Few things are closer to my heart than Graphs and Machine Learning. This survey paper looks at deep learning techniques for handling graphs:
https://arxiv.org/pdf/1812.04202.pdf
https://arxiv.org/pdf/1812.04202.pdf
Stanford computer scientists developed a deep learning algorithm that can diagnose heart rhythm defects better than cardiologists.
https://stanford.io/2BnLYzB
https://stanford.io/2BnLYzB
Stanford News
Algorithm diagnoses heart arrhythmias with cardiologist-level accuracy | Stanford News
A new deep learning algorithm can diagnose 13 types of heart rhythm defects, called arrhythmias, better than cardiologists.
Excellent post on achieving state-of-the-art heart disease diagnosis using deep learning (dilated U-Net in Keras): https://blog.insightdatascience.com/heart-disease-diagnosis-with-deep-learning-c2d92c27e730 https://t.iss.one/ArtificialIntelligenceArticles
Know here everything about Julia - the programming language making Machine Learning (ML) better. 💻
https://appinventiv.com/blog/introducing-julia-machine-learning-development-language/
https://appinventiv.com/blog/introducing-julia-machine-learning-development-language/
Appinventiv
Introducing Julia: A Powerful Machine Learning Language
Julia is proving to be the best ML development programming language. But, will it be able to surpass the existing ones? What features it holds? Find out.
DoorGym: A Scalable Door Opening Environment and Baseline Agent
Urakami et al.: https://arxiv.org/pdf/1908.01887v1.pdf
#DeepLearning #ReinforcementLearning #Robotics
Urakami et al.: https://arxiv.org/pdf/1908.01887v1.pdf
#DeepLearning #ReinforcementLearning #Robotics
Benchmarking Bonus-Based Exploration Methods on the Arcade Learning Environment
Adrien Ali Taïga, William Fedus, Marlos C. Machado, Aaron Courville, Marc G. Bellemare : https://arxiv.org/abs/1908.02388
#deeplearning #machinelearning #reinforcementlearning
Adrien Ali Taïga, William Fedus, Marlos C. Machado, Aaron Courville, Marc G. Bellemare : https://arxiv.org/abs/1908.02388
#deeplearning #machinelearning #reinforcementlearning
Machine Learning Yearning (Draft Version)
By Andrew Ng: https://d2wvfoqc9gyqzf.cloudfront.net/content/uploads/2018/09/Ng-MLY01-13.pdf
#100DaysOfMLCode #ArtificialIntelligence #DeepLearning #MachineLearning #NeuralNetworks
By Andrew Ng: https://d2wvfoqc9gyqzf.cloudfront.net/content/uploads/2018/09/Ng-MLY01-13.pdf
#100DaysOfMLCode #ArtificialIntelligence #DeepLearning #MachineLearning #NeuralNetworks
"Linear Algebra"
Instructor : Prof. Gilbert Strang
MIT OpenCourseWare : https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/video-lectures/
#LinearAlgebra #MatrixTheory
Instructor : Prof. Gilbert Strang
MIT OpenCourseWare : https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/video-lectures/
#LinearAlgebra #MatrixTheory
Compressing BERT for faster prediction
Blog by Sam Sucik : https://blog.rasa.com/compressing-bert-for-faster-prediction-2/
#ArtificialIntelligence #NaturalLanguageProcessing #UnsupervisedLearning
Blog by Sam Sucik : https://blog.rasa.com/compressing-bert-for-faster-prediction-2/
#ArtificialIntelligence #NaturalLanguageProcessing #UnsupervisedLearning
Rasa
Learn how to make BERT smaller and faster
Let's look at compression methods for neural networks, such as quantization and pruning. Then, we apply one to BERT using TensorFlow Lite.
New Releases: PyTorch 1.2, torchtext 0.4, torchaudio 0.3, and torchvision 0.4
Blog by Team PyTorch : https://pytorch.org/blog/pytorch-1.2-and-domain-api-release/
GitHub : https://github.com/pytorch/pytorch/releases/tag/v1.2.0
#deeplearning #machinelearning #pytorch
Blog by Team PyTorch : https://pytorch.org/blog/pytorch-1.2-and-domain-api-release/
GitHub : https://github.com/pytorch/pytorch/releases/tag/v1.2.0
#deeplearning #machinelearning #pytorch
PyTorch
New Releases: PyTorch 1.2, torchtext 0.4, torchaudio 0.3, and torchvision 0.4
Since the release of PyTorch 1.0, we’ve seen the community expand to add new tools, contribute to a growing set of models available in the PyTorch Hub, and continually increase usage in both research and production.
From TensorFlow to PyTorch
By Thomas Wolf : https://medium.com/huggingface/from-tensorflow-to-pytorch-265f40ef2a28
#deeplearning #pytorch #tensorflow
By Thomas Wolf : https://medium.com/huggingface/from-tensorflow-to-pytorch-265f40ef2a28
#deeplearning #pytorch #tensorflow
Medium
🌓 From TensorFlow to PyTorch
Friends and users of our open-source tools are often surprised how fast 🚀 we reimplement the latest SOTA pretrained TensorFlow models to…
An Empirical Study of Batch Normalization and Group Normalization in Conditional Computation
https://arxiv.org/abs/1908.00061v1
https://arxiv.org/abs/1908.00061v1
arXiv.org
An Empirical Study of Batch Normalization and Group Normalization...
Batch normalization has been widely used to improve optimization in deep
neural networks. While the uncertainty in batch statistics can act as a
regularizer, using these dataset statistics...
neural networks. While the uncertainty in batch statistics can act as a
regularizer, using these dataset statistics...
Attend To Count: Crowd Counting with Adaptive Capacity Multi-scale CNNs. arxiv.org/abs/1908.02797
Efficient Approximation of Deep ReLU Networks for Functions on Low Dimensional Manifolds. arxiv.org/abs/1908.01842
Structured Knowledge Discovery from Massive Text Corpus
arxiv.org/abs/1908.01837
arxiv.org/abs/1908.01837
How We Construct a Virtual Being’s Brain with Deep Learning
https://towardsdatascience.com/how-we-construct-a-virtual-beings-brain-with-deep-learning-8f8e5eafe3a9
https://towardsdatascience.com/how-we-construct-a-virtual-beings-brain-with-deep-learning-8f8e5eafe3a9
Medium
How We Construct a Virtual Being’s Brain with Deep Learning
3 video demos showcasing TwentyBN’s deep learning technology for human behavior understanding