10 Practical Tips for the Successful Adoption of Your Machine Learning Products
https://medium.com/omdena/10-practical-tips-for-the-successful-adoption-of-your-machine-learning-products-e68dd1b486c8
https://medium.com/omdena/10-practical-tips-for-the-successful-adoption-of-your-machine-learning-products-e68dd1b486c8
Medium
10 Practical Tips for the Successful Adoption of Your Machine Learning Products
Hands-on tips for companies to build Machine Learning Products that are being adopted by their users and customers.
Architectural Innovations in Convolutional Neural Networks for Image Classification
https://machinelearningmastery.com/review-of-architectural-innovations-for-convolutional-neural-networks-for-image-classification/
https://machinelearningmastery.com/review-of-architectural-innovations-for-convolutional-neural-networks-for-image-classification/
MachineLearningMastery.com
Convolutional Neural Network Model Innovations for Image Classification - MachineLearningMastery.com
A Gentle Introduction to the Innovations in LeNet, AlexNet, VGG, Inception, and ResNet Convolutional Neural Networks.
Convolutional neural networks are comprised of two very simple elements, namely convolutional layers and pooling layers.
Although simple…
Convolutional neural networks are comprised of two very simple elements, namely convolutional layers and pooling layers.
Although simple…
Увеличение видео 1080P до 4К, или Как я научился не волноваться и полюбил апскейл с помощью нейросетей
https://habr.com/ru/post/446032/
https://habr.com/ru/post/446032/
Хабр
Увеличение видео 1080P до 4K, или Как я научился не волноваться и полюбил апскейл с помощью нейросетей
Читая недавно очередную статью про апскейл ( Upscale — масштабирование изображения до более высокого разрешения), на этот раз про коммерческий продукт Topaz AI Gigapixel, я оставил комментарий...
Evaluating the Unsupervised Learning of Disentangled Representations
http://ai.googleblog.com/2019/04/evaluating-unsupervised-learning-of.html
http://ai.googleblog.com/2019/04/evaluating-unsupervised-learning-of.html
Googleblog
Evaluating the Unsupervised Learning of Disentangled Representations
Hyperparameter optimization in python. Part 1: Scikit-Optimize.
https://towardsdatascience.com/hyperparameter-optimization-in-python-part-1-scikit-optimize-754e485d24fe
https://towardsdatascience.com/hyperparameter-optimization-in-python-part-1-scikit-optimize-754e485d24fe
MuseNet deep neural network that can generate 4-minute musical compositions with 10 different instruments
https://openai.com/blog/musenet/
https://openai.com/blog/musenet/
Openai
MuseNet
We’ve created MuseNet, a deep neural network that can generate 4-minute musical compositions with 10 different instruments, and can combine styles from country to Mozart to the Beatles. MuseNet was not explicitly programmed with our understanding of music…
How to Implement VGG, Inception and ResNet Modules for Convolutional Neural Networks from Scratch
https://machinelearningmastery.com/how-to-implement-major-architecture-innovations-for-convolutional-neural-networks/
https://machinelearningmastery.com/how-to-implement-major-architecture-innovations-for-convolutional-neural-networks/
MachineLearningMastery.com
How to Develop VGG, Inception and ResNet Modules from Scratch in Keras - MachineLearningMastery.com
There are discrete architectural elements from milestone models that you can use in the design of your own convolutional neural networks. Specifically, models that have achieved state-of-the-art results for tasks like image classification use discrete architecture…
How to Start Competing on Kaggle
https://towardsdatascience.com/how-to-begin-competing-on-kaggle-bd9b5f32dbbc
https://towardsdatascience.com/how-to-begin-competing-on-kaggle-bd9b5f32dbbc
A pytorch-toolbelt is a Python library with a set of bells and whistles for PyTorch for fast R&D prototyping and Kaggle farming
https://github.com/BloodAxe/pytorch-toolbelt
https://github.com/BloodAxe/pytorch-toolbelt
GitHub
GitHub - BloodAxe/pytorch-toolbelt: PyTorch extensions for fast R&D prototyping and Kaggle farming
PyTorch extensions for fast R&D prototyping and Kaggle farming - BloodAxe/pytorch-toolbelt
A Gentle Introduction to 1×1 Convolutions to Reduce the Complexity of Convolutional Neural Networks
https://machinelearningmastery.com/introduction-to-1x1-convolutions-to-reduce-the-complexity-of-convolutional-neural-networks/
https://machinelearningmastery.com/introduction-to-1x1-convolutions-to-reduce-the-complexity-of-convolutional-neural-networks/
MachineLearningMastery.com
A Gentle Introduction to 1×1 Convolutions to Manage Model Complexity - MachineLearningMastery.com
Pooling can be used to down sample the content of feature maps, reducing their width and height whilst maintaining their salient features. A problem with deep convolutional neural networks is that the number of feature maps often increases with the depth…
Forwarded from Artificial Intelligence
Announcing the 6th Fine-Grained Visual Categorization Workshop
http://ai.googleblog.com/2019/04/announcing-6th-fine-grained-visual.html
http://ai.googleblog.com/2019/04/announcing-6th-fine-grained-visual.html
Googleblog
Announcing the 6th Fine-Grained Visual Categorization Workshop
Создаем с нуля собственную нейронную сеть на Python
https://habr.com/ru/company/mailru/blog/449416/
https://habr.com/ru/company/mailru/blog/449416/
Deep Learning Lecture
https://www.youtube.com/watch?v=FQw2l0AJ2iw
https://www.youtube.com/watch?v=FQw2l0AJ2iw
YouTube
(Old) Lecture 26 | (3/4) Deep Reinforcement Learning - TD and SARSA
Carnegie Mellon University
Course: 11-785, Intro to Deep Learning
Offering: Spring 2019
For more information, please visit: http://deeplearning.cs.cmu.edu/
Contents:
• Reinforcement Learning
• TD Learning
• SARSA
Course: 11-785, Intro to Deep Learning
Offering: Spring 2019
For more information, please visit: http://deeplearning.cs.cmu.edu/
Contents:
• Reinforcement Learning
• TD Learning
• SARSA
Five Machine Learning Paradoxes that will Change the Way You Think About Data
https://towardsdatascience.com/five-machine-learning-paradoxes-that-will-change-the-way-you-think-about-data-e100be5620d7
https://towardsdatascience.com/five-machine-learning-paradoxes-that-will-change-the-way-you-think-about-data-e100be5620d7
Medium
Five Machine Learning Paradoxes that will Change the Way You Think About Data
Paradoxes are one of the marvels of human cognition that are hard to using math and statistics. Conceptually, a paradox is a statement…
Automating Optimization of Quantized Deep Learning Models on CUDA
https://tvm.ai/2019/04/29/opt-cuda-quantized.html
https://tvm.ai/2019/04/29/opt-cuda-quantized.html
A Gentle Introduction to the ImageNet Large Scale Visual Recognition Challenge (ILSVRC)
https://machinelearningmastery.com/introduction-to-the-imagenet-large-scale-visual-recognition-challenge-ilsvrc/
https://machinelearningmastery.com/introduction-to-the-imagenet-large-scale-visual-recognition-challenge-ilsvrc/
MachineLearningMastery.com
A Gentle Introduction to the ImageNet Challenge (ILSVRC) - MachineLearningMastery.com
The rise in popularity and use of deep learning neural network techniques can be traced back to the innovations in the application of convolutional neural networks to image classification tasks.
Some of the most important innovations have sprung from submissions…
Some of the most important innovations have sprung from submissions…
Пошаговое руководство по созданию голосового помощника с Python
https://habr.com/ru/post/450224/
https://habr.com/ru/post/450224/
Real-Time Patch-Based Stylization of Portraits
Using Generative Adversarial Network
http://dcgi.fel.cvut.cz/home/sykorad/facestyleGAN.html
Using Generative Adversarial Network
http://dcgi.fel.cvut.cz/home/sykorad/facestyleGAN.html
dcgi.fel.cvut.cz
Real-Time Patch-Based Stylization of Portraits Using Generative Adversarial Network
Ensemble methods: bagging, boosting and stacking
https://towardsdatascience.com/ensemble-methods-bagging-boosting-and-stacking-c9214a10a205
https://towardsdatascience.com/ensemble-methods-bagging-boosting-and-stacking-c9214a10a205
Towards Data Science
Ensemble learning: Bagging and Boosting | Towards Data Science
It's time to explore the world of bagging and boosting. With these powerful techniques, you can improve the performance of your models...