The most important step in text analysis — Natural language processing is a subfield of artificial intelligence that combines computational linguistics, statistics, machine learning, and deep learning models to allow computers to process human language and understand its context, intent, and sentiment. A generic natural language processing (NLP) model is a combination of multiple mathematical and statistical steps…

A Simple Guide For Beginners on How to Make a Clown Filter — AR (augmented reality) is the next-gen thing. It provides an interactive experience of a real-world environment where the objects that reside in the real world are enhanced by computer-generated perceptual information, sometimes across multiple sensory modalities including visual, auditory, and haptic. Facebook’s Spark AR Studio is a tool for creators…

Making an Emoji Selector Filter — Facebook’s AR Spark Studio is a tool for creators to create, publish, and manage AR Apps. It is an open-source tool by Facebook, and anyone with a Facebook account can easily access this studio. Nowadays it is mostly used for creating AR filters for both Facebook and Instagram. Do you…

Creating a Color-Graded Filter — Color LUTs are tables of RGB color values. Values of each position of the table range from 0 to 255. These color LUTs can be used to create color-graded effects across the whole scene of your effect. As a beginner you should always consider using the pre-built color LUTs that…

Learn How To Replace Backgrounds With Animation Sequences — Facebook’s Spark AR is an open-source tool built for creators to create, publish, and share Instagram and Facebook filters. This tool doesn’t require any knowledge of coding as it is a drag-and-drop tool with simple use GUI. In this blog, we will create an animated fireworks background filter in Spark…

Playing with Hue and Saturation — In this article, you will see how we can create a color filter without LUTs. We will be using the Patch editor and some patches to play around with hue and saturation to generate a color-graded effect. The effect we are building today only uses the textures and patches already…

Stacking Models For Profit — Accuracy is a major evaluation matrix for most machine learning problems. It tells how good your model is at identifying relationships and patterns between variables. It is the number of correctly predicted data points out of all the data points and it is a good practice to calculate the accuracy…

The pros and cons of various data splitting techniques in Python — In Machine Learning, the first step towards a better-performing model is clean, relevant data. As the saying goes, “garbage in, garbage out.” If your model is trained on “bad data,” (noisy, redundant, irrelevant, or dirty data) it will learn the patterns of this messy data and will not perform as…

Save Hours With The Help of Transfer Learning & Optimizers — Deep learning is a subset of machine learning that utilizes neural networks in “deep” architectures, or multiple layers, to extract information from data. Early neural networks employed relatively simple (or “shallow”) architectures, but today’s deep learning neural networks can be incredibly complex. Neural networks are designed to work much like…

Improve the performance of your regression model with these tips and tricks — Regression analysis is a powerful statistical method used to estimate the structure of a relationship between two variables by determining a line-of-best-fit among data points. The mathematical formula representing this line is then used to predict a dependent variable given one or more independent variables. Regression-based algorithms are useful in…

A guide to understanding different evaluation metrics for clustering models in machine learning — Machine learning is a subset of artificial intelligence that employs statistical algorithms and other methods to visualize, analyze and forecast data. Generally, machine learning is broken down into two subsequent categories based on certain properties of the data used: supervised and unsupervised. Supervised learning algorithms refer to those that require…

And How to Deal With Them — Data play a key role in machine learning, and the better and more relevant data you have, the more accurate the model you will build. Getting the perfect data, however, is still a dream for many data scientists. A lot of data comes from web scraping, APIs and other external…

And How to Deal With Them — Note: This is the part 2 of the series Major Problems of Machine Learning Datasets. You can read part 1 here. Outliers in data Outliers are unusual data points that differ significantly from other values in the sample of a population. Outliers sometimes represent errors in measurement or data collection, and can have…

And How to Deal With Them — Note: This is the part 3 of the series Major Problems of Machine Learning Datasets. You can read part 1 here and part 2 here. Imbalanced data Imbalanced data occurs when there is an uneven distribution of classes or labels. For example, in a credit card detection task, the number of non-fraudulent…

Data Augmentation, Dropout Layers, L1 and L2 Regularization, and Early Stopping — A neural network is a combination of different neurons, layers, weights, and biases. The first neural network was created in 1957 and named perceptron. It is similar to modern-day neural networks but it only had one layer. …

“No Data is Clean, But Most are Useful “— Dean Abbott — Natural language refers to the medium we humans use to communicate with each other, and processing simply means the conversion of data into a readable form. In short, natural language processing is a way to provide computers with the ability to understand and communicate in human language. NLP is a…

Bagging, Boosting, Stacking, Voting, and Blending — Machine learning is a subfield of artificial intelligence devoted to building systems that are able to learn and adapt without following explicit instructions. It makes use of statistical models to visualize, analyze, and forecast data. A generic machine learning model includes a dataset (used for training the model) and an…

Extracting top topics from the text — Topic modeling is an unsupervised machine learning technique for discovering “topics” that occur in a collection of documents. In recent years, as the people’s screen time around the world has increased, the amount of data produced by us has also increased. …