[unable to retrieve full-text content]Also How to Implement a YOLO (v3) Object Detector from Scratch in PyTorch; Frameworks for Approaching the Machine Learning Process.
Original Post: KDnuggets™ News 18:n21, May 23: Python eats away at R; Top 2018 Analytics, Data Science, Machine Learning tools; 9 Must-have skills for a Data Scientist
[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]The best way to go about learning object detection is to implement the algorithms by yourself, from scratch. This is exactly what we’ll do in this tutorial.
Original Post: How to Implement a YOLO (v3) Object Detector from Scratch in PyTorch: Part 1
[unable to retrieve full-text content]PyTorch Tensor Basics; Top 7 Data Science Use Cases in Finance; The Executive Guide to Data Science and Machine Learning; Data Augmentation: How to use Deep Learning when you have Limited Data
Original Post: KDnuggets™ News 18:n20, May 16: PyTorch Tensor Basics; Data Science in Finance; Executive Guide to Data Science
[unable to retrieve full-text content]PyTorch includes an automatic differentiation package, autograd, which does the heavy lifting for finding derivatives. This post explores simple derivatives using autograd, outside of neural networks.
Original Post: Simple Derivatives with PyTorch
[unable to retrieve full-text content]This is an introduction to PyTorch’s Tensor class, which is reasonably analogous to Numpy’s ndarray, and which forms the basis for building neural networks in PyTorch.
Original Post: PyTorch Tensor Basics
[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
[unable to retrieve full-text content]Playlists, individual tutorials (not part of a playlist) and online courses on Deep Learning (DL) in Python using the Keras, Theano, TensorFlow and PyTorch libraries. Assumes no prior knowledge. These videos cover all skill levels and time constraints!
Original Post: Top 10 Videos on Deep Learning in Python
[unable to retrieve full-text content]We rank 23 open-source deep learning libraries that are useful for Data Science. The ranking is based on equally weighing its three components: Github and Stack Overflow activity, as well as Google search results.
Original Post: Ranking Popular Deep Learning Libraries for Data Science
[unable to retrieve full-text content]Also #MachineLearning: Understanding Decision Tree Learning; #PyTorch tutorial distilled – Moving from #TensorFlow to PyTorch.
Original Post: Top KDnuggets tweets, Oct 04-10: Using #MachineLearning to Predict, Explain Attrition; Tidyverse, an opinionated #DataScience Toolbox in R