Distributed Deep Learning Pipelines with PySpark and Keras

An easy approach to data pipelining using PySpark and doing distributed deep learning with Keras.

http://bit.ly/2ZFao3s

Machine Learning for Everyone

The best general intro article about Machine Learning. Only real-world problems, practical solutions, simple language, and no high-level theorems. One and for everyone.

http://bit.ly/2ZGQBRd

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

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

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

And Voilà!

In this article, you will learn how Voilà turns Jupyter notebooks into standalone web applications.

http://bit.ly/2YnB74m

​​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

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

​​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

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

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

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

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

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

How To Become a Data Engineer

A list of useful resources to help you learn Data Engineering from scratch

http://bit.ly/2G0H53V

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

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

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

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

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