Image Synthesis using Pixel CNN based Autoregressive Generative Models Recent advances in the field of deep learning have led to the development of complex generative models that are capable of generating high quality content in the form of text, audio, pictures, videos and so on. Generative models that make use of deep learning architectures to tackle the task… Read More »
What are Autoregressive Generative Models? The term ‘autoregressive’ is taken from the field of time-series forecasting frameworks. Where, In order to make a future prediction, a model considers all the past observations in a timely manner. Autoregressive generative models are also quite similar in nature. They also take help from all their past predictions in order to decide… Read More »
Building blocks of deep generative models In this article, we will learn about some important concepts that are important to learn if we want to thoroughly understand how a deep generative learning model works. We will learn about some probabilistic concepts that help the generative learning frameworks in learning data distributions. These concepts are basic building blocks of… Read More »
Generative Learning refers to a special class of statistical models that are capable ofgenerating content that is very hard to distinguish from the reality (or fake content thatlooks real). The generated content could be poems, images, music, songs, videos, 3Dobjects or content from some other domain we could imagine. A domain is nothing but a fancy word for… Read More »
Generative Learning and its Differences from the Discriminative Learning Generative Learning refers to a special class of statistical models that are capable ofgenerating content that is very hard to distinguish from the reality (or fake content thatlooks real). The generated content could be poems, images, music, songs, videos, 3Dobjects or content from some other domain we could imagine.… Read More »
In my previous article on ‘Sentiment Analysis with Python: Bag of Words‘, We compared the results of three traditional machine learning sentiment classification algorithms using bag-of-words features(from scratch). This is my second article on sentiment analysis in continuation of that and this time we are going to experiment with TFIDF features for the task of Sentiment Analysis on… Read More »
Sentiment Analysis Overview Sentiment Analysis(also known as opinion mining or emotion AI) is a common task in NLP (Natural Language Processing). It involves identifying or quantifying sentiments of a given sentence, paragraph, or document that is filled with textual data. Sentiment Analysis techniques are widely applied to customer feedback data (ie., reviews, survey responses, social media posts). Sentiment Analysis has proved… Read More »
Overview Training a Deep Learning model (or any machine learning model in fact) is all about bringing the model predictions (model output) close to the real output(Ground truth) for a given set of input-output pairs. Once the model’s results are close to the real results our job is done. To understand how close model predictions are with respect… Read More »
Data is like a fuel to a Data Scientist. Any study or research work requires a good amount of quality data. The term ‘good amount of quality data’ changes with the kind of study one wants to do. Various sampling techniques are there to get you just that. As a researcher, you may want to study-different animals, changing… Read More »
If you have some experience in solving the classification problems then you must have encountered imbalanced data several times. An imbalanced dataset is a dataset that has an imbalanced distribution of the examples of different classes. Consider a binary classification problem where you have two classes 1 and 0 and suppose more than 90% of your training examples… Read More »