"Neural Networks for Machine Learning by Geoffrey Hinton" (Coursera 2013)
https://www.youtube.com/playlist?list=PLiPvV5TNogxKKwvKb1RKwkq2hm7ZvpHz0 or https://www.coursera.org/learn/neural-networks
https://www.youtube.com/playlist?list=PLiPvV5TNogxKKwvKb1RKwkq2hm7ZvpHz0 or https://www.coursera.org/learn/neural-networks
Interview with The Youngest Kaggle Grandmaster: Mikel Bober-Irizar (anokas)
https://hackernoon.com/interview-with-the-youngest-kaggle-grandmaster-mikel-bober-irizar-anokas-17dfd2461070
https://hackernoon.com/interview-with-the-youngest-kaggle-grandmaster-mikel-bober-irizar-anokas-17dfd2461070
Access free GPU compute via Colab
https://colab.research.google.com/notebooks/welcome.ipynb
Colaboratory is a research tool for machine learning education and research. It’s a Jupyter notebook environment that requires no setup to use.
@ArtificialIntelligenceArticles
https://colab.research.google.com/notebooks/welcome.ipynb
Colaboratory is a research tool for machine learning education and research. It’s a Jupyter notebook environment that requires no setup to use.
@ArtificialIntelligenceArticles
Google
Welcome To Colab
Run, share, and edit Python notebooks
Deep learning in radiology: an overview of the concepts and a survey of the state of the art
https://arxiv.org/abs/1802.08717
https://arxiv.org/abs/1802.08717
How to implement a YOLO (v3) object detector from scratch in PyTorch: Part 1
Blog by Ayoosh Kathuria: https://blog.paperspace.com/how-to-implement-a-yolo-object-detector-in-pytorch/?mlreview
Blog by Ayoosh Kathuria: https://blog.paperspace.com/how-to-implement-a-yolo-object-detector-in-pytorch/?mlreview
"Deep Reinforcement Learning with Subgoals"#NIPS 2017
Invited talk by DeepMind’s Professor David Silver
https://vimeo.com/249557775 @ArtificialIntelligenceArticles
Invited talk by DeepMind’s Professor David Silver
https://vimeo.com/249557775 @ArtificialIntelligenceArticles
Practical Text Classification With Python and Keras
By Nikolai Janakiev: https://realpython.com/python-keras-text-classification/
By Nikolai Janakiev: https://realpython.com/python-keras-text-classification/
Introduction to Applied Linear Algebra – Vectors, Matrices, and Least Squares
By Stephen Boyd and Lieven Vandenberghe, Cambridge University https://web.stanford.edu/~boyd/vmls/
By Stephen Boyd and Lieven Vandenberghe, Cambridge University https://web.stanford.edu/~boyd/vmls/
Complex-YOLO: Real-time 3D Object Detection on Point Clouds
Simon et al.: https://arxiv.org/abs/1803.06199
Simon et al.: https://arxiv.org/abs/1803.06199
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Capture Anything in 3D (Using Only Your Phone)
Copy + Paste reality: https://3dScannerApp.com @ArtificialIntelligenceArticles
Copy + Paste reality: https://3dScannerApp.com @ArtificialIntelligenceArticles
pair2vec: Compositional Word-Pair Embeddings for Cross-Sentence Inference
Joshi et al.: https://arxiv.org/abs/1810.08854
Joshi et al.: https://arxiv.org/abs/1810.08854
Reconstructing quantum states with generative models"
Carrasquilla et al.: https://arxiv.org/abs/1810.10584
#quantumphysics #quantumcomputing #machinelearning #quantummachinelearning #artificialinte
Carrasquilla et al.: https://arxiv.org/abs/1810.10584
#quantumphysics #quantumcomputing #machinelearning #quantummachinelearning #artificialinte
Build your own robotic cat
Blog by Alex Bate: https://www.raspberrypi.org/blog/robotic-cat-petoi-nybble/
#Arduino #ArtificialIntelligence #MachineLearning #RaspberryPi #Robotics
Blog by Alex Bate: https://www.raspberrypi.org/blog/robotic-cat-petoi-nybble/
#Arduino #ArtificialIntelligence #MachineLearning #RaspberryPi #Robotics
SURREAL: Open-Source Reinforcement Learning Framework and Robot Manipulation Benchmark
https://surreal.stanford.edu/
https://surreal.stanford.edu/
BabyAI: First Steps Towards Grounded Language Learning With a Human In the Loop"
Maxime Chevalier-Boisvert et al.: https://arxiv.org/abs/1810.08272
Maxime Chevalier-Boisvert et al.: https://arxiv.org/abs/1810.08272
NEP 18 — A dispatch mechanism for NumPy’s high level array functions
Abstact: "We propose the array_function protocol, to allow arguments of NumPy functions to define how that function operates on them. This will allow using NumPy as a high level API for efficient multi-dimensional array operations, even with array implementations that differ greatly from numpy.ndarray."
https://www.numpy.org/neps/nep-0018-array-function-protocol.html
#numpy
Abstact: "We propose the array_function protocol, to allow arguments of NumPy functions to define how that function operates on them. This will allow using NumPy as a high level API for efficient multi-dimensional array operations, even with array implementations that differ greatly from numpy.ndarray."
https://www.numpy.org/neps/nep-0018-array-function-protocol.html
#numpy
These enhancements enable us to process large 3D dark matter distribution and predict the cosmological parameters ΩM, σ8, and ns with unprecedented accuracy arxiv.org/abs/1808.04728
30 Amazing Machine Learning Projects https://medium.mybridge.co/30-amazing-machine-learning-projects-for-the-past-year-v-2018-b853b8621ac7 @ArtificialIntelligenceArticles
Ultimate guide and resources for Data science 2019
https://medium.com/@purnasaigudikandula/ultimate-guide-and-resources-for-data-science-2019-f663f9384fc7 @ArtificialIntelligenceArticles
https://medium.com/@purnasaigudikandula/ultimate-guide-and-resources-for-data-science-2019-f663f9384fc7 @ArtificialIntelligenceArticles