Bayes Theorem in One Picture - http://bit.ly/2H1LfrW

MRNet Dataset

The MRNet dataset consists of 1,370 knee MRI exams performed at Stanford University Medical Center. The dataset contains 1,104 (80.6%) abnormal exams, with 319 (23.3%) ACL tears and 508 (37.1%) meniscal tears; labels were obtained through manual extraction from clinical reports.

Link: http://bit.ly/2H3YwAg

Fast Neural Style Transfer in PyTorch

PyTorch implementation of Fast Neural Style Transfer.

Link: http://bit.ly/2H0Ob8k

Google Coral Edge TPU Board Vs NVIDIA Jetson Nano Dev board 

This article compares the hardware of the two dev kits which can be used as a Single board computer (SBC), but not an Edge TPU USB stick.

Link: http://bit.ly/2HagSj0

Easy Image Classification with TensorFlow 2.0

This article will introduce you to TensorFlow 2.0 by exploring how to apply its high-level APIs in classic image classification.

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

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

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