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Data Phoenix
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Data Phoenix is your best friend in learning and growing in the data world!
We publish digest, organize events and help expand the frontiers of your knowledge in ML, CV, NLP, and other aspects of AI. Idea and implementation: @dmitryspodarets
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Object detection and image classification with Google Coral USB Accelerator

In this tutorial, you will learn how to get started with Google Coral USB Accelerator, including:
- Image classification with the Coral USB Accelerator
- Image classification in video with the Google Coral Accelerator
- Object detection with the Google Coral Accelerator
- Object detection in video with the Coral USB Accelerator

http://bit.ly/2VJVxY7
TensorFlow Graphics

TensorFlow Graphics is one of the latest additions to TensorFlow, which is expected to enable research in the intersection of deep learning and computer graphics.

http://bit.ly/2VGWtfI

Github repository: http://bit.ly/2VKNElc
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
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
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