[unable to retrieve full-text content]We find 6 tools form the modern open source Data Science / Machine Learning ecosystem; examine whether Python declared victory over R; and review which tools are most associated with Deep Learning and Big Data.
Original Post: The 6 components of Open-Source Data Science/ Machine Learning Ecosystem; Did Python declare victory over R?
TensorFlow
Deep Learning With Apache Spark: Part 2
[unable to retrieve full-text content]In this article I’ll continue the discussion on Deep Learning with Apache Spark. I will focus entirely on the DL pipelines library and how to use it from scratch.
Original Post: Deep Learning With Apache Spark: Part 2
Python eats away at R: Top Software for Analytics, Data Science, Machine Learning in 2018: Trends and Analysis
[unable to retrieve full-text content]Python continues to eat away at R, RapidMiner gains, SQL is steady, Tensorflow advances pulling along Keras, Hadoop drops, Data Science platforms consolidate, and more.
Original Post: Python eats away at R: Top Software for Analytics, Data Science, Machine Learning in 2018: Trends and Analysis
Top Stories, May 14-20: Data Science vs Machine Learning vs Data Analytics vs Business Analytics; Implement a YOLO Object Detector from Scratch in PyTorch
[unable to retrieve full-text content]Also: An Introduction to Deep Learning for Tabular Data; 9 Must-have skills you need to become a Data Scientist, updated; GANs in TensorFlow from the Command Line: Creating Your First GitHub Project; Complete Guide to Build ConvNet HTTP-Based Application
Original Post: Top Stories, May 14-20: Data Science vs Machine Learning vs Data Analytics vs Business Analytics; Implement a YOLO Object Detector from Scratch in PyTorch
GANs in TensorFlow from the Command Line: Creating Your First GitHub Project
[unable to retrieve full-text content]In this article I will present the steps to create your first GitHub Project. I will use as an example Generative Adversarial Networks.
Original Post: GANs in TensorFlow from the Command Line: Creating Your First GitHub Project
Complete Guide to Build ConvNet HTTP-Based Application using TensorFlow and Flask RESTful Python API
[unable to retrieve full-text content]In this tutorial, a CNN is to be built, and trained and tested against the CIFAR10 dataset. To make the model remotely accessible, a Flask Web application is created using Python to receive an uploaded image and return its classification label using HTTP.
Original Post: Complete Guide to Build ConvNet HTTP-Based Application using TensorFlow and Flask RESTful Python API
How I Used CNNs and Tensorflow and Lost a Silver Medal in Kaggle Challenge
[unable to retrieve full-text content]I joined the competition a month before it ended, eager to explore how to use Deep Natural Language Processing (NLP) techniques for this problem. Then came the deception. And I will tell you how I lost my silver medal in that competition.
Original Post: How I Used CNNs and Tensorflow and Lost a Silver Medal in Kaggle Challenge
How I Used CNNs and Tensorflow and Lost a Silver Medal in Kaggle Challenge
[unable to retrieve full-text content]I joined the competition a month before it ended, eager to explore how to use Deep Natural Language Processing (NLP) techniques for this problem. Then came the deception. And I will tell you how I lost my silver medal in that competition.
Original Post: How I Used CNNs and Tensorflow and Lost a Silver Medal in Kaggle Challenge
Ultra-compact workstation for top deep learning frameworks
[unable to retrieve full-text content]For workstation development platforms purpose-built for Tensorflow, PyTorch, Caffe2, MXNet, and other DL frameworks, the solution is BOXX. We’re bringing deep learning to your deskside with the all-new APEXX W3!
Original Post: Ultra-compact workstation for top deep learning frameworks
Introducing the CVPR 2018 On-Device Visual Intelligence Challenge
Posted by Bo Chen, Software Engineer and Jeffrey M. Gilbert, Member of Technical Staff, Google ResearchOver the past year, there have been exciting innovations in the design of deep networks for vision applications on mobile devices, such as the MobileNet model family and integer quantization. Many of these innovations have been driven by performance metrics that focus on meaningful user experiences in real-world mobile applications, requiring inference to be both low-latency and accurate. While the accuracy of a deep network model can be conveniently estimated with well established benchmarks in the computer vision community, latency is surprisingly difficult to measure and no uniform metric has been established. This lack of measurement platforms and uniform metrics have hampered the development of performant mobile applications.Today, we are happy to announce the On-device Visual Intelligence Challenge (OVIC), part of the Low-Power Image Recognition…
Original Post: Introducing the CVPR 2018 On-Device Visual Intelligence Challenge