Final LLM Course Insights: Chapter 27

Weights & Biases · Intermediate ·🔢 Mathematical Foundations ·2y ago

Key Takeaways

The final chapter of the LLM-powered applications course covers key learnings and provides guidance on building impactful LLM-powered applications using techniques such as prompt engineering, L chain, and vector databases, with tools like Weights & Biases for logging and analysis.

Full Transcript

[Music] we've reached the end of our llm powered applications course in a way this is the end but really it's just the beginning you now start building your application experimenting with techniques we discussed and ideally creating a product that improves the lives of many users what have we covered we started with an introduction and explored the inner workings of llm apis we learned about oranization llm training sampling methods such as temperature or top P sampling and prompt engineering we also used weights and biases to log results for later analysis next we built a baseline llm application and delved into app architecture using L chain and a vector database we learned about embeddings document similarity and exposed our app in in a web user interface in the final module we discussed evaluating enhancing and optimizing llm applications we covered improving document search different chain types prop engineering techniques producing structured outputs and safety considerations at weights and biases we're excited to see you build apps that make the world better we have high hopes for this community and and believe you will create fantastic applications we hope you join our community share your work on our Discord server and post a short report about your app for the best applications we'll send you some we and bys swag we hope you find Value in using our prompts product to trace analyze and debac your app good luck build amazing applications and stay in touch through our community events thank you

Original Description

🚀 Final Chapter Unveiled! Embark on the last chapter of our transformative LLM course with Darek Kleczek. Recap key learnings and advance to building impactful LLM-powered applications. 🧑🏾‍🎓 Full course with certification and class materials available free at http://wandb.me/building-llm-powered-apps 🏆 Daily swag draw and grand prize Airpods draw from Dec 1 and 31, 2023. Details at http://wandb.me/llm-apps-contest 🗣️ Join the course conversation on our Discord channel at http://wandb.me/course-discord *Episode Description* Join us for the concluding chapter of our "Building LLM-Powered Apps" course with Darek Kleczek, Machine Learning Engineer at Weights & Biases. This final chapter encapsulates the entire journey of learning to build LLM-powered applications, setting you on the path to creating impactful and innovative solutions. 🌟 Chapter Highlights -Course Recap: A thorough recapitulation of the entire course, covering the essentials of LLM APIs, tokenization, sampling methods, and prompt engineering. -From Theory to Practice: Insights on transitioning from learning to building your own LLM-powered applications. -Application Architecture Mastery: Key learnings on app architecture using Langchain and vector databases. -Evaluation and Optimization: Deep dive into the final module focusing on evaluating, enhancing, and optimizing LLM applications. -Community Engagement: Invitation to join the vibrant Weights & Biases community, share your projects, and connect with fellow developers. 🎓 Enroll for Free: Join us on this educational journey to master the art of building LLM-powered applications. Enroll at http://wandb.me/building-llm-powered-apps. 👉 Next Chapter Sneak Peek: Ready to build your own LLM-powered app? Apply the skills and techniques learned in this course and start making a difference.
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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
Intro to ML: Course Overview
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 4 2. Multi-Layer Perceptrons
2. Multi-Layer Perceptrons
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 5 3. Convolutional Neural Networks
3. Convolutional Neural Networks
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 6 Weights & Biases at OpenAI
Weights & Biases at OpenAI
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 7 Why Experiment Tracking is Crucial to OpenAI
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 8 4. Autoencoders
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)
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 24 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)
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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)
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 29 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)
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 31 Troubleshooting and Iterating ML Models with Lee Redden (2019)
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 32 Designing a Machine Learning Project with Neal Khosla (2019)
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 33 Lukas Beiwald on ML Tools and Experiment Management (2019)
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 34 Building Machine Learning Teams with Josh Tobin (2019)
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 35 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)
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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)
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 39 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)
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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
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 44 Applications of Machine Learning to COVID-19 Research with Isaac Godfried
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 45 Testing Machine Learning Models with Eric Schles
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 46 How Linear Algebra is not like Algebra with Charles Frye
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 47 Predicting Protein Structures using Deep Learning with Jonathan King
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 48 Rachael Tatman — Conversational AI and Linguistics
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 49 Reformer by Han Lee
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 50 Sequence Models with Pujaa Rajan
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 51 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
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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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This final chapter of the LLM-powered applications course recaps key learnings and provides guidance on building impactful LLM-powered applications. Students learn how to apply techniques such as prompt engineering, L chain, and vector databases to create effective applications. The course also covers evaluating, enhancing, and optimizing LLM applications, as well as safety considerations.

Key Takeaways
  1. Review key learnings from the course
  2. Apply prompt engineering techniques
  3. Design app architecture using L chain and vector databases
  4. Optimize LLM applications
  5. Evaluate and enhance LLM applications
  6. Consider safety implications
💡 To build impactful LLM-powered applications, it's essential to apply techniques such as prompt engineering, L chain, and vector databases, while also considering safety implications and optimizing for performance.

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