How Xnor.ai Managed to Squeeze a Deep Neural Network onto a $20 Wyze Camera
Blog by Carlo C del Mundo: https://medium.com/@xnor_ai/how-xnor-ai-managed-to-squeeze-a-deep-neural-network-onto-a-20-wyze-camera-88a5f9fc3466
#neuralnetwork #deeplearning #machinelearning
Blog by Carlo C del Mundo: https://medium.com/@xnor_ai/how-xnor-ai-managed-to-squeeze-a-deep-neural-network-onto-a-20-wyze-camera-88a5f9fc3466
#neuralnetwork #deeplearning #machinelearning
Medium
How Xnor.ai Managed to Squeeze a Deep Neural Network onto a $20 Wyze Camera
By Carlo C del Mundo
Two new benchmark papers for reinforcement learning have been published online!
[https://www.endtoend.ai/rl-weekly/24](https://www.endtoend.ai/rl-weekly/24)
[https://www.endtoend.ai/rl-weekly/24](https://www.endtoend.ai/rl-weekly/24)
endtoend.ai
RL Weekly 24: Benchmarks for Model-based RL and Bonus-based Exploration Methods
Studying Artificial Intelligence, from backbone to application.
Object-oriented programming for data scientists: Build your ML estimator
https://towardsdatascience.com/object-oriented-programming-for-data-scientists-build-your-ml-estimator-7da416751f64
https://towardsdatascience.com/object-oriented-programming-for-data-scientists-build-your-ml-estimator-7da416751f64
Towards Data Science
Object-oriented programming for data scientists: Build your ML estimator | Towards Data Science
Implement some of the core OOP principles in a machine learning context by building your own Scikit-learn-like estimator.
A Tour of Generative Adversarial Network Models
https://machinelearningmastery.com/tour-of-generative-adversarial-network-models/
https://machinelearningmastery.com/tour-of-generative-adversarial-network-models/
MachineLearningMastery.com
A Tour of Generative Adversarial Network Models - MachineLearningMastery.com
Generative Adversarial Networks, or GANs, are deep learning architecture generative models that have seen wide success.
There are thousands of papers on GANs and many hundreds of named-GANs, that is, models with a defined name that often includes
There are thousands of papers on GANs and many hundreds of named-GANs, that is, models with a defined name that often includes
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DeepView: generate new views having sparse set of input images from different viewpoints
arxiv.org/abs/1906.07316
arxiv.org/abs/1906.07316
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Neural Rendering in the Wild:
1. Take ~3K Internet photos
2. Retrieve 3D point cloud from photos using any structure from motion algorithm
3. Enjoy realistic free-point-of-view video, rerendered by NN
arxiv.org/abs/1904.04290
1. Take ~3K Internet photos
2. Retrieve 3D point cloud from photos using any structure from motion algorithm
3. Enjoy realistic free-point-of-view video, rerendered by NN
arxiv.org/abs/1904.04290
Video Question Generation via Cross-Modal Self-Attention Networks Learning. arxiv.org/abs/1907.03049
Dependency-aware Attention Control for Unconstrained Face Recognition with Image Sets. arxiv.org/abs/1907.03030
One of the hardest problems in #AI is common sense reasoning. This paper by
Nazneen Fatema Rajani, Bryan McCann, Caiming Xiong, Richard Socher
arxiv.org/abs/1906.02361
Github: (link: https://github.com/salesforce/cos-e)
Blog: (link: https://blog.einstein.ai/leveraging-language-models-for-commonsense/)
Nazneen Fatema Rajani, Bryan McCann, Caiming Xiong, Richard Socher
arxiv.org/abs/1906.02361
Github: (link: https://github.com/salesforce/cos-e)
Blog: (link: https://blog.einstein.ai/leveraging-language-models-for-commonsense/)
15 Of The Best Machine Learning Courses On Coursera For Free
https://hackernoon.com/15-of-the-best-machine-learning-courses-on-coursera-for-free-d83ad23f54d7
https://hackernoon.com/15-of-the-best-machine-learning-courses-on-coursera-for-free-d83ad23f54d7
Medium
15 Of The Best Machine Learning Courses On Coursera For Free
How to learn about data science and machine learning without breaking the bank
Si os interesa, también han publicado un estudio en el que detallan el experimento.
Es este: L. Broussard, K. Bailey, W. Bailey et al.; "New Search for Mirror Neutrons at HFIR" y está disponible en arXiv: arxiv.org/pdf/1710.00767
Es este: L. Broussard, K. Bailey, W. Bailey et al.; "New Search for Mirror Neutrons at HFIR" y está disponible en arXiv: arxiv.org/pdf/1710.00767
Blind Universal Bayesian Image Denoising with Gaussian Noise Level Learning. arxiv.org/abs/1907.03029
Financial Time Series Data Processing for Machine Learning. arxiv.org/abs/1907.03010
Get ready for the new upcoming book from Machine Learning Mastery on Generative Adversarial Network (GAN)!
(The image from Image-to-Image Translation with Conditional Adversarial Nets site:
https://phillipi.github.io/pix2pix/)
(The image from Image-to-Image Translation with Conditional Adversarial Nets site:
https://phillipi.github.io/pix2pix/)
Real-Time Hair Segmentation and Recoloring on Mobile GPUs
Real-time inference speed on mobile GPUs with high accuracy:
Full size (512×512) in 5.7 ms on iPhone XS with 81.0% IOU accuracy
Small size (256×256) in 6 ms on Pixel 3 with 80.2% IOU accuracy
https://static1.squarespace.com/static/5c3f69e1cc8fedbc039ea739/t/5d0291ea06eb89000122c4b9/1560449515878/24_CVPR2019_Hair_Segmentation_v2.pdf
Real-time inference speed on mobile GPUs with high accuracy:
Full size (512×512) in 5.7 ms on iPhone XS with 81.0% IOU accuracy
Small size (256×256) in 6 ms on Pixel 3 with 80.2% IOU accuracy
https://static1.squarespace.com/static/5c3f69e1cc8fedbc039ea739/t/5d0291ea06eb89000122c4b9/1560449515878/24_CVPR2019_Hair_Segmentation_v2.pdf
tf.keras for Researchers: Crash Course
"That's all you need to get started with reimplementing most deep learning research papers in TensorFlow 2.0 and Keras!"
Code by François Chollet: https://colab.research.google.com/drive/14CvUNTaX1OFHDfaKaaZzrBsvMfhCOHIR
#deeplearning #keras #tensorflow #tutorial
"That's all you need to get started with reimplementing most deep learning research papers in TensorFlow 2.0 and Keras!"
Code by François Chollet: https://colab.research.google.com/drive/14CvUNTaX1OFHDfaKaaZzrBsvMfhCOHIR
#deeplearning #keras #tensorflow #tutorial
Google
tf.keras for Researchers: Crash Course.ipynb
Colaboratory notebook