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Deep Learning - Stanford University
The idea: Most perception (input processing) in the brain may be due to one learning algorithm. The idea: Build learning algorithms that mimic the brain. Most of human intelligence may be due to one learning algorithm.

Deep Learning: A Tutorial - arXiv.org
Our goal is to provide a review of deep learning methods which provide insight into structured high-dimensional data. Rather than using shallow additive architectures common to most statistical models, deep learning uses layers of semi-afine input transformations to provide a predictive rule.

Introduction to Deep Learning - Stanford University
What is Deep Learning? Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction.

Deep Learning - Study Material
By the end of the book, we hope you will be left with an intuition for how to approach problems using deep learning, the historical context for modern deep learning approaches, and a familiarity with implementing deep learning algorithms using the PyTorch open source library.

Mathematical Foundations of Deep Learning - arXiv.org
A relatively coherent, principled, and mathematically grounded account of deep learning that focuses on the underlying structures, assumptions, and theoretical guarantees of deep learning models.

DeepLearning
A good understanding of linear algebra is essential for understanding and working with many machine learning algorithms, especially deep learning algorithms. We therefore precede our introduction to deep learning with a focused presentation of the key linear algebra prerequisites.

Understanding Deep Learning
A deep neural network is a type of machine learning model, and when it is fitted to data, this is referred to as deep learning. At the time of writing, deep networks are the most powerful and practical machine learning models and are often encountered in day-to-day life.

 

 

 

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