Introduction to Artificial Neural Networks for Beginners. Understanding the concepts of Neural Networks.
ANNs (Artificial Neural Network) is at the very core of Deep Learning an advanced version of Machine Learning techniques. ANNs are versatile, adaptive, and scalable, making them appropriate to tackle large datasets and highly complex Machine Learning tasks such as image classification (e.g., Google Images), speech recognition (e.g., Apple’s Siri), video recommendation (e.g., YouTube), or analyzing sentiments among customers (e.g. Twitter Sentiment Analyzer).
ANN was first introduced in 1943 by the neurophysiologist Warren McCulloch and the mathematician Walter Pitts. However, ANN had its ups and downs. Post-1960 there was a drop in interest and excitement among researchers w.r.t neural networks with the advancement of Support Vector Machines and other powerful Machine Learning techniques that produced better accuracy and had a stronger theoretical foundation. Neural networks were complex and required tremendous computation power and time to train. However post 1990, the advancement in the field of computation (refer to Moore’s law) followed by the production of powerful GPU cards brought some interest back.
Most popular Data Science and Machine Learning courses — August 2020. This list was last updated in August 2020 — and will be updated regularly so as to keep it relevant
Artificial Intelligence, Machine Learning, and Data Science are amongst a few terms that have become extremely popular amongst professionals in almost all the fields.
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Fundamentals of Neural Network in Machine Learning. What is a Neuron? What is the Activation Function? How do Neural Network Works? How do Neural Networks Learn?
In this tutorial on "Data Science vs Machine Learning vs Artificial Intelligence," we are going to cover the whole relationship between them and how they are different from each other.