[unable to retrieve full-text content]This post discusses a variety of contemporary Deep Meta Learning methods, in which meta-data is manipulated to generate simulated architectures. Current meta-learning capabilities involve either support for search for architectures or networks inside networks.
Original Post: Taxonomy of Methods for Deep Meta Learning
[unable to retrieve full-text content]We examine which top tools are “friends”, their Python vs R bias, and which work well with Spark/Hadoop and Deep Learning, and identify an emerging Big Data Deep Learning ecosystem.
Original Post: Emerging Ecosystem: Data Science and Machine Learning Software, Analyzed
[unable to retrieve full-text content]Learn Data Science skills you need for free; Understanding Deep Learning Requires Re-thinking Generalization; K-means Clustering with Tableau – Call Detail Records; The Machine Learning Algorithms Used in Self-Driving Cars.
Original Post: KDnuggets™ News 17:n24, Jun 21: Learn Data Science skills you need for free; Understanding Deep Learning Requires Re-thinking Generalization
[unable to retrieve full-text content]This new course with limited places will focus on AI design (product, development and Data) for the fintech industry and will be taught online by Ajit Jaokar and Jakob Aungiers.
Original Post: AI for fintech course – Early discounts and limited places
[unable to retrieve full-text content]Currently we are expanding our team to support projects that focus on Artificial Intelligence and Cognitive analytics. We are looking for an expert in this field from business/academia with extensive experience in this area.
Original Post: Microsoft: Principal Data Scientist (Artificial Intelligence & Deep Learning)
[unable to retrieve full-text content]What is it that distinguishes neural networks that generalize well from those that don’t? A satisfying answer to this question would not only help to make neural networks more interpretable, but it might also lead to more principled and reliable model architecture design.
Original Post: Understanding Deep Learning Requires Re-thinking Generalization
Posted by Jonathan Huang, Research Scientist and Vivek Rathod, Software Engineer(Cross-posted on the Google Open Source Blog)At Google, we develop flexible state-of-the-art machine learning (ML) systems for computer vision that not only can be used to improve our products and services, but also spur progress in the research community. Creating accurate ML models capable of localizing and identifying multiple objects in a single image remains a core challenge in the field, and we invest a significant amount of time training and experimenting with these systems. Last October, our in-house object detection system achieved new state-of-the-art results, and placed first in the COCO detection challenge. Since then, this system has generated results for a number of research publications1,2,3,4,5,6,7 and has been put to work in Google products such as NestCam, the similar items and style ideas feature in Image Search and…
Original Post: Supercharge your Computer Vision models with the TensorFlow Object Detection API
[unable to retrieve full-text content]In this article we will focus — basic deep learning using Keras and Theano. We will do 2 examples one using keras for basic predictive analytics and other a simple example of image analysis using VGG.
Original Post: Medical Image Analysis with Deep Learning , Part 3