Collection of Interactive Machine Learning Examples
Seedbank is a registry and search engine for Colab notebooks for machine learning, enabling rapid exploration and learning. You can browse the site and use, experiment with, and fork Colab notebooks. The forked notebooks are stored in your Google Drive and you can share them just like any other Google Docs.
http://bit.ly/2ZPFWDE
Seedbank is a registry and search engine for Colab notebooks for machine learning, enabling rapid exploration and learning. You can browse the site and use, experiment with, and fork Colab notebooks. The forked notebooks are stored in your Google Drive and you can share them just like any other Google Docs.
http://bit.ly/2ZPFWDE
Random Forest vs AutoML
In this article, the author demonstrates how to prepare the data and train the Random Forest model on an Adult dataset with python and scikit-learn. Using the same dataset, he shows how to train Random Forest with AutoML using mljar-supervised.
http://bit.ly/2Ygkmb9
In this article, the author demonstrates how to prepare the data and train the Random Forest model on an Adult dataset with python and scikit-learn. Using the same dataset, he shows how to train Random Forest with AutoML using mljar-supervised.
http://bit.ly/2Ygkmb9
TensorWatch
TensorWatch is a debugging and visualization tool designed for data science, deep learning and reinforcement learning from Microsoft Research. It works in Jupyter Notebook to show real-time visualizations of your machine learning training and perform several other key analysis tasks for your models and data.
http://bit.ly/2YhR8ZG
TensorWatch is a debugging and visualization tool designed for data science, deep learning and reinforcement learning from Microsoft Research. It works in Jupyter Notebook to show real-time visualizations of your machine learning training and perform several other key analysis tasks for your models and data.
http://bit.ly/2YhR8ZG
GitHub
GitHub - microsoft/tensorwatch: Debugging, monitoring and visualization for Python Machine Learning and Data Science
Debugging, monitoring and visualization for Python Machine Learning and Data Science - microsoft/tensorwatch
And Voilà!
In this article, you will learn how Voilà turns Jupyter notebooks into standalone web applications.
http://bit.ly/2YnB74m
In this article, you will learn how Voilà turns Jupyter notebooks into standalone web applications.
http://bit.ly/2YnB74m
Jupyter Blog
And voilà!
… from Jupyter notebooks to standalone applications and dashboards
Webinar «Kubeflow, MLFlow, and Beyond — Augmenting ML Delivery»
Organized in collaboration with #ODSC, this free webinar will provide insights on how to design a reference machine learning workflow, as well as an overview of open source tools used to automate ML workflow.
The attendees will go away with a deeper view of traps and pitfalls they may come across at every stage of ML lifecycle and get a reference implementation and automation of ML Workflow.
When: July 16, 1 pm — 2 pm EST
Speaker: Stepan Pushkarev, CTO of Provectus
Act fast to register: http://bit.ly/MLFlowWebinar
Learn more: https://www.facebook.com/events/1183366371834820
Organized in collaboration with #ODSC, this free webinar will provide insights on how to design a reference machine learning workflow, as well as an overview of open source tools used to automate ML workflow.
The attendees will go away with a deeper view of traps and pitfalls they may come across at every stage of ML lifecycle and get a reference implementation and automation of ML Workflow.
When: July 16, 1 pm — 2 pm EST
Speaker: Stepan Pushkarev, CTO of Provectus
Act fast to register: http://bit.ly/MLFlowWebinar
Learn more: https://www.facebook.com/events/1183366371834820
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.
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