The Best Machine Learning Resources

This article is an addendum to the series «Machine Learning for Humans» a guide for getting up-to-speed on machine learning concepts in 2-3 hours.

http://bit.ly/2HDWkzK

GraphPipe

GraphPipe is a protocol and collection of software designed to simplify machine learning model deployment and to decouple it from framework-specific model implementations.

http://bit.ly/2HCCVzh

Deep learning: the final frontier for signal processing and time series analysis?

In this article, the author demonstrates a few areas where signals or time series are vital, after which he briefly reviews classical approaches and moves on to his experience with applying deep learning for biosignal analysis in Mawi Solutions and for algorithmic trading.

http://bit.ly/2HGIM6D

The Deep Learning Summit is back in Europe

Bridging the gap between the latest research advancements and real-world applications in business and society. See the full agenda here - https://bit.ly/2Eis8cO. Use code DSD for 20% off.

#reworkDL #Conference

Railyard: how we rapidly train machine learning models with Kubernetes.

Stripe uses machine learning to evaluate complex, real-world problems at scale. This post explores the challenges and decisions behind the infrastructure that enables it.

http://bit.ly/2VSB03p

The Power of Self-Learning Systems

Demis Hassabis (Co-Founder & CEO, Google DeepMind) will discuss the capabilities and power of self-learning systems. He will illustrate this with reference to some of DeepMind's recent breakthroughs.

http://bit.ly/2VWW4FY

Text Preprocessing in Python: Steps, Tools, and Examples

In this article, you will learn the basic steps of text preprocessing. These steps are needed for transferring text from human language to machine-readable format for further processing. You will also learn about text preprocessing tools.

http://bit.ly/2JY4x4z

Hello everyone!

DataScience Digest is now on ProductHunt! Are you amazed? If so, make sure to check out us there.

Here's the link: https://www.producthunt.com/posts/datascience-digest.

Any feedback and comments are appreciated. So, let us know what you want to change or improve.

Top 8 trends from ICLR 2019: http://bit.ly/2W2i7Lq

PyTorch internals

This post is a long-form essay version of a talk about PyTorch internals by Edward Z. Yang that he gave at the PyTorch NYC meetup on May 14, 2019.

http://bit.ly/2HWXHJX

Real-time object detection part 1: Understanding SSD

This post explains the inner workings of the Single-Shot MultiBox Detector and provides a detailed code walkthrough.

http://bit.ly/2W3qWEV

​​AI Conference Kyiv 2019

On June 4, Smile-Expo will organize the second AI Conference in Kyiv – a major event dedicated to the integration of AI in business. Presentations from speakers, successful cases, smart products, and networking – the event will become a place to gain experience from the specialists who have been using AI technologies in their operations.

The conference will feature 3 theme blocks:
1. AI and machine learning.
2. IoT and data analysis.
3. Automation and chatbots.

The event will end with a roundtable dedicated to successful business development using AI, Machine Learning and IoT.

Attendees: developers, marketers, managers, analysts, and everyone who wants to improve their business with the help of AI technologies.

Tickets with a discount: a promo code AIPR20 provides a 20% discount on tickets. Enter the promo code on the registration page.

Website: https://bit.ly/2HDbUgm

InterpretML

InterpretML is an open-source package from Microsoft for training interpretable models and explaining blackbox systems.

http://bit.ly/2WbF52M

Data Science Cheatsheets:
- Artificial Intelligence
- Big Data Analytics
- Big Data
- Data Engineering
- Data Mining
- Data Science
- Data Visualization
- Deep Learning
- Machine Learning
- SQL
- Python
- etc

http://bit.ly/2I3MgQQ

How to Deploy Machine Learning Models

In this article, you will learn how to deploy machine learning models into production. It's relatively high-level, but there are links throughout to go deeper. Includes a discussion of the complexities involved, design considerations, tooling, testing, developments to watch, etc.

http://bit.ly/2KfLYcu

Artificial Intelligence cheatsheets for Stanford’s CS 221

This repository sums up all the important stuff covered in Stanford’s CS 221 Artificial Intelligence course, and includes:
— Cheatsheets for each field of artificial intelligence
— All elements of the above combined in the ultimate compilation of concepts to have at hand all the time.

http://bit.ly/2Wzazzs

AUTOML: METHODS, SYSTEMS, CHALLENGES

This open access book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects denoscriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems.

http://bit.ly/2WEdB5q

The Best and Most Current of Modern Natural Language Processing

A useful collection of resources to catch up on the latest trends in natural language processing. In addition to selected research papers, this article includes links to introductory posts, recommended blogs, online courses, and books.

http://bit.ly/2KngeBZ

​​June 11 Kyiv is going to host AWS Dev Day! Are you all fired up?

This international conference for techies is part of the global series of AWS Dev Day events, featuring three major tracks:
— Analytics & Machine Learning
— Backends & Architecture
— Modern App Development

Among all of that, the participants will attend ten keynotes delivered by strong AWS pros, engage with AWS architects at the Ask an Architect session, and visit the fabled Cost Optimization Booth.

The keynotes will be of value to Machine Learning Engineers, Data Scientists, DevOps Engineers, Solution Architects, Software Engineers, and everyone interested in the AWS ecosystem.

When: Tuesday, June 11, 9 AM — 5 PM
Where: Mercure Congress Centre

The participation is free: http://bit.ly/2KvMqn9

Testing TensorFlow Lite image classification model

In this post, we will demonstrate major differences between TensorFlow, TensorFlow Lite and quantized TensorFlow Lite (with post-training quantization) models. This should help you with early models debugging when something goes really wrong.

http://bit.ly/2WFAdmg