Chapters (19)
- 0:00Course introduction
- 1:46Our first neural net!
- 4:31How they learn - Propagation
- 7:57How they learn - Structure
- 10:09How they learn - Layers
- 14:04Working with objects!
- 21:52Learning more than numbers
- 34:21Example: Counter
- 44:10Normalization
- 50:35Example: Stock price predictor
- 56:06Predicting multiple steps
- 57:43Example: A recurrent neural network that learns math
- 1:03:56Example: Number detector
- 1:09:41Example: Writing a children's book
- 1:11:28Example: Sentiment detection
- 1:13:50RNN inputs and outputs
- 1:17:56Example: Simple reinforcement learning
- 1:21:03Example: Recommendation engine
- 1:26:02Closing thoughts
Show the creator's full description
This course gives you a practical introduction to building neural networks in the browser and in Node.js using the Brain.js JavaScript library. To complete the course’s interactive challenges, simply head over to the Scrimba version: https://scrimba.com/g/gneuralnetworks
⭐️What you'll learn ⭐️
By the end of the course, you'll be able to solve a range of different problems using neural networks. The lectures does not dwell with much theory, but rather on how to code the networks. That means the course is suitable for anybody who knows JavaScript.
⭐️About Robert Plummer ⭐️
Robert is the lead developer of the Brain.js library. He has a unique ability to explain complex concepts in a manner that everyone can understand. Feel free to reach out to Robert via Twitter if you have feedback, or simply want to thank him for creating this course.
Good luck, and welcome to the exciting world of neural networks!
⭐️Course Contents ⭐️
⌨️ (0:00:00) Course introduction
⌨️ (0:01:46) Our first neural net!
⌨️ (0:04:31) How they learn - Propagation
⌨️ (0:07:57) How they learn - Structure
⌨️ (0:10:09) How they learn - Layers
⌨️ (0:14:04) Working with objects!
⌨️ (0:21:52) Learning more than numbers
⌨️ (0:34:21) Example: Counter
⌨️ (0:44:10) Normalization
⌨️ (0:50:35) Example: Stock price predictor
⌨️ (0:56:06) Predicting multiple steps
⌨️ (0:57:43) Example: A recurrent neural network that learns math
⌨️ (1:03:56) Example: Number detector
⌨️ (1:09:41) Example: Writing a children's book
⌨️ (1:11:28) Example: Sentiment detection
⌨️ (1:13:50) RNN inputs and outputs
⌨️ (1:17:56) Example: Simple reinforcement learning
⌨️ (1:21:03) Example: Recommendation engine
⌨️ (1:26:02) Closing thoughts
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