Radial Basis Function Networks – Regression for ML
Machine learning is an expansive field – one often made better by techniques common to data science like regression. In this tutorial, we’re going to explore the topic of neural … Read more
Mohit Deshpande
Clustering with Gaussian Mixture Models – Data Science & ML
Gaussian Mixture Models are an essential part of data analysis – but do you know how they work? In this article, we’ll seek to demystify how to analyze “clusters” of … Read more
Face Recognition with Eigenfaces – Computer Vision Tutorial
Face recognition – or the ability of computers to recognize faces and facial features – is an imminent concern to our future. In this tutorial, we’re going to explore face … Read more
Perceptrons: The First Neural Networks for Machine Learning
Machine learning is not only a fascinating topic but one with a variety of approaches. Neural networks, one such approach, have come to the forefront largely due to their accessibility … Read more
Text Classification with Naive Bayes – Python Tutorial
If asked how your email sorted spam emails from legitimate emails, what would you say? Do you think you could build a spam filter yourself? Hidden throughout our lives is … Read more
Classification with Support Vector Machines
One of the most widely-used and robust classifiers is the support vector machine. Not only can it efficiently classify linear decision boundaries, but it can also classify non-linear boundaries and … Read more
Creating and Presenting Dialogs
In this post, we’re going to learn how to build and post dialogs to the user. Our app will create two dialogs. One of them will be a plain dialog … Read more
An Overview of Reinforcement Learning: Teaching Machines to Play Games
Think back to the time you first learned a skill: driving a car, playing an instrument, cooking a recipe. Let’s consider the example of playing chess. Initially, it might have … Read more
Supervised Learning – Using Decision Trees to Classify Data
One challenge of neural or deep architectures is that it is difficult to determine what exactly is going on in the machine learning algorithm that makes a classifier decide how … Read more
Advanced Recurrent Neural Networks
Recurrent Neural Networks (RNNs) are used in all of the state-of-the-art language modeling tasks such as machine translation, document detection, sentiment analysis, and information extraction. Previously, we’ve only discussed the … Read more
Understanding Advanced Convolutional Neural Networks
Convolutional Neural Networks (CNNs) are used in all of the state-of-the-art vision tasks such as image classification, object detection and localization, and segmentation. Previously, we’ve only discussed the LeNet-5 architecture, … Read more
Dimensionality Reduction
Dimensionality Reduction is a powerful technique that is widely used in data analytics and data science to help visualize data, select good features, and to train models efficiently. We use … Read more
Recurrent Neural Networks for Language Modeling
Many neural network models, such as plain artificial neural networks or convolutional neural networks, perform really well on a wide range of data sets. They’re being used in mathematics, physics, … Read more
All About Autoencoders
Data compression is a big topic that’s used in computer vision, computer networks, computer architecture, and many other fields. The point of data compression is to convert our input into … Read more
Data Clustering with K-Means
Determining data clusters is an essential task to any data analysis and can be a very tedious task to do manually! This task is nearly impossible to do by hand … Read more
A Guide to Improving Deep Learning’s Performance
Although deep learning has great potential to produce fantastic results, we can’t simply leave everything to the learning algorithm! In other words, we can’t treat the model as some black-box, … Read more
Introduction to Convolutional Neural Networks for Vision Tasks
Neural networks have been used for a wide variety of tasks across different fields. But what about image-based tasks? We’d like to do everything we could with a regular neural … Read more
Complete Guide to Deep Neural Networks – Part 2
Read Part 1 here. Last time, we formulated our multilayer perceptron and discussed gradient descent, which told us to update our parameters in the opposite direction of the gradient. Now … Read more