AI in electronics: Quilter’s journey in PCB design

Weights & Biases · Beginner ·🛡️ AI Safety & Ethics ·1y ago
In this episode of Gradient Dissent, Sergiy Nesterenko, CEO of Quilter, joins host Lukas Biewald to discuss the groundbreaking use of reinforcement learning in PCB design. 🎙 *Listen on Apple Podcasts* : http://wandb.me/apple-podcasts 🎙 *Listen on Spotify* : http://wandb.me/spotify Learn how Quilter automates the complex, manual process of creating PCBs, making it faster and more efficient. Nesterenko shares insights on the challenges and successes of integrating AI with real-world applications and discusses the future of electronic design. ✅ *Subscribe to Weights & Biases* → https://bit.ly/45BCkYz ⏳Timestamps: 00:00 - Introduction to Gradient Dissent 00:45 - Meet Sergiy Nesterenko, CEO of Quilter 01:27 - Understanding PCBs and Their Importance 03:06 - Challenges in PCB Design 08:12 - Automating PCB Layout with AI 10:28 - Physics and Practical Issues in PCB Design 16:07 - Real-World Applications of Quilter 24:13 - Insights on Reinforcement Learning in PCB Design 28:11 - Handling High-Speed PCB Designs 34:22 - Balancing Research and Real-World Applications 38:39 - Challenges and Surprises in Building Quilter 43:25 - Future of PCB Design and AI Integration 🎙 Get our podcasts on these platforms: Apple Podcasts: http://wandb.me/apple-podcasts Spotify: http://wandb.me/spotify Google: http://wandb.me/gd_google YouTube: http://wandb.me/youtube Connect with Sergiy Nesterenko: https://www.linkedin.com/in/sergiynesterenko/ Follow Weights & Biases: https://twitter.com/weights_biases https://www.linkedin.com/company/wandb Join the Weights & Biases Discord Server: https://discord.gg/CkZKRNnaf3 Andrew Ng course (37:20): https://www.coursera.org/specializations/machine-learning-introduction
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1 0. What is machine learning?
0. What is machine learning?
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2 1. Build Your First Machine Learning Model
1. Build Your First Machine Learning Model
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3 Intro to ML: Course Overview
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4 2. Multi-Layer Perceptrons
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5 3. Convolutional Neural Networks
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6 Weights & Biases at OpenAI
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7 Why Experiment Tracking is Crucial to OpenAI
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8 4. Autoencoders
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9 5. Sentiment Analysis
5. Sentiment Analysis
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10 6. Recurrent Neural Networks [RNNs]
6. Recurrent Neural Networks [RNNs]
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11 7. Text Generation using LSTMs and GRUs
7. Text Generation using LSTMs and GRUs
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12 8. Text Classification Using Convolutional Neural Networks
8. Text Classification Using Convolutional Neural Networks
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13 9. Hybrid LSTMs [Long Short-Term Memory]
9. Hybrid LSTMs [Long Short-Term Memory]
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14 Toyota Research Institute on Experiment Tracking with Weights & Biases
Toyota Research Institute on Experiment Tracking with Weights & Biases
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15 Weights and Biases - Developer Tools for Deep Learning
Weights and Biases - Developer Tools for Deep Learning
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16 Introducing Weights & Biases
Introducing Weights & Biases
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17 10. Seq2Seq Models
10. Seq2Seq Models
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18 11. Transfer Learning for Domain-Specific Image Classification with Small Datasets
11. Transfer Learning for Domain-Specific Image Classification with Small Datasets
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19 12. One-shot learning for teaching neural networks to classify objects never seen before
12. One-shot learning for teaching neural networks to classify objects never seen before
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20 13. Speech Recognition with Convolutional Neural Networks in Keras/TensorFlow
13. Speech Recognition with Convolutional Neural Networks in Keras/TensorFlow
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21 14. Data Augmentation | Keras
14. Data Augmentation | Keras
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22 15. Batch Size and Learning Rate in CNNs
15. Batch Size and Learning Rate in CNNs
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23 Applied Deep Learning Fellowship Overview and Project Selection with Josh Tobin (2019)
Applied Deep Learning Fellowship Overview and Project Selection with Josh Tobin (2019)
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24 Grading Rubric for AI Applications with Sergey Karayev  (2019)
Grading Rubric for AI Applications with Sergey Karayev (2019)
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25 16. Video Frame Prediction using CNNs and LSTMs (2019)
16. Video Frame Prediction using CNNs and LSTMs (2019)
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26 Image to LaTeX - Applied Deep Learning Fellowship (2019)
Image to LaTeX - Applied Deep Learning Fellowship (2019)
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27 17.  Build and Deploy an Emotion Classifier (2019)
17. Build and Deploy an Emotion Classifier (2019)
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28 Applied Deep Learning - Data Management with Josh Tobin (2019)
Applied Deep Learning - Data Management with Josh Tobin (2019)
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29 Snorkel: Programming Training Data with Paroma Varma of Stanford University (2019)
Snorkel: Programming Training Data with Paroma Varma of Stanford University (2019)
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30 Applied Deep Learning - Troubleshooting and Debugging with Josh Tobin (2019)
Applied Deep Learning - Troubleshooting and Debugging with Josh Tobin (2019)
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31 Troubleshooting and Iterating ML Models with Lee Redden (2019)
Troubleshooting and Iterating ML Models with Lee Redden (2019)
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32 Designing a Machine Learning Project with Neal Khosla (2019)
Designing a Machine Learning Project with Neal Khosla (2019)
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33 Lukas Beiwald on ML Tools and Experiment Management (2019)
Lukas Beiwald on ML Tools and Experiment Management (2019)
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34 Building Machine Learning Teams with Josh Tobin (2019)
Building Machine Learning Teams with Josh Tobin (2019)
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35 Pieter Abeel on Potential Deep Learning Research Directions  (2019)
Pieter Abeel on Potential Deep Learning Research Directions (2019)
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36 Testing and Deployment of Deep Learning Models with Josh Tobin (2019)
Testing and Deployment of Deep Learning Models with Josh Tobin (2019)
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37 Five Lessons for Team-Oriented Research with Peter Welder (2019)
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38 Applied Deep Learning - Rosanne Liu on AI Research (2019)
Applied Deep Learning - Rosanne Liu on AI Research (2019)
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39 Making the Mid-career Leap from Urban Design to Deep Learning/Data Science
Making the Mid-career Leap from Urban Design to Deep Learning/Data Science
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40 Organizing ML projects — W&B walkthrough (2020)
Organizing ML projects — W&B walkthrough (2020)
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41 Brandon Rohrer — Machine Learning in Production for Robots
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42 Nicolas Koumchatzky — Machine Learning in Production for Self-Driving Cars
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43 My experiments with Reinforcement Learning with Jariullah Safi
My experiments with Reinforcement Learning with Jariullah Safi
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44 Applications of Machine Learning to COVID-19 Research with Isaac Godfried
Applications of Machine Learning to COVID-19 Research with Isaac Godfried
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45 Testing Machine Learning Models with Eric Schles
Testing Machine Learning Models with Eric Schles
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46 How Linear Algebra is not like Algebra with Charles Frye
How Linear Algebra is not like Algebra with Charles Frye
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47 Predicting Protein Structures using Deep Learning with Jonathan King
Predicting Protein Structures using Deep Learning with Jonathan King
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48 Rachael Tatman — Conversational AI and Linguistics
Rachael Tatman — Conversational AI and Linguistics
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49 Reformer by Han Lee
Reformer by Han Lee
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50 Sequence Models with Pujaa Rajan
Sequence Models with Pujaa Rajan
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51 GitHub Actions & Machine Learning Workflows with Hamel Husain
GitHub Actions & Machine Learning Workflows with Hamel Husain
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52 Look Mom, No Indices! Vector Calculus with the Fréchet Derivative by Charles Frye
Look Mom, No Indices! Vector Calculus with the Fréchet Derivative by Charles Frye
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53 Jack Clark — Building Trustworthy AI Systems
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54 Surprising Utility of Surprise: Why ML Uses Negative Log Probabilities - Charles Frye
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55 Track your machine learning experiments locally, with W&B Local - Chris Van Pelt
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56 Antipatterns in open source research code with Jariullah Safi
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57 Attention for time series forecasting & COVID predictions - Isaac Godfried
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58 Made with ML - Goku Mohandas
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59 Angela & Danielle — Designing ML Models for Millions of Consumer Robots
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60 Deep Learning Salon by Weights & Biases
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Chapters (12)

Introduction to Gradient Dissent
0:45 Meet Sergiy Nesterenko, CEO of Quilter
1:27 Understanding PCBs and Their Importance
3:06 Challenges in PCB Design
8:12 Automating PCB Layout with AI
10:28 Physics and Practical Issues in PCB Design
16:07 Real-World Applications of Quilter
24:13 Insights on Reinforcement Learning in PCB Design
28:11 Handling High-Speed PCB Designs
34:22 Balancing Research and Real-World Applications
38:39 Challenges and Surprises in Building Quilter
43:25 Future of PCB Design and AI Integration
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