Author: Aayush Srivastava

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Sparse Matrices: What makes them important for Machine Learning

Reading Time: 4 minutes Dense Matrix and Sparsity of the Matrix There are two common matrix types: dense and sparse. The main difference is that sparse metrics have many null values ​​and dense metrics have none. Below is an example of a 4-by-4 sparse matrix. Reasons to use Sparse Matrix representation Storage Since there are fewer non-zero elements than zero, less memory is available to store only those elements. Continue Reading

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Tokenization in a Nutshell – Tools, Types

Reading Time: 4 minutes The entire process of converting a set of text (paragraphs) into a set of sentences or a set of words is called tokenization. It can consist of individual words, characters, sentences, or paragraphs. One of the important steps to perform in the NLP pipeline. Convert unstructured text into a suitable data format using tokenization in a nutshell along with its tools, types. One of the Continue Reading

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What Are Transformers In NLP And It’s Advantages

Reading Time: 4 minutes NLP’s Transformer is a new architecture that aims to solve tasks sequence-to-sequence while easily handling long-distance dependencies. Computing the input and output representations without using sequence-aligned RNNs or convolutions and it relies entirely on self-attention. Lets look in detail what are transformers. The Basic Architecture In general, the Transformer model is based on the encoder-decoder architecture. The encoder is the gray rectangle on the left Continue Reading

A Simple Guide to Data Preprocessing in Machine Learning

Reading Time: 5 minutes Machine learning algorithms are completely data-dependent as they are the most important aspect of enabling model training. On the other hand, if you don’t understand this data before feeding it to the ML algorithm, the machine becomes useless. Simply put, you always need to provide the right data due to that preparing data in machine learning with the appropriate scale, format, and meaningful attributes for Continue Reading

A Complete Guide To Recurrent Neural Network

Reading Time: 5 minutes Recurrent neural networks are a type of deep learning-oriented algorithm that follows a sequential approach. Neural networks always assume that each input and output is independent of all other layers. This type of neural network is recurrent neural network because it performs mathematical calculations in a sequence. Neural networks imitate the function of the human brain in the fields of AI, machine learning, and deep Continue Reading

Complete Guide to Single Layer Perceptron with Implementation

Reading Time: 4 minutes To understand the single-layer perceptron, it is important to understand the artificial neural network (ANN). An artificial neural network is an information processing system whose mechanism is inspired by the function of biological neural circuits. Artificial neural networks have many interconnected computing units. The schematic diagram of the artificial neural network is as follows. This figure shows that the hidden entity is communicating with the Continue Reading

Introduction to Ensemble Learning

Reading Time: 4 minutes Ensemble methods are techniques that create multiple models and then combine them to produce improved results. Ensemble learning usually produces more accurate solutions than a single model would. This has been the case in a number of machine learning competitions and, where the winning solutions used ensemble methods. Ensemble methods You must ensure that your models are independent of one another and when creating a Continue Reading

Introduction to Machine Learning Lifecycle

Reading Time: 3 minutes Building a machine learning model is an iterative process. For a successful deployment, most of the steps are replicated several times to achieve optimal results. The model must sustain after deployment and adapted to changing environment. Let’s look at the details of the lifecycle of a machine learning model. What is machine learning lifecycle? The machine learning lifecycle is the process of developing, deploying, and Continue Reading

Hyperparameter Optimization and Tuning

Reading Time: 3 minutes A Machine Learning model is defined as a mathematical model with a number of parameters that need to be learned from the data. By training a model with existing data, we are able to fit the model parameters.However, there is another kind of parameters, known as Hyperparameters. Hyperparameters contain the data that govern the training process itself These parameters express important properties of the model such Continue Reading

Natural Language Understanding(NLU)

Reading Time: 4 minutes What is NLU? One easy way to understand NLU is by looking at available consumer services and business products that model natural language understanding. For example, Apple’s Siri or Amazon’s Alexa performs natural language understanding work in the context of hearing and deciphering user inputs. A similar natural language understanding engine is built into Amazon “Lex,” an enterprise service for building machine learning platforms. By Continue Reading

An Intro to Chatbot Development using RASA

Reading Time: 4 minutes What is RASA? RASA is an open-source chatbot framework based on machine learning. With the help of it, we can easily create highly accurate and sophisticated chatbots and can easily integrate these chatbots with our website, Telegram, Facebook, etc. Before we get into it, let’s look into some simple concepts that we should know while creating a chatbot. Query A query is the user message Continue Reading

A Simple Guide to OCR using Pytesseract

Reading Time: 2 minutes What is OCR OCR is an acronym for optical character recognition. It is a widespread technology to recognize text inside images, such as scanned documents and photos. OCR technology is used to convert virtually any kind of image containing written text (typed, handwritten, or printed) into machine-readable text data.  OCR using Pytesseract Python-tesseract is a wrapper for Google’s Tesseract OCR engine. It can read any Continue Reading

DBSCAN Clustering Algorithm

Reading Time: 4 minutes What is Clustering? Clustering, often known as cluster analysis, is an unsupervised machine learning task. Using a clustering algorithm entails providing the algorithm with a large amount of unlabeled data and allowing it to locate whatever groupings in the data it can. The names given to these groups are clusters. A cluster is a collection of data points that are related to one another based Continue Reading