PyTorch Image Models
This repository contains PyTorch image models, noscripts, pre-trained weights: (SE) ResNet/ResNeXT, DPN, EfficientNet, MobileNet-V3/V2/V1, MNASNet, Single-Path NAS, FBNet, and more.
http://bit.ly/2YlZwqW
This repository contains PyTorch image models, noscripts, pre-trained weights: (SE) ResNet/ResNeXT, DPN, EfficientNet, MobileNet-V3/V2/V1, MNASNet, Single-Path NAS, FBNet, and more.
http://bit.ly/2YlZwqW
GitHub
rwightman/pytorch-image-models
PyTorch image models, noscripts, pretrained weights -- (SE)ResNet/ResNeXT, DPN, EfficientNet, MixNet, MobileNet-V3/V2, MNASNet, Single-Path NAS, FBNet, and more - rwightman/pytorch-image-models
Looking forward to ODS.ai Odessa Meetup & Data Bar taking place July 5 in Odessa. Join the first informal meetup community on Open Data Science in the Odessa region. Lots of networking and heated discussion on the upcoming EECVC conference are guaranteed. The participation is free, but registration is required.
Learn more: http://bit.ly/2YB19B2
Learn more: http://bit.ly/2YB19B2
Study E-Book
This repository contains a variety of insightful e-books about Computer Vision, Deep Learning, Machine Learning, Math, NLP, Python, and Reinforcement Learning.
http://bit.ly/2Yun110
This repository contains a variety of insightful e-books about Computer Vision, Deep Learning, Machine Learning, Math, NLP, Python, and Reinforcement Learning.
http://bit.ly/2Yun110
GitHub
changwookjun/StudyBook
Study E-Book(ComputerVision DeepLearning MachineLearning Math NLP Python ReinforcementLearning) - changwookjun/StudyBook
Best Resources for Getting Started With GANs
In this article, you will learn the best resources you can use to learn about generative adversarial networks: GAN Applications, GAN Video Presentations, GAN Paper Reading List, GAN Books.
http://bit.ly/2YuEF51
In this article, you will learn the best resources you can use to learn about generative adversarial networks: GAN Applications, GAN Video Presentations, GAN Paper Reading List, GAN Books.
http://bit.ly/2YuEF51
Machine Learning Mastery
Best Resources for Getting Started With GANs - Machine Learning Mastery
Generative Adversarial Networks, or GANs, are a type of deep learning technique for generative modeling.
GANs are the techniques behind the startlingly photorealistic generation of human faces, as well as impressive image translation tasks such as photo…
GANs are the techniques behind the startlingly photorealistic generation of human faces, as well as impressive image translation tasks such as photo…
Accelerating MRI reconstruction via active acquisition
In this article, you will learn how researchers from Facebook AI proposed a new approach to MRI reconstruction that restores a high fidelity image from partially observed measurements in less time and with fewer errors.
http://bit.ly/2YyT3ZK
In this article, you will learn how researchers from Facebook AI proposed a new approach to MRI reconstruction that restores a high fidelity image from partially observed measurements in less time and with fewer errors.
http://bit.ly/2YyT3ZK
Facebook
Accelerating MRI reconstruction via active acquisition
This novel approach to undersampled magnetic resonance imaging (MRI) reconstruction restores a high-fidelity image from partially observed measurements.
R Functions Tutorial
In this tutorial, you will look closely at different types of functions in R to learn how they work, and why they're useful for data science and data analysis tasks.
http://bit.ly/2YF7Br6
In this tutorial, you will look closely at different types of functions in R to learn how they work, and why they're useful for data science and data analysis tasks.
http://bit.ly/2YF7Br6
Dataquest
R Functions Tutorial: Writing, Scoping, Vectorizing, and More! – Dataquest
Learn how to use R functions, including how to use built-in generic functions, how to use vectorization, and how to write your own custom functions.
Introducing MASS
In this article, you will learn about MASS, a new pre-training method that’s claimed to achieve better results than BERT and GPT.
http://bit.ly/2YHxdDK
In this article, you will learn about MASS, a new pre-training method that’s claimed to achieve better results than BERT and GPT.
http://bit.ly/2YHxdDK
Microsoft Research
Introducing MASS – A pre-training method that outperforms BERT and GPT in sequence to sequence language generation tasks
Pre-training is a hot topic in NLP research and models like BERT and GPT have definitely delivered exciting breakthroughs. The challenge is in upping our game in finer sequence to sequence based language generation tasks. Enter MASS. Click the link in our…
How To Become a Data Engineer
A list of useful resources to help you learn Data Engineering from scratch
http://bit.ly/2G0H53V
A list of useful resources to help you learn Data Engineering from scratch
http://bit.ly/2G0H53V
GitHub
adilkhash/Data-Engineering-HowTo
A list of useful resources to learn Data Engineering from scratch - adilkhash/Data-Engineering-HowTo
Advanced R
This is the 2nd edition of “Advanced R”, a book in Chapman & Hall’s R Series. The book is designed primarily for R users who want to improve their programming skills and understanding of the language. It should also be useful for programmers coming to R from other languages, as help for you to understand why R works the way it does.
http://bit.ly/2YCtyqy
This is the 2nd edition of “Advanced R”, a book in Chapman & Hall’s R Series. The book is designed primarily for R users who want to improve their programming skills and understanding of the language. It should also be useful for programmers coming to R from other languages, as help for you to understand why R works the way it does.
http://bit.ly/2YCtyqy
adv-r.hadley.nz
Advanced R
The book is designed primarily for R users who want to improve their programming skills and understanding of the language. It should also be useful for programmers coming to R from other languages, as help you to understand why R works the way it does.
Panel: A high-level app and dashboarding solution for the PyData ecosystem
In this article, you will learn about Panel, a new open-source Python library that lets you create custom interactive web apps and dashboards by connecting user-defined widgets to plots, images, tables, or text.
http://bit.ly/2YJnc8L
In this article, you will learn about Panel, a new open-source Python library that lets you create custom interactive web apps and dashboards by connecting user-defined widgets to plots, images, tables, or text.
http://bit.ly/2YJnc8L
Medium
Panel: A high-level app and dashboarding solution for the PyData ecosystem.
A high-level app and dashboarding solution for the PyData ecosystem.
Keras Mask R-CNN
In this tutorial, you will learn how to use Keras and Mask R-CNN to perform instance segmentation (both with and without a GPU).
http://bit.ly/2YKKZVV
In this tutorial, you will learn how to use Keras and Mask R-CNN to perform instance segmentation (both with and without a GPU).
http://bit.ly/2YKKZVV
PyImageSearch
Keras Mask R-CNN - PyImageSearch
In this tutorial you will learn how to use Keras, Mask R-CNN, and Deep Learning for instance segmentation (both with and without a GPU).
How to Implement GAN Hacks to Train Stable Generative Adversarial Networks
In this article, you will learn the best sources for practical heuristics or hacks when developing generative adversarial networks; how to implement seven best practices for the deep convolutional GAN model architecture from scratch; how to implement four additional best practices from Soumith Chintala’s GAN Hacks presentation and list.
http://bit.ly/2NP3iZc
In this article, you will learn the best sources for practical heuristics or hacks when developing generative adversarial networks; how to implement seven best practices for the deep convolutional GAN model architecture from scratch; how to implement four additional best practices from Soumith Chintala’s GAN Hacks presentation and list.
http://bit.ly/2NP3iZc
Machine Learning Mastery
How to Implement GAN Hacks in Keras to Train Stable Models - Machine Learning Mastery
Generative Adversarial Networks, or GANs, are challenging to train.
This is because the architecture involves both a generator and a discriminator model that compete in a zero-sum game. It means that improvements to one model come at the cost of a degrading…
This is because the architecture involves both a generator and a discriminator model that compete in a zero-sum game. It means that improvements to one model come at the cost of a degrading…
Change input shape dimensions for fine-tuning with Keras
In this tutorial, you will learn how to change the input shape tensor dimensions for fine-tuning using Keras. You’ll understand how to apply transfer learning to images with different image dimensions than what the CNN was originally trained on.
http://bit.ly/2NNceOv
#ML #AI #ArtificialIntelligence #DataScience #MachineLearning #Keras #deeplearning #dl
In this tutorial, you will learn how to change the input shape tensor dimensions for fine-tuning using Keras. You’ll understand how to apply transfer learning to images with different image dimensions than what the CNN was originally trained on.
http://bit.ly/2NNceOv
#ML #AI #ArtificialIntelligence #DataScience #MachineLearning #Keras #deeplearning #dl
PyImageSearch
Change input shape dimensions for fine-tuning with Keras - PyImageSearch
In this tutorial, you will learn how to change the input shape tensor dimensions for fine-tuning using Keras. After going through this guide you’ll understand how to apply transfer learning to images with different image dimensions than what the CNN was originally…
How to Perform Face Recognition With VGGFace2 in Keras
In this article, you will learn about the VGGFace and VGGFace2 models for face recognition and how to install the keras_vggface library to make use of these models in Python with Keras; how to develop a face identification system to predict the name of celebrities in given photographs; how to develop a face verification system to confirm the identity of a person given a photograph of their face.
http://bit.ly/32oOOlX
In this article, you will learn about the VGGFace and VGGFace2 models for face recognition and how to install the keras_vggface library to make use of these models in Python with Keras; how to develop a face identification system to predict the name of celebrities in given photographs; how to develop a face verification system to confirm the identity of a person given a photograph of their face.
http://bit.ly/32oOOlX
Machine Learning Mastery
How to Perform Face Recognition With VGGFace2 in Keras - Machine Learning Mastery
Face recognition is a computer vision task of identifying and verifying a person based on a photograph of their face.
Recently, deep learning convolutional neural networks have surpassed classical methods and are achieving state-of-the-art results on standard…
Recently, deep learning convolutional neural networks have surpassed classical methods and are achieving state-of-the-art results on standard…
Tips for Training Likelihood Models
This is a tutorial on common practices in training generative models that optimize likelihood directly, such as autoregressive models and normalizing flows.
http://bit.ly/2NQEmAq
This is a tutorial on common practices in training generative models that optimize likelihood directly, such as autoregressive models and normalizing flows.
http://bit.ly/2NQEmAq
Evjang
Tips for Training Likelihood Models
This is a tutorial on common practices in training generative models that optimize likelihood directly, such as autoregressive models and ...
Building Lyft’s Marketing Automation Platform
This post on Lyft's Engineering blog walks-through the machine learning system that enables Lyft's marketing at scale. It's fairly high-level but it's a good read and includes worthwhile details along the way.
https://lft.to/2NWlCiU
This post on Lyft's Engineering blog walks-through the machine learning system that enables Lyft's marketing at scale. It's fairly high-level but it's a good read and includes worthwhile details along the way.
https://lft.to/2NWlCiU
Medium
Building Lyft’s Marketing Automation Platform
Machine learning based marketing automation to improve cost and volume efficiency in an ever-changing marketplace.
👍1
NumPy implementations of various ML models
Repository of mostly pure NumPy implementations of machine learning models. These are bare-bones implementations and aren't optimized to be efficient. They're optimized to be understanding how they work.
http://bit.ly/2lbReDe
Repository of mostly pure NumPy implementations of machine learning models. These are bare-bones implementations and aren't optimized to be efficient. They're optimized to be understanding how they work.
http://bit.ly/2lbReDe
GitHub
GitHub - ddbourgin/numpy-ml: Machine learning, in numpy
Machine learning, in numpy. Contribute to ddbourgin/numpy-ml development by creating an account on GitHub.
Let's meet at AIUkraine 2019 in September!
AI Ukraine is an annual and most professional industry conference powered by AltexSoft.
The conference will include three stages:
- Data Science & Machine Learning
- BigData & Analytics
- Business & Startups
Special promo code for 7% discount for our subscribers - DSDigest-AI2019
Registration and more information: http://bit.ly/2O5DOXz
AI Ukraine is an annual and most professional industry conference powered by AltexSoft.
The conference will include three stages:
- Data Science & Machine Learning
- BigData & Analytics
- Business & Startups
Special promo code for 7% discount for our subscribers - DSDigest-AI2019
Registration and more information: http://bit.ly/2O5DOXz
Top 25 pandas tricks
25 tricks that will help you to work faster and write better pandas code.
http://bit.ly/2O65qMn
25 tricks that will help you to work faster and write better pandas code.
http://bit.ly/2O65qMn
nbviewer.jupyter.org
Notebook on nbviewer
Check out this Jupyter notebook!
Anomaly Detection for Dummies
This tutorial explores unsupervised anomaly detection for univariate and multivariate data. Covers a variety of detection strategies with python code snippets and screenshots.
Jupyter notebook: http://bit.ly/2O2PFp9
http://bit.ly/2O7unaa
This tutorial explores unsupervised anomaly detection for univariate and multivariate data. Covers a variety of detection strategies with python code snippets and screenshots.
Jupyter notebook: http://bit.ly/2O2PFp9
http://bit.ly/2O7unaa
GitHub
susanli2016/Machine-Learning-with-Python
Python code for common Machine Learning Algorithms - susanli2016/Machine-Learning-with-Python
TRFL
TRFL is a library built on top of TensorFlow that exposes several useful building blocks for implementing Reinforcement Learning agents, which was developed by the Research Engineering team at DeepMind.
http://bit.ly/2O8nfKu
TRFL is a library built on top of TensorFlow that exposes several useful building blocks for implementing Reinforcement Learning agents, which was developed by the Research Engineering team at DeepMind.
http://bit.ly/2O8nfKu
GitHub
GitHub - deepmind/trfl: TensorFlow Reinforcement Learning
TensorFlow Reinforcement Learning. Contribute to deepmind/trfl development by creating an account on GitHub.