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Random Pearls~Machine Learning: LSTM AI Piano, image segment

Random Pearls~Machine Learning: LSTM AI Piano, image segment

Long short-term memory is an artificial recurrent neural network architecture used in the field of deep learning. Unlike standard feedforward neural networks, LSTM has feedback connections. It can process not only single data points, but also entire sequences of data.

What you'll learn

  • Have a fundamental understanding of the Python programming language.
  • Acquire the pre-requisite Python skills to move into specific branches - Machine Learning, Data Science, etc.
  • Understand how to create your own Python programs.
  • Understand both Python 3.

Description

This lecture includes:

LSTM AI Piano

Real time Image segmentation using short code

Building a simple Machine Learning model using Pytorch from scratch.

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.

Recommendation engines are a common use case for machine learning. Other popular uses include fraud detection, spam filtering, malware threat detection, business process automation (BPA) and predictive maintenance.

Why is machine learning important?

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 significant competitive differentiator for many companies.

What are the different types of machine learning?

Classical machine learning is often categorized by how an algorithm learns to become more accurate in its predictions. There are four basic approaches:supervised learning, unsupervised learning, semi-supervised learning and reinforcement learning. The type of algorithm data scientists choose to use depends on what type of data they want to predict.

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