Dear readers!

As you might already know, we're working tirelessly to create and regularly update the collection of exciting and useful content on ML, DL, NLP, CV, etc. — Data Science Digest. Hope you love it all so far.

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Generating Images with Autoencoders

This tutorial will introduce you to unsupervised and self-supervised learning using neural networks for image generation, image augmentation, and image blending. The first part is focused on a specific type of autoencoder called a variational autoencoder.

Link: http://bit.ly/2HikL5x

Advanced Keras — Constructing Complex Custom Losses and Metrics

In this tutorial, you will learn about a simple trick that will help you construct custom loss functions in Keras which can receive arguments other than ytrue and ypred.

Link: http://bit.ly/2VtrzaI

Benchmarking Edge Computing

We will compare the following platforms: the Coral Dev Board, the NVIDIA Jetson Nano, the Coral USB Accelerator with a Raspberry Pi, the original Movidus Neural Compute Stick with a Raspberry Pi, and the second generation Intel Neural Compute Stick 2 again with a Raspberry Pi. Finally, just to add a yardstick, we’ll also run the same models on my Apple MacBook Pro (2016), which has a quad-core 2.9 GHz Intel Core i7, and a vanilla Raspberry Pi 3, Model B+ without any acceleration.

http://bit.ly/2HtRUf0

Getting started with the NVIDIA Jetson Nano

In this tutorial, you will learn how to get started with your NVIDIA Jetson Nano, including:
- First boot
- Installing system packages and prerequisites
- Configuring your Python development environment
- Installing Keras and TensorFlow on the Jetson Nano
- Changing the default camera
- Classification and object detection with the Jetson Nano

http://bit.ly/2VCmZqs

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

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