Neural Networks Demystified [Part 1: Data and Architecture]

Name: Neural Networks Demystified [Part 1: Data and Architecture]
Uploaded: 2014-11-04T12:22:43Z
Duration: 3 min 7 s
Channel: Welch Labs
Description: The first of a short series by Welch Labs on building a complete neural network in Python. It introduces the data and the architecture of a simple network before later parts cover forward propagation, gradient descent, and backpropagation.

Welch Labs3 HN recommenders · recommended in 3 months, 2015-2018

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838K views · 12 years ago · tutorial · evergreen

The first of a short series by Welch Labs on building a complete neural network in Python. It introduces the data and the architecture of a simple network before later parts cover forward propagation, gradient descent, and backpropagation.

HN commenters point beginners to this series as one of the clearest short walkthroughs of how neural networks and backpropagation work.

Machine learning & deep learning #neuralnetworks #backpropagation #python #machinelearning #welchlabs

4 comments from Hacker News

@rayalez2018 · in “How to learn Deep Learning in 6 months”

Good post. I want to add several great resources. - Grokking Deep Learning link This is a fantastic book that assumes no prerequisites other than knowing python, and takes you through the fundamentals of DL. It has very intuitive and easy to follow explanations, and doesn't use any libraries other than NumPy, so you're building the whole thing yourself, from scratch. - Deep Learning With Python link This is kind of the opposite of the previous one, it doesn't go into math and theory, instead it guides you through building several practical projects with a very simple to use DL library(keras). It's a great way to gain practical experience in addition to theory from the previous book. Also has no prerequisites other than python, and makes it very easy to get started. - 3blue1brown videos on neural networks: link Extremely brilliant high-level concise overview of how ANNs work. I highly recommend you get started here. You should also check out his videos on calulus and linear algebra, they're fantastic way to learn the math you need. - Khan Academy videos - one of the easiest ways to learn the math prerequisites. Calculus: link Linear Algebra: link Probability and Statistics: link - Hands-On Machine Learning with Scikit-Learn and TensorFlow link I haven't read this one yet, but it looks very promising, and a lot of people seem to find it very useful. - Andrew Ng's Coursera course link Everyone knows about this one, I just think every article on AI resources shoul

CitesView on HN

@max_2016 · in “Ask HN: From programming to AI, how?”

Hello, my background is just like yours. Assuming you are a total noob I learned that AI is not like programming, where anyone with basic knowledge of computation can dive in. You can NOT just learn AI by reading other people's code. Its like Physics, there are solid principles you need to understand before you get in. The point: AI does not come with the "batteries included". If you are in deed a noob you MUST learn the prerequisites. Follow these steps in order and I guarantee you will be competent with AI in six months tops.(from my past experience) - 1. Know what AI actually is ▶ this video - 2. Write basic Machine Learning code link - 3. Understand the Mathmatics involved link - 4. Get some important concepts, link - 5. Finnish off. link

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@josso2016 · in “Google has started a video series on machine learning and I can understand it”

I also recommend Welch Labs’ Neural Network Demystified-series[0]. It is a combination of the Coursera course and the YouTube videos. It get’s into some of the math, while still keeping it basic. [0]: ▶ this video

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@film422015 · in “Beginning deep learning with 500 lines of Julia”

The gist of this is really the Backpropagation algorithm [1]. It appears as though everything else is a means to detect when to stop. One of the clearest walkthroughs I've seen online is a short series of very short videos by Stephen C Welch [2] [3]. If you're interested in this space, start with him. [1]: link [2]: ▶ this video [3]: link

Neural Networks Demystified [Part 1: Data and Architecture]

4 comments from Hacker News

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Neural Networks Demystified [Part 1: Data and Architecture]

4 comments from Hacker News

The spelled-out intro to neural networks and backpropagation: building micrograd

But what is a neural network? | Deep learning chapter 1

Gradient descent, how neural networks learn | Deep Learning Chapter 2

Backpropagation, intuitively | Deep Learning Chapter 3

Lecture 1 | Introduction to Convolutional Neural Networks for Visual Recognition

CS231n Winter 2016: Lecture1: Introduction and Historical Context

Lecture 1 | Machine Learning (Stanford)

Backpropagation calculus | Deep Learning Chapter 4

AI can't cross this line and we don't know why.

Tensorflow and deep learning - without a PhD by Martin Görner

Hello World - Machine Learning Recipes #1

WE MUST ADD STRUCTURE TO DEEP LEARNING BECAUSE...

Lecture 1 | Natural Language Processing with Deep Learning

Lecture 01 - The Learning Problem

But how do AI images and videos actually work? | Guest video by Welch Labs

Practical Deep Learning for Coders: Lesson 1

CS480/680 Lecture 19: Attention and Transformer Networks

Building the Software 2 0 Stack (Andrej Karpathy)

Building Brains to Understand the World's Data

RL Course by David Silver - Lecture 1: Introduction to Reinforcement Learning