[unable to retrieve full-text content]R is a great choice for manipulating, cleaning, summarizing, producing probability statistics, and so on. In addition, it’s not going away anytime soon, it is platform independent, so what you create will run almost anywhere, and it has awesome help resources.
Original Post: How to tackle common data cleaning issues in R
[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]Adversarial Neural Networks are oddly named since they actually cooperate to make things.
Original Post: Generative Adversarial Neural Networks: Infinite Monkeys and The Great British Bake Off
[unable to retrieve full-text content]This post is a summary of 2 distinct frameworks for approaching machine learning tasks, followed by a distilled third. Do they differ considerably (or at all) from each other, or from other such processes available?
Original Post: Frameworks for Approaching the Machine Learning Process
[unable to retrieve full-text content]Optimization is a technique for finding out the best possible solution for a given problem for all the possible solutions. Optimization uses a rigorous mathematical model to find out the most efficient solution to the given problem.
Original Post: Optimization Using R
[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
[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]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
[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
[unable to retrieve full-text content]Like Wikipedia, there are all kinds of data stored in Wikidata. As such, when you are looking for a specific dataset or if you want to answer a curious question, it can be a good start looking for that data at Wikidata first.
Original Post: A Brief Introduction to Wikidata