Components of Neural Networks | Get started with Deep Learning | Machine Learning Tutorial | Uplatz

https://training.uplatz.com/online-it-course.php?id=deep-learning-foundation-304

In this video session by Uplatz, we will learn the Components of a Neural Network that forms the foundation for Deep Learning. There are three main components: an input layer, a processing layer, and an output layer. The inputs may be weighted based on various criteria. The three components that make a machine learning model are representation, evaluation, and optimization. These three are most directly related to supervised learning, but it can be related to unsupervised learning as well.

What is Machine Learning?
Machine learning (ML) is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed to do so. Machine learning algorithms use historical data as input to predict new output values. Machine learning is a modern innovation that has enhanced many industrial and professional processes as well as our daily lives. It's a subset of artificial intelligence (AI), which focuses on using statistical techniques to build intelligent computer systems to learn from available databases. Machine learning is used in internet search engines, email filters to sort out spam, websites to make personalized recommendations, banking software to detect unusual transactions, and lots of apps on our phones such as voice recognition.

To enroll into the complete Machine Learning, please click on the following link:
https://training.uplatz.com/online-it-course.php?id=deep-learning-foundation-304

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