A sequence to sequence model lies behind numerous systems which we use on a daily basis. In this blog, we will try to give a short and concise explanation of the sequence to sequence model and apply it to achieve significant results on pretty complex tasks of machine translation.
Naive Bayes belongs to the family of probabilistic algorithms that take advantage of Probability Theory and Bayes Theorem to predict the class. Let's learn how it works!!
In supervised machine learning, we are building predictive models that predict variance of some variable, using variance of some other variables. To model these connections, we have to learn Conditional Probabilities and Independence of events.
In many real-world data applications, often we encounter scenarios where each data point may belong to multiple classes. A multilabel classifier is trained to predict the K most likely classes among N possible classes. The article focuses on solving multi-label text classification problems using the Hierarchical Attention Network.
The article covers step-by-step proof for proving the convexity of a mean squared error loss function. The Ability to test convexity for different loss functions can come in handy especially with more and more exotic loss functions being proposed every day.
The convexity property of a function unlocks a crucial advantage where the local minima of a convex function is also a global minima. This ensures that a model can be trained where the loss function is minimized to its globally minimum value. In this blog post, we shall work through the concepts needed to prove the convexity of a function.
As a part of "Getting Acquainted with Deep Learning Frameworks" series, in the article we shall explore Pytorch Library. Pytorch is a deep learning library developed by Facebook Researchers. The focus of this article will be to highlight the steps involved in training a multiclass classifier using Pytorch.
Getting started with deep learning frameworks often involves a steep learning curve. This article is aimed at providing a gentle introduction to building DNN models with Keras which can be scaled and customized as per dataset. The focus will be on understanding the syntax and good practices involved in building a complex DNN model rather than achieving accuracy.
Extracting Key Phrases from Textual data is a problem faced across domains. In this Article, we shall explore an approach which leverages Google's pagerank algorithm to solve the problem. Basic knowledge of Linear Algebra, Markov Chains and Text Parsing would help in comprehending the content.
With all the buzz around Indian Premier League (IPL), FIFA World Cup, Cricket World Cup-2019, fantasy sports websites like Dream11 are gaining traction. Fanatsy Sports Portal allows sports fans like me to be a part of it. Having said that, it is very difficult to keep track of all the players and their performance across the sports. In this Tutorial, I will try to automate the fantasy selection process so that the probability of winning a fantasy league is maximized.
Image classification using deep learning and its applications has been proving its worth across business verticals.However, building an image centric AI product is often marred by Unavailability of large amounts of data, poor quality of images, etc. Often the metadata / textual data associated with these images are ignored. In this article, we shall try and build a DL model that can leverage image as well as textual data.
Logistic Regression is one the most basic algorithm on ML. With the likes of sklearn providing an off the shelf implementation of Linear Regression, it is very difficult to gain an insight on what really happens under the hood. This tutorial is aimed at implementing Logistic Regression from scratch in python using Numpy.
Linear Regression is one the most basic algorithm on ML. With the likes of sklearn providing an off the shelf implementation of Linear Regression, it is very difficult to gain an insight on what really happens under the hood. This tutorial is aimed at implementing Linear Regression from scratch in python using Numpy.