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Neural Networks Demystified [Part 1: Data and Architecture]

Welch Labs

913K subscribers • 3 videos on Hallway

YouTube
  • Recommended by 3 people: 3 on Hacker News.
  • Presented by Stephen C. Welch
  • Linked in the descriptionstephencwelch/Neural-Networks-Demystified
  • May be out of date. Uploaded 11 years ago, in an area where tools change fast.

Beginner. No background in the topic needed.

838K views12 years ago3 recommendations

AI and machine learningNeural networks and deep learningMath for machine learning

Stephen C. Welch introduces the data and architecture of a simple neural network in the first part of a Python-based tutorial series.

Neural Networks Demystified Part 1: Data and Architecture @stephencwelch Supporting Code: github.com/stephencwelch/Neural-Networks-Demystified In this short series, we will build and train a complete Artificial Neural Network in python. New videos every other friday. Part 1: Data + Architecture Part 2: Forward Propagation Part 3: Gradient Descent Part 4: Backpropagation Part 5: Numerical Gradient Checking Part 6: Training Part 7: Overfitting, Testing, and Regularization

Why it's here

3 people recommend this

What people say. Highly recommended as one of the clearest, most intuitive visual walkthroughs of neural network fundamentals and backpropagation.

film42on Hacker News • Recommends

On “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: link to this video 3: link
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jossoon Hacker News • Recommends

On “Google has started a video series on machine learning and I can understand it”

I also recommend Welch Labs’ Neural Network Demystified-series0. 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: link to this video
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max_on Hacker News • Recommends

On “Ask HN: From programming to AI, how?”

…ike 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 link to 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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rayalezon Hacker News • Mentions

On “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…
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u/Stickypatrolin r/compsci • Mentions

On “(x-post from artificial) Noob question, don't up vote. How many inputs would a hand writing recognition network have and how many layers would be needed?”

…reduced image(so 64 inputs): http://ejohn.org/blog/ocr-and-neural-nets-in-javascript/ edit - there are also ways you can devise yourself that cut your image down to its bare recognizable essentials. for example categorizing shapes in a digit(I don't know if this is actually feasible, it's something i've thought about myself) another edit - this(link to this video is a video that helped me a lot in understanding the actual training and forward- backpropagation of the network. I don't know how far you are in understanding it but it might be useful. good luck :)…
2View on Reddit

u/aftersoxin r/compsci • Mentions

On “Intro into Neural Networks?”

I really liked this video series. link to this video Also Nando de Freitas has a ton of machine learning lectures up online, many of which on ANN and deep learning: link to this video
1View on Reddit