Senior Machine Learning Engineer
https://ai-jobs.net/job/senior-machine-learning-engineer-13/
https://ai-jobs.net/job/senior-machine-learning-engineer-13/
With Links to everything:
Elements of Statistical Learning: https://statweb.stanford.edu/~tibs/ElemStatLearn/printings/ESLII_print10.pdf
Andrew Ng's Coursera Course: https://www.coursera.org/learn/machine-learning/home/info
The Deep Learning Book: https://www.deeplearningbook.org/front_matter.pdf
Put tensor flow or torch on a linux box and run examples: https://cs231n.github.io/aws-tutorial/
Keep up with the research: https://arxiv.org
Resume Filler - Kaggle Competitions: https://www.kaggle.com
Arxiv-sanity is pretty good for looking up arXiv papers. I've recently been making my own arXiv paper reader (https://www.lobal.io/). The intention is that you'd be able to see today's arXiv papers at a glance.
https://www.arxiv-sanity.com/
Elements of Statistical Learning: https://statweb.stanford.edu/~tibs/ElemStatLearn/printings/ESLII_print10.pdf
Andrew Ng's Coursera Course: https://www.coursera.org/learn/machine-learning/home/info
The Deep Learning Book: https://www.deeplearningbook.org/front_matter.pdf
Put tensor flow or torch on a linux box and run examples: https://cs231n.github.io/aws-tutorial/
Keep up with the research: https://arxiv.org
Resume Filler - Kaggle Competitions: https://www.kaggle.com
Arxiv-sanity is pretty good for looking up arXiv papers. I've recently been making my own arXiv paper reader (https://www.lobal.io/). The intention is that you'd be able to see today's arXiv papers at a glance.
https://www.arxiv-sanity.com/
PySlowFast: video understanding codebase from FAIR for reproducing state-of-the-art video models.
Github: https://github.com/facebookresearch/SlowFast
Paper: https://arxiv.org/pdf/1812.03982v3.pdf
Github: https://github.com/facebookresearch/SlowFast
Paper: https://arxiv.org/pdf/1812.03982v3.pdf
GitHub
GitHub - facebookresearch/SlowFast: PySlowFast: video understanding codebase from FAIR for reproducing state-of-the-art video models.
PySlowFast: video understanding codebase from FAIR for reproducing state-of-the-art video models. - facebookresearch/SlowFast
Artificial Intelligence Improves Brain Tumor Diagnosis
https://labblog.uofmhealth.org/health-tech/artificial-intelligence-improves-brain-tumor-diagnosis
https://labblog.uofmhealth.org/health-tech/artificial-intelligence-improves-brain-tumor-diagnosis
labblog.uofmhealth.org
Artificial Intelligence Improves Brain Tumor Diagnosis
Deep Learning State of the Art (2020) | MIT Deep Learning Series
https://www.youtube.com/watch?v=0VH1Lim8gL8
https://www.youtube.com/watch?v=0VH1Lim8gL8
YouTube
Deep Learning State of the Art (2020)
Lecture on most recent research and developments in deep learning, and hopes for 2020. This is not intended to be a list of SOTA benchmark results, but rather a set of highlights of machine learning and AI innovations and progress in academia, industry, and…
PyTorch: An Imperative Style, High-Performance Deep Learning Library
Paszke et al.: https://arxiv.org/abs/1912.01703
#ArtificialIntelligence #DeepLearning #PyTorch
Paszke et al.: https://arxiv.org/abs/1912.01703
#ArtificialIntelligence #DeepLearning #PyTorch
On the Relationship between Self-Attention and Convolutional Layers
Jean-Baptiste Cordonnier, Andreas Loukas, Martin Jaggi: https://openreview.net/forum?id=HJlnC1rKPB
#ArtificialIntelligence #DeepLearning #Transformers
Jean-Baptiste Cordonnier, Andreas Loukas, Martin Jaggi: https://openreview.net/forum?id=HJlnC1rKPB
#ArtificialIntelligence #DeepLearning #Transformers
OpenReview
On the Relationship between Self-Attention and Convolutional Layers
A self-attention layer can perform convolution and often learns to do so in practice.
𝗔𝗿𝘁𝗶𝗳𝗶𝗰𝗶𝗮𝗹 𝗜𝗻𝘁𝗲𝗹𝗹𝗶𝗴𝗲𝗻𝗰𝗲 𝗶𝗻 𝗛𝗲𝗮𝗹𝘁𝗵𝗰𝗮𝗿𝗲.
Very interesting and comprehensive 67-page article on the applications of AI in health and health care.
Download: https://www.healthit.gov/sites/default/files/jsr-17-task-002_aiforhealthandhealthcare12122017.pdf
Very interesting and comprehensive 67-page article on the applications of AI in health and health care.
Download: https://www.healthit.gov/sites/default/files/jsr-17-task-002_aiforhealthandhealthcare12122017.pdf
Neural Ordinary Differential Equations for Semantic Segmentation of Individual Colon Glands
Hans Pinckaers, Geert Litjens : https://arxiv.org/abs/1910.10470
GitHub : https://github.com/DIAGNijmegen/neural-odes-segmentation
#MedNeurIPS #NeurIPS #NeurIPS2019
Hans Pinckaers, Geert Litjens : https://arxiv.org/abs/1910.10470
GitHub : https://github.com/DIAGNijmegen/neural-odes-segmentation
#MedNeurIPS #NeurIPS #NeurIPS2019
arXiv.org
Neural Ordinary Differential Equations for Semantic Segmentation...
Automated medical image segmentation plays a key role in quantitative research and diagnostics. Convolutional neural networks based on the U-Net architecture are the state-of-the-art. A key...
"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
Machine Learning from scratch!
Implementation of some classic Machine Learning model from scratch and benchmarking against popular ML library, by Quan Tran: https://github.com/anhquan0412/basic_model_scratch
#ArtificialIntelligence #DeepLearning #NeuralNetworks #MachineLearning
Implementation of some classic Machine Learning model from scratch and benchmarking against popular ML library, by Quan Tran: https://github.com/anhquan0412/basic_model_scratch
#ArtificialIntelligence #DeepLearning #NeuralNetworks #MachineLearning
GitHub
GitHub - anhquan0412/basic_model_scratch: Implementation of some classic Machine Learning model from scratch and benchmarking against…
Implementation of some classic Machine Learning model from scratch and benchmarking against popular ML library - GitHub - anhquan0412/basic_model_scratch: Implementation of some classic Machine Lea...
Deep RL Bootcamp
By Pieter Abbeel, Rocky Duan, Peter Chen, Andrej Karpathy et al.: https://sites.google.com/view/deep-rl-bootcamp/lectures
#ArtificialIntelligence #DeepLearning #MachineLearning #NeuralNetworks #ReinforcementLearning
By Pieter Abbeel, Rocky Duan, Peter Chen, Andrej Karpathy et al.: https://sites.google.com/view/deep-rl-bootcamp/lectures
#ArtificialIntelligence #DeepLearning #MachineLearning #NeuralNetworks #ReinforcementLearning
Blake Richards: Deep Learning with Ensembles of Neocortical Microcircuits
An inspiring talk by Blake Richards at the ICLR2018 emphasizing the interaction between neuroscience and machine learning. This intersection is where great things happen.
https://goo.gl/1YCjrm
https://t.iss.one/ArtificialIntelligenceArticles
An inspiring talk by Blake Richards at the ICLR2018 emphasizing the interaction between neuroscience and machine learning. This intersection is where great things happen.
https://goo.gl/1YCjrm
https://t.iss.one/ArtificialIntelligenceArticles
YouTube
Blake Richards: Deep Learning with Ensembles of Neocortical Microcircuits (ICLR 2018 invited talks)
Abstract: Deep learning in artificial intelligence (AI) has demonstrated that learning hierarchical representations is a good approach for generating useful sensorimotor behaviors. However, the key to effective hierarchical learning is a mechanism for ""credit…
Artificial Intelligence & #Neuroscience: A Virtuous Circle https://deepmind.com/blog/ai-and-neuroscience-virtuous-circle/
Deepmind
AI and Neuroscience: A virtuous circle
Recent progress in AI has been remarkable. Artificial systems now outperform expert humans at Atari video games, the ancient board game Go, and high-stakes matches of heads-up poker. They can also produce handwriting and speech indistinguishable from those…
Machine Learning Unlocks Library of The Human Brain. #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #NeuroScience
https://thetartan.org/2019/11/11/scitech/brain-thoughts
https://thetartan.org/2019/11/11/scitech/brain-thoughts
"Meta-Learning and Universality: Deep Representations and Gradient Descent can Approximate any Learning Algorithm"
Chelsea Finn and Sergey Levine : https://arxiv.org/abs/1710.11622
#MachineLearning #ArtificialIntelligence #MetaLearning #NeuralComputing
Chelsea Finn and Sergey Levine : https://arxiv.org/abs/1710.11622
#MachineLearning #ArtificialIntelligence #MetaLearning #NeuralComputing
PyTorch Geometry
The PyTorch Geometry package is a geometric computer vision library for PyTorch
By Arraiy: https://github.com/arraiy/torchgeometry
#ArtificialIntelligence #ComputerVision #DeepLearning #MachineLearning #PyTorch
The PyTorch Geometry package is a geometric computer vision library for PyTorch
By Arraiy: https://github.com/arraiy/torchgeometry
#ArtificialIntelligence #ComputerVision #DeepLearning #MachineLearning #PyTorch
GitHub
GitHub - kornia/kornia: Open Source Differentiable Computer Vision Library
Open Source Differentiable Computer Vision Library - GitHub - kornia/kornia: Open Source Differentiable Computer Vision Library
Poly-time universality and limitations of deep learning
Emmanuel Abbe, Colin Sandon : https://arxiv.org/abs/2001.02992
#ArtificialIntelligence #MachineLearning #InformationTheory
Emmanuel Abbe, Colin Sandon : https://arxiv.org/abs/2001.02992
#ArtificialIntelligence #MachineLearning #InformationTheory