#47 Building a NotebookLM Clone, Time Series Clustering, Instruction Tuning, and More!

Author(s): Towards AI Editorial Team

Originally published on Towards AI.

Good morning, AI enthusiasts! As we wrap up October, we’ve compiled a bunch of diverse resources for you — from the latest developments in generative AI to tips for fine-tuning your LLM workflows, from building your own NotebookLM clone to instruction tuning. We’re also excited to share updates on Building LLMs for Production, now available on our own platform: Towards AI Academy.

Also, Happy Halloween to all those celebrating. Enjoy the read!

— Louis-François Bouchard, Towards AI Co-founder & Head of Community

🎉 Great news! Building LLMs for Production is now available as an e-book at an exclusive price on Towards AI Academy!

By making it available on our own platform, we’re not just reducing the cost — we’re making it easier than ever for you to access, learn, and grow your skills with this essential guide.

For the first time, you can access this comprehensive guide to designing, deploying, and scaling language models directly through our platform — and at a price lower than on Amazon!

The e-book covers everything from foundational concepts to advanced techniques and real-world applications, offering a structured and hands-on learning experience. If you already have the first edition, you’re eligible for an additional discount — just reach out to [email protected] to upgrade affordably!

Get Building LLMs for Production on Towards AI Academy and explore all the other tools available to support your AI journey!

We will soon launch our new Towards AI Academy course platform more broadly with a series of extremely in-depth practical LLM courses, so stay tuned!

Learn AI Together Community section!

AI poll of the week!

We have long supported RAG as one of the most practical ways to make LLMs more reliable and customizable. We would love to hear your thoughts on whether RAG is here to stay and why. Share them in the thread on Discord!

Collaboration Opportunities

The Learn AI Together Discord community is flooding with collaboration opportunities. If you are excited to dive into applied AI, want a study partner, or even want to find a partner for your passion project, join the collaboration channel! Keep an eye on this section, too — we share cool opportunities every week!

1. Golden_leaves68731 is a senior AI developer looking for a non-technical co-founder to join their venture. If this sounds like you, reach out in the thread!

2. Wildgamingyt is looking for someone to learn AI with and build projects. If you enjoy learning with a partner, connect in the thread!

3. Lazybutlearning_44405 is new to AI and seeking guidance from the community. If you can guide him, reach out in the thread!

Meme of the week!

Meme shared by ghost_in_the_machine

TAI Curated section

Article of the week

How I Developed a NotebookLM Clone? By Vatsal Saglani

This article explores the creation of PDF2Pod, a NotebookLM clone that transforms PDF documents into engaging, multi-speaker podcasts. Inspired by Google’s NotebookLM, PDF2Pod aims to produce shorter, dynamic audio discussions featuring up to five speakers, complete with overlapping dialogue for a more natural conversational flow. It details the processes of extracting text from PDFs, generating dialogue using OpenAI’s GPT-4o, converting that dialogue into audio using ElevenLabs’ text-to-speech model, and developing a user-friendly Gradio interface that allows users to upload PDFs and receive their podcast audio clips, making the transformation process intuitive and accessible.

Our must-read articles

1. A Mixture Model Approach for Clustering Time Series Data By Shenggang Li

This article explores a mixture model approach for clustering time series data, particularly focusing on financial and biological applications. It uses Gaussian Mixture Models (GMM) combined with Autoregressive (AR), Moving Averages (MA), and nonlinear trend functions to group time series with similar statistical properties. The method effectively captures both long-term trends and short-term dependencies, providing a more nuanced understanding of dynamic data compared to traditional clustering methods. Also, the article demonstrates the technique using both synthetic and real stock price data, showcasing its potential for identifying patterns and volatility differences in financial markets.

2. A Complete Guide to Embedding For NLP & Generative AI/LLM By Mdabdullahalhasib

This article provides a comprehensive guide to understanding and implementing vector embedding in NLP and generative AI. It covers the concept of embedding, its importance for machine learning algorithms, and how it is used in LangChain for various applications. It explains different embedding techniques, including Word2Vec, GloVe, BERT, and more, and details how to utilize embedding models from providers such as OpenAI, HuggingFace, and Gemini within LangChain. It also demonstrates how to store and retrieve embedded documents using vector stores and visualize embeddings for better understanding. It also explores caching embeddings using LangChain to speed up the process and make it more efficient.

3. Reconstruction of Clean Images from Noisy Data: A Bayesian Inference Perspective By Bhavesh Agone

This article provides a detailed look into using Bayesian inference to reconstruct clean images from noisy data. It outlines the fundamentals of Bayesian inference, emphasizing its suitability for handling uncertainty in image reconstruction across fields like medical imaging, satellite imagery, and astronomy. By combining prior knowledge with noisy observations, Bayesian methods enable more accurate reconstructions. It explores practical techniques, including belief propagation, Gaussian priors, and Markov Chain Monte Carlo (MCMC), to estimate clean images probabilistically.

4. Key Insights and Best Practices on Instruction Tuning By Florian June

This article provides insights and best practices for instruction tuning in large language models (LLMs). It covers key considerations like balancing data quality versus quantity, ensuring data diversity, and selecting the right tuning method. It also addresses challenges in fine-tuning, such as preserving general capabilities while improving task-specific performance. Techniques like Low-Rank Adaptation (LoRA) and self-distillation are highlighted as efficient tuning strategies, offering practical advice for developers working on specialized LLM applications.

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Published via Towards AI