Talks
Speakers
Events
Topics
Sign in
Home
Talks
Speakers
Events
Topics
Leaderboard
Use
Analytics
Sign in
Suggest modification to this talk
Title
Description
Neural networks are an excellent way of mapping past observations to a functional model. Many researchers have been able to build tools to recognize handwriting, or even jaundice detection. While Neural Networks are powerful they still are somewhat of a mystery to many. This talk aims to explain neural networks in a test driven way. We'll write tests first and go through how to build a neural network to determine what language a sentence is. By the end of this talk you'll know how to build neural networks with tests! Help us caption & translate this video! http://amara.org/v/FG2I/
Date
Summarized using AI?
If this talk's summary was generated by AI, please check this box. A "Summarized using AI" badge will be displayed in the summary tab to indicate that the summary was generated using AI.
Show "Summarized using AI" badge on summary page
Summary
Markdown supported
The video titled "Test Driven Neural Networks with Ruby" features Matthew Kirk presenting at the MountainWest RubyConf 2014. The talk focuses on utilizing test-driven development (TDD) to implement neural networks, specifically feedforward neural networks, aimed at classifying languages based on sentence input. Kirk begins by discussing the relevance of data in modern technology, drawing parallels between tools like Gmail and Spotify that leverage data to enhance user experience. He emphasizes the richness of Ruby for data science despite it not being traditionally recognized as a big data language. The presentation introduces neural networks as supervised learning models, highlighting their structure composed of input, hidden, and output layers, akin to biological networks. He explains concepts like weighted sums and activation functions, clarifying how they relate to logic gates and fuzzy logic. Kirk mentions the importance of finding weights in neural networks and proposes the use of training algorithms, particularly backpropagation. Key points discussed include: - **The Structure of Neural Networks**: Composition includes input, hidden, and output layers with neurons functioning as weighted sums. - **Learning Process in Neural Networks**: Involves minimizing error through methods like backpropagation, visualized through a gradient descent analogy. - **Data Collection Techniques**: The presentation advocates using letter frequency analysis, taking the Bible as a data source due to its wide availability for various languages. - **Testing in TDD**: Emphasizes employing test-driven approaches to ensure proper model functionality and performance, focusing on practices to avoid underfitting and overfitting while optimizing model accuracy. - **Implementation and Example**: Demonstrates the language classification application, showcasing its instant processing capabilities by typing in words and retrieving results in real-time. Kirk encourages the audience to utilize the provided GitHub resources for further exploration and expresses his excitement about the future of machine learning within the Ruby ecosystem. He concludes by highlighting the expansive potential of neural networks and their applications in real-world data problems, urging attendees to engage more deeply in this innovative field.
Suggest modifications
Cancel
