# Neural Networks and Deep Learning

DevFeed: [Neural Networks and Deep Learning](<https://devfeed.tech/articles/neural-networks-and-deep-learning-38604.md>)

Original publisher: [Read original article](<https://krossovochkin.com/posts/2017_11_09_neural_networks_and_deep_learning/>)

Published: 2017-11-09T00:00:00Z

Content type: tutorial

Language: en

Sources: [Vasya Drobushkov](<https://devfeed.tech/sources/vasya-drobushkov.md>)

Topics: [Deep neural networks](<https://devfeed.tech/topics/deep-neural-networks.md>), [Neural Network](<https://devfeed.tech/topics/neural-network.md>), [Deep learning](<https://devfeed.tech/topics/deep-learning.md>), [Mathematics](<https://devfeed.tech/topics/mathematics.md>), [data](<https://devfeed.tech/topics/data.md>), [NumPy](<https://devfeed.tech/topics/numpy.md>)

Tags: [arrays](<https://devfeed.tech/tags/arrays.md>), [cpu](<https://devfeed.tech/tags/cpu.md>), [deep-learning](<https://devfeed.tech/tags/deep-learning.md>), [learning](<https://devfeed.tech/tags/learning.md>), [mathematics](<https://devfeed.tech/tags/mathematics.md>), [matrix](<https://devfeed.tech/tags/matrix.md>), [neural-networks](<https://devfeed.tech/tags/neural-networks.md>), [training-data](<https://devfeed.tech/tags/training-data.md>)

## AI overview

A concise synopsis of a Coursera course on neural networks and deep learning, covering supervised learning, training data, iterative prediction and correction, and vectorization. It emphasizes that array and matrix operations can be faster than explicit loops, while noting that the computational details are not fully understood by the author.

## Source excerpt

Disclaimer It is brief synopsis of Neural Networks and Deep learning course on Coursera. That course is a first part of Deep learning specialization. Though course is great and very useful, I found that it has a lot of mathematics explanation (linear algebra -- matrices, derivatives etc.), so sometimes it was difficult to wait for the actual useful information I didn't know. So, I think I might be interested in reviewing neural networks in the future, but won't be able to review course information as there will be a lot of such noise. So, I decided to briefly put everything into one page, so I will be able to quickly review this topic in the future. There will be no code, just formulas as they were explained in a course with some notes on how I understood it.