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Allow All Cookies. Applied Optics Vol. Lee Giles and Tom Maxwell, "Learning, invariance, and generalization in high-order neural networks," Appl. Not Accessible Your account may give you access. Abstract High-order neural networks have been shown to have impressive computational, storage, and learning capabilities. More Recommended Articles. References You do not have subscription access to this journal.

Quantum generalisation of feedforward neural networks

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Models of Neural Networks III

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Generalization Through the Recurrent Interaction of Episodic Memories

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10.4: Neural Networks: Multilayer Perceptron Part 1 - The Nature of Code

Interested in this course for your Business or Team? Train your employees in the most in-demand topics, with edX for Business. Purchase now Request Information. About this course Machine learning methods are commonly used across engineering and sciences, from computer systems to physics.


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Moreover, commercial sites such as search engines, recommender systems e. As a discipline, machine learning tries to design and understand computer programs that learn from experience for the purpose of prediction or control.

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In this course, students will learn about principles and algorithms for turning training data into effective automated predictions. Students will implement and experiment with the algorithms in several Python projects designed for different practical applications.

Master the skills needed to be an informed and effective practitioner of data science. You will complete this course and three others from MITx, at a similar pace and level of rigor as an on-campus course at MIT, and then take a virtually-proctored exam to earn your MicroMasters, an academic credential that will demonstrate your proficiency in data science or accelerate your path towards an MIT PhD or a Master's at other universities.

What you'll learn Understand principles behind machine learning problems such as classification, regression, clustering, and reinforcement learning Implement and analyze models such as linear models, kernel machines, neural networks, and graphical models Choose suitable models for different applications Implement and organize machine learning projects, from training, validation, parameter tuning, to feature engineering. Lectures : Introduction Linear classifiers, separability, perceptron algorithm Maximum margin hyperplane, loss, regularization Stochastic gradient descent, over-fitting, generalization Linear regression Recommender problems, collaborative filtering Non-linear classification, kernels Learning features, Neural networks Deep learning, back propagation Recurrent neural networks Recurrent neural networks Generalization, complexity, VC-dimension Unsupervised learning: clustering Generative models, mixtures Mixtures and the EM algorithm Learning to control: Reinforcement learning Reinforcement learning continued Applications: Natural Language Processing Projects : Automatic Review Analyzer Digit Recognition with Neural Networks Reinforcement Learning.