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Welcome to DataPro #109—Your Weekly Data Science & ML Digest! 🚀

This week’s edition is packed with exciting updates! Discover Table-Augmented Generation (TAG) for smarter querying, Vectorlite v0.2.0 for speedy SQL-powered search, Zyphra's Zamba2-mini, and Weaviate's StructuredRAG for reliable AI outputs. Plus, we’ve curated top resources to supercharge your ML models with enhanced accuracy and efficiency!

⚡ Tech Tidbits: Fresh Innovations and Tools

▪️ AWS: Speed up AI inference with NVIDIA NIM on SageMaker and integrate Amazon Q with GitHub.

▪️ Google ML: Explore multimodal search with BigQuery and get the lowdown on Imagen 3 on Vertex AI.

▪️ Microsoft Research: Dive into brain-inspired AI design for next-gen tech.

📚 Hot Reads from Packt Library

▪️ Data Science Fundamentals Pocket Primer: Your essential guide to data science concepts.

▪️ Mastering Looker and LookML: Create insightful views, dashboards, and databases.

▪️ AI and Expert Systems: Techniques and applications for solving real-world problems.

🔍 From Bits to BERT: LLMs & GPTs Spotlight

▪️ TAG: Revolutionize database querying with a unified approach.

▪️ Vectorlite v0.2.0: Get SQL-powered vector search with speed.

▪️ StructuredRAG by Weaviate: Benchmark for reliable JSON outputs in AI.

▪️ Cerebras DocChat: Fast, Llama 3-based GPT-4-level QA.

▪️ Extension|OS: Open-source tool for on-demand AI access.

▪️ AI21 Labs' Jamba 1.5: Quick, high-quality multilingual AI.

▪️ LayerPano3D: AI framework for generating 3D scenes from text.

▪️ Zyphra's Zamba2-mini: High-performance small language model.

▪️ Fairness in Graph Filtering: Framework for better AI fairness.

▪️ iAsk AI: Outperforming ChatGPT on MMLU Pro Test.

▪️ DeepSeek-AI’s Fire-Flyer AI-HPC: Cost-effective deep learning solution.

✨ On the Radar: What’s New & Noteworthy

▪️ New LLM Agents: Exploring the latest architecture.

▪️ Pandas Power: Advanced plotting techniques.

▪️ AWS DeepRacer: Bridging the Sim2Real gap.

▪️ MarianMT Translation: Easy language translation with Hugging Face Transformers.

▪️ Building Transformers: A guide to training from scratch.

▪️ ML Optimization: Top tips for boosting algorithm performance.

Enjoy your weekend and stay ahead in the world of data science!

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Did you know? “Books are the quietest, most constant friends, holding the world’s treasured wisdom. They offer gentle guidance and timeless lessons, passing their rich inheritance from one generation to the next.”

We’re thrilled to bring you this week’s must-have new releases, straight from the experts to your bookshelf! Whether you're eager to enhance your skills or explore new horizons, now is the perfect moment to add these invaluable resources to your collection.

For a limited time, enjoy 30% off all eBooks at Packtpub.com. These books are thoughtfully crafted by industry insiders with hands-on experience, offering unique insights you won’t find anywhere else.

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Data Science Fundamentals Pocket Primer: An Essential Guide to Data Science Concepts and Techniques

By Mercury Learning and Information, Oswald Campesato

Imagine having a go-to guide that gently walks you through the essentials of data science, making complex concepts feel accessible. This book does just that. With a blend of practical exercises and real-world examples, it simplifies the vast world of data science. Here’s what you’ll love:

- A clear introduction to data science fundamentals.

- Hands-on learning with practical examples.

- Mastery of tools like Python, NumPy, Pandas, and R.

- Techniques for data visualization to bring your data to life.

Whether you're just starting or looking to sharpen your skills, this book is your companion on the journey to mastering data science.Get your copy now for $41.98 (originally $59.99).

Mastering Looker and LookML - Complete Looker Guide for Developers: Master Looker and LookML to create views, dashboards, and databases with this guide [Video]

By HHN Automate Book Inc.

Embark on a journey to unlock the full potential of Looker with our all-encompassing course. Whether you’re new to Looker or looking to deepen your skills, this course guides you step-by-step through everything you need to know.

Here’s what you can expect:

- Hands-on tutorials for setting up your environment and connecting data.

- In-depth exploration of LookML fields, parameters, and joins.

- Advanced techniques for creating and managing impactful dashboards.

By the end, you’ll have the confidence to create dynamic, data-driven insights that can drive meaningful decisions in your organization.

Get the full video course now for $104.99 (MP4 download available).

Artificial Intelligence and Expert Systems: Techniques and Applications for Problem Solving

By Mercury Learning and Information ,I. Gupta ,G. Nagpal

Dive into the world of AI with a guide that makes complex concepts approachable and practical. This book is your gateway to mastering AI, offering:

- In-depth coverage of AI and expert systems.

- Clear explanations paired with real-world applications.

- Exploration of advanced topics like neural networks and fuzzy logic.

From understanding the basics of AI to applying expert systems and neural networks, this book equips you with the tools to solve real-world problems. Perfect for anyone eager to enhance their knowledge of intelligent systems.

Grab your copy now for $34.98 (originally $49.99).

➤ NicolasHug/Surprise:Python scikit for building recommender systems with explicit rating data, emphasizing experiment control, dataset handling, and diverse prediction algorithms.

➤ gorse-io/gorse:Open-source recommendation system in Go, designed for universal integration into online services, automating model training based on user interaction data.

➤ recommenders-team/recommenders:Recommenders, a Linux Foundation project, offers Jupyter notebooks for building classic and cutting-edge recommendation systems, covering data prep, modeling, evaluation, optimization, and production deployment on Azure.

➤ alibaba/Alink:Alink, developed by Alibaba's PAI team, integrates Flink for ML algorithms. PyAlink supports various Flink versions, maintaining compatibility up to Flink 1.13.

➤ RUCAIBox/RecBole:RecBole, built on Python and PyTorch, facilitates research with 91 recommendation algorithms across general, sequential, context-aware, and knowledge-based categories.

Access 100+ data tools in this specially curated blog, covering everything from data analytics to business intelligence—all in one place. Check out"Top 100+ Essential Data Science Tools & Repos: Streamline Your Workflow Today!"on PacktPub.com.

AWS ML Made Easy

➤ Accelerate Generative AI Inference with NVIDIA NIM Microservices on Amazon SageMaker: The blog details NVIDIA's new NIM Inference Microservices integration with Amazon SageMaker, enabling fast, cost-effective deployment of large language models. It covers the use of prebuilt containers for efficient AI inferencing and provides a guide for setup and evaluation.

➤ Connect the Amazon Q Business generative AI coding companion to your GitHub repositories with Amazon Q GitHub (Cloud) connector: This blog explains how incorporating generative AI, like Amazon Q Developer, can boost development productivity by up to 30% and streamline developer tasks. It details integrating Amazon Q Business with GitHub (Cloud) for natural language queries to manage repositories and enhance enterprise operations.

Mastering ML with Google

➤ Multimodel search using NLP, BigQuery and embeddings: This blog introduces a new era in search with multimodal embeddings, enabling text-based queries for images and videos. It showcases a demo for cross-modal search using Google Cloud Storage and BigQuery, allowing users to search for visual content through text queries.

➤ A developer's guide to Imagen 3 on Vertex AI: The blog highlights user feedback on Imagen 3, emphasizing its need for high-quality, versatile image generation. It discusses improvements in artistic style, prompt adherence, and safety features like watermarking. Code examples illustrate creating photorealistic images and rendering text with the model.

Microsoft Research Insights

➤ Innovations in AI: Brain-inspired design for more capable and sustainable technology. Microsoft Research Asia, in collaboration with multiple institutions, is developing brain-inspired AI models to improve efficiency and sustainability. Key projects include CircuitNet for neural patterns, enhanced spiking neural networks (SNNs) for time-series prediction, and integrating central pattern generators for better sequence processing.

➤ Table-Augmented Generation (TAG): A Unified Method for Improved Database Querying. Researchers from UC Berkeley and Stanford propose Table-Augmented Generation (TAG) to improve natural language queries over databases. TAG enhances query handling by combining query synthesis, execution, and answer generation, outperforming existing methods like Text2SQL and RAG in accuracy and complexity.

➤ Vectorlite v0.2.0: Fast, SQL-Powered Vector Search with SQLite Driver. Vectorlite v0.2.0 enhances performance by using Google’s highway library for vector distance, addressing hnswlib’s limitations on SIMD instruction support and vector normalization. The update improves speed significantly, especially on x64 platforms with AVX2, and is now SIMD-accelerated on ARM.

➤ StructuredRAG by Weaviate: Benchmark for Reliable JSON Output in AI. The StructuredRAG benchmark evaluates LLMs' ability to generate structured outputs like JSON. Testing Gemini 1.5 Pro and Llama 3 8B-instruct with various prompting strategies revealed an 82.55% success rate on average, with performance varying significantly by task and model.

➤ Cerebras DocChat: Llama 3-Based GPT-4-Level QA in Hours. Cerebras has released two models for document-based Q&A: Llama3-DocChat and Dragon-DocChat, trained quickly using Cerebras Systems. Llama3-DocChat builds on Llama 3, while Dragon-DocChat improves on Dragon+ with enhanced recall. Both models and their training data are open-source.

➤ Extension|OS: Open-Source Browser Tool for On-Demand AI Access. Extension|OS is a browser extension that integrates AI tools directly into web pages, allowing users to perform tasks like grammar checks and content edits without switching tabs. It features prompt customization, secure API key storage, and enhanced functionality with a Mixture of Agents.

➤ AI21 Labs' Jamba 1.5 Models: Speedy, Quality, Multilingual AI. AI21's Jamba 1.5 Open Model Family features the Jamba 1.5 Mini and Large models, built on the SSM-Transformer architecture. They offer the longest context window, exceptional speed, and high quality. Jamba 1.5 models outperform competitors and support extensive enterprise applications.

➤ LayerPano3D: AI Framework for Consistent 3D Scene Generation from Text. LayerPano3D introduces a novel framework for generating full-view, explorable panoramic 3D scenes from a single text prompt. By decomposing 2D panoramas into layered 3D representations, it achieves high-quality, consistent views and immersive exploration, surpassing existing methods.

➤ Zyphra's Zamba2-mini: Efficient, High-Performance Small Language Model. Zamba2-1.2B improves hybrid SSM-transformer models by adding rotary embeddings and LoRA projectors for depth-specialization, enhancing performance. Developed to optimize model efficiency and accuracy, it’s applicable in real-world scenarios like advanced NLP tasks and code generation.

➤ Fairness in Graph Filtering: Framework for Theory and Mitigation Techniques. The paper addresses fairness in GNN-based recommendation systems, which often overlook consumer fairness. It evaluates a new method for adjusting fairness via fair graph augmentation. This approach consistently improves fairness across various GNN models and datasets, advancing recommendation system equity.

➤ iAsk Ai Outperforms ChatGPT and Others on MMLU Pro Test: The iAsk Pro model achieved a record 85.85% accuracy on the MMLU-Pro benchmark, surpassing all current LLMs, including GPT-4o, by over 13 percentage points. This dataset, with 12,000 complex questions, tests multi-task language comprehension rigorously. iAsk Pro's performance highlights its advanced reasoning and understanding capabilities, setting a new standard in AI evaluation.

➤ Lite Oute 2 Mamba2Attn 250M: 10X More Efficient AI. The Lite Oute 2 Mamba2Attn 250M model, using the new Mamba2 architecture with attention layers, boasts 250 million parameters and achieves high benchmark scores. It was developed for improved efficiency and performance in various tasks, showing enhanced results in multiple evaluations compared to previous models.

➤ DeepSeek-AI Launches Fire-Flyer AI-HPC: Cost-Effective Deep Learning Solution. The Fire-Flyer AI-HPC architecture addresses high costs and energy demands in Deep Learning by integrating hardware-software design. With 10,000 PCIe A100 GPUs, it cuts costs by 50% and reduces energy use by 40%, improving scalability and performance.

➤ Navigating the New Types of LLM Agents and Architectures: The post explores the evolution of AI agents from early ReAct models to the second generation of more structured, efficient agents. It introduces tools and frameworks for building these agents and highlights advancements in design and performance. Key insights include improvements in routing and state management.

➤ The Power of Pandas Plots: Backends. The article highlights how Pandas can leverage various visualization backends, such as Matplotlib, Plotly, and Hvplot, to enhance data visualization without extensive retraining. It shows how easy it is to switch between these backends for interactive and efficient plotting, emphasizing Hvplot's ease of use and integration.

➤ AWS DeepRacer : A Practical Guide to Reducing The Sim2Real Gap. The article focuses on training the AWS DeepRacer to safely navigate a track. It emphasizes creating a "safe" model that prioritizes staying on the track over speed. Key aspects include setting up the track, designing reward functions, and using a discrete action space. It details iterative training, starting with slower models and gradually increasing speed, to enhance both safety and performance. The final reward function balances staying on the track and adjusting speed for turns, with iterative improvements for increased reliability.

➤ How to Translate Languages with MarianMT and Hugging Face Transformers? The article explains how to use MarianMT with Hugging Face Transformers for language translation. It covers installation, model selection, loading, tokenization, and translating text. The guide provides steps for translating to multiple languages and highlights MarianMT’s ease of use and effectiveness.

➤ How to Build and Train a Transformer Model from Scratch with Hugging Face Transformers? The Hugging Face Transformers library enables both the use of pre-trained models and the creation of custom transformer models from scratch. This tutorial guides you through setting up, tokenizing data, configuring, and training a transformer for sentiment classification, emphasizing the need for high-performance computing resources.

➤ 5 Tips for Optimizing Machine Learning Algorithms: This blog provides key tips for optimizing machine learning algorithms, focusing on data preparation, hyperparameter tuning, cross-validation, regularization, and ensemble methods. It aims to improve the accuracy, efficiency, and robustness of ML models for real-world applications.

See you next time!