Support Vector Machine (SVM) Algorithm

Support Vector Machine (SVM) Algorithm

Introduction Support Vector Machine (SVM) is a supervised machine learning algorithm. These are a set of supervised learning methods used for classification, regression, and outliers detection. But, it is generally used in classification problems. We plot each data item as a point in n-dimensional space in the SVM algorithm. Where n is a number of … Read more

Encoder-Decoder Sequence-to-Sequence Models

Encoder-Decoder Sequence-to-Sequence Models

Introduction Encoder-Decoder Sequence-to-Sequence Models are famous for diverse tasks. These models are a distinctive class of Recurrent Neural Network architectures. We often use them to solve complex Language problems. For example; Machine translation Video captioning Image captioning Question answering Creating Chatbots Text Summarization In this article, we will discuss how an RNN can be trained … Read more

Backpropagation Algorithm

Backpropagation Algorithm

Introduction The backpropagation Algorithm is broadly used in machine learning. This algorithm is greatly used for training feed-forward neural networks. It permits the information from the cost to then flow backward through the network, acceptable to compute the gradient. Backpropagation is the core of neural network training. It is the way of adjusting the weights … Read more

Ensemble methods in Deep Learning

Ensemble methods in Deep Learning

Introduction Ensemble methods in Deep Learning associate the output of machine learning models in various stimulating means. We were unmindful of the power of ensemble methods after years of working on machine learning projects. Because this topic is typically ignored or only given a short-lived outline in utmost machine learning courses and books. By testing … Read more

LSTM meta-learner in Machine Learning

LSTM meta-learner in Machine Learning

Introduction Meta-learning is a sub-branch of machine learning. In this subfield of machine learning, automatic learning algorithms are implemented to metadata about machine learning experiments. In this article, we will learn in-depth about LSTM meta-learner. That what is the key idea to using such metadata to know? And how automatic learning may become flexible in … Read more

Parametric and Nonparametric Machine Learning Algorithm

Parametric and Nonparametric Machine Learning Algorithim

Introduction In this article, we will find out the difference between parametric and nonparametric machine learning Methods. We need to learn a function that maps the input as the set of independent variables X to the output as the target variable Y as described below. Y = f(X) + ε We require to fit a … Read more

Building Machine Learning Algorithm

Building Machine Learning Algorithm

Introduction Machine learning is driving the best of the new developments in AI. It comprises; Natural language processing, Computer vision, Predictive analytics, Autonomous systems, and A wide range of applications. Machine learning systems are essential to allowing each of the designs of AI. With the purpose of moving up the data value chain from the … Read more