Introduction to Multilayer Perceptron History of Multilayer Perceptron Multi-layer Perceptron in TensorFlow Compressed lesson On Multi-Layer Perceptron Neural Networks For additional subtleties on the MLP, see the post Multi-layer perceptron work Stepwise Implementation Multi-layer Perceptron Neural Networks Examples in Business ...

Introduction to Keras Beginning with Keras Keras Principles Keras layers Keras use and Application What makes Keras exceptional? Keras client experience How Keras support the case of being multi-backend and multi-stage? Keras with Deep Learning Frameworks Highlights of Keras library Multi-input and multi-yield models Keras Backend Benefits of...

Introduction to confusion Matrix Confusion Matrix - Not so confusing! Have you been in a situation where you expected your machine learning model to perform really well, but it shows poor accuracy? You've done all the hard work - so where did the classification model go wrong? How can you fix it? There are lots of ways to measure the...

Bagging Vs Boosting: We all use decision tree techniques in daily life to make decisions. Organisations use these supervised machine learning techniques such as decision trees to make better decisions and generate more surplus and profit. Ensemble methods combine different decision trees to produce better predictive results, the latter using...

Machine learning is important because it gives enterprises a view of trends in customer behavior and business operational patterns, as well as supports the development of new products. Many of today's leading companies, such as Facebook, Google and Uber, make machine learning a central part of their operations. Machine learning has become a...

Support Vector Machine” (SVM) is a supervised machine learning algorithm that can be used for both classification or regression challenges. Support Vectors are simply the coordinates of individual observation. The SVM classifier is a frontier that best segregates the two classes (hyper-plane/ line). [contact-form-7 id="76417" title="Subscriber...

Decision Trees (DTs) are a non-parametric supervised learning method used for classification and regression. The goal is to create a model that predicts the value of a target variable by learning simple decision rules inferred from the data features. A tree can be seen as a piecewise constant approximation. [contact-form-7 id="76417"...

Pattern recognition is the use of machine learning algorithms to identify patterns. It classifies data based on statistical information or knowledge gained from patterns and their representation. In this technique, labeled training data is used to train pattern recognition systems. Introduction to Pattern Recognition Features of pattern...

Amazon Machine Learning is an Amazon Web Services product that allows a developer to discover patterns in end-user data through algorithms, construct mathematical models based on these patterns and then create and implement predictive applications. [contact-form-7 id="76417" title="Subscriber Demo Form"] ...

Keras is a neural network library while TensorFlow is the open-source library for a number of various tasks in machine learning. TensorFlow provides both high-level and low-level APIs while Keras provides only high-level APIs. TensorFlow vs Keras TensorFlow Keras TensorFlow vs Keras: The Comparison Keras vs Tensorflow -Which one to choose...