Tinker With a Neural Network Right Here in Your Browser. Don’t Worry, You Can’t Break It. We Promise.
toy 2d classification with 2-layer neural network
An In-Depth Guide – Overview, Goals, Learning Types, and Algorithms
Data and information on the web is growing exponentially. All of us today use Google as our first source of knowledge – be it about finding reviews about a place to understanding a new term. All this information is available on the web already. With the amount of data available over the web, it opens new horizons of possibility for a Data Scientist. I strongly believe web scrapping is a must have skill for any data scientist. In today’s world, all the data that you need is already available on the internet, the only thing limiting you from using it is the ability to access it. With the help of this article, you will be able to overcome that barrier as well. Most of the data available over the web is not readily available. It is present in an unstructured format (HTML format) and is not downloadable. Therefore, it requires knowledge & expertise to use this data. In this article, I am going to take you through the process of web scrapping in R. With this article, you will gain expertise to use any type of data available over the internet.
When you first start learning about data science, one of the first things you learn about are classification algorithms. The concept behind these algorithms is pretty simple: take some information about a data point and place the data point in the correct group or class.
Can you see the random forest for its leaves? The Leaf Classification playground competition ran on Kaggle from August 2016 to February 2017. Kagglers were challenged to correctly identify 99 classes of leaves based on images and pre-extracted features. In this winner’s interview, Kaggler Ivan Sosnovik shares his first place approach. He explains how he had better luck using logistic regression and random forest algorithms over XGBoost or convolutional neural networks in this feature engineering competition.
Structural Equation Modeling (SEM) is an extremely broad and flexible framework for data analysis, perhaps better thought of as a family of related methods rather than as a single technique. What is its relevance to Marketing Research?
At SVDS, our R&D team has been investigating different deep learning technologies, from recognizing images of trains to speech recognition. We needed to build a pipeline for ingesting data, creating a model, and evaluating the model performance. However, when we researched what technologies were available, we could not find a concise summary document to reference for starting a new deep learning project.
I want to give a quick tutorial on fitting Linear Mixed Models (hierarchical models) with a full variance-covariance matrix for random effects (what Barr et al 2013 call a maximal model) using Stan.