornblith
https://arxiv.org/abs/1905.00414
#ICLR2019 https://t.iss.one/ArtificialIntelligenceArticles

Visual Relationships as Functions: Enabling Few-Shot Scene Graph Prediction
Dornadula et al.: https://arxiv.org/pdf/1906.04876.pdf
#ArtificialIntelligence #DeepLearning #MachineLearning

new paper from Andrew Ng , Yoshua Bengio ,Demis Hassabis , .... arxiv.org/abs/1906.05433 https://t.iss.one/ArtificialIntelligenceArticles

Tackling Climate Change with Machine Learning

Collaboration between #CarnegieMellon Carnegie Mellon University School of Computer Science, University of Pennsylvania, ETH Zürich, University of Colorado Boulder, Element AI, Mila, Université de Montréal, Harvard University, Mercator Research Institute, Technische Universit¨at Berlin, Massachusetts Institute of Technology (MIT), Cornell University, Stanford University, DeepMind, GoogleAI, Microsoft Research arxiv.org/abs/1906.05433 https://t.iss.one/ArtificialIntelligenceArticles

https://bit.ly/2KQTfzF

Face Recognition is the upcoming and modern challenge required in the machine learning in today's world. We have listed out some of the best Face Recogntion APIs which can be seamlessly integrated into your project to go one step further in image processing