[unable to retrieve full-text content]A personal account as to why 2018 is going to be a fun year for machine learning engineers.
Original Post: What is it like to be a machine learning engineer in 2018?
[unable to retrieve full-text content]In this blog I am going to talk about the issues related to initialization of weight matrices and ways to mitigate them. Before that, let’s just cover some basics and notations that we will be using going forward.
Original Post: Deep Learning Best Practices – Weight Initialization
[unable to retrieve full-text content]5 highlights and thoughts from my attendance to Strata London 2018.
Original Post: 5 Key Takeaways from Strata London 2018
[unable to retrieve full-text content]In anticipation of his upcoming conference co-presentation at Deep Learning World in Las Vegas, June 3-7, we asked Abbas Chokor, Staff Data Scientist at Seagate Technology, a few questions about his work in deep learning.
Original Post: Interview: How Seagate Technology Makes Great Use of Deep Learning – Last Call to Register for Deep Learning World
[unable to retrieve full-text content]Geoffrey Hinton, one of the fathers of Deep Learning, will be back to share his most recent and cutting-edge research progressions, and will be joined by other top researchers. Save 20% on Early Bird passes when you sign up before 15 June w. code KDNUGGETS. Also check Women in AI dinner series and get new white paper on Ethical implications of AI.
Original Post: Deep Learning Summit, Toronto featuring Geoff Hinton – save with KDnuggets
[unable to retrieve full-text content]CRISP-DM methodology is a must teach to explain analytics project steps. This article purpose it to complement it with specific chart flow that explain as simply as possible how it is more likely used in descriptive analytics, classic machine learning or deep learning.
Original Post: Descriptive analytics, machine learning, and deep learning viewed via the lens of CRISP-DM
[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
[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
[unable to retrieve full-text content]Download the report Find the Right Accelerator for your Deep Learning Needs to learn how I&O leaders must deliver effective machine learning infrastructures that effectively balance performance, cost, and functionality while minimizing complexity.
Original Post: Find the Right Accelerator for Your Deep Learning Needs
[unable to retrieve full-text content]This post will discuss a technique that many people don’t even realize is possible: the use of deep learning for tabular data, and in particular, the creation of embeddings for categorical variables.
Original Post: An Introduction to Deep Learning for Tabular Data