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🗞️Welcome toDataPro #125– Your Weekly Data Science & ML Wizardry! 🌟

We are back from the holiday break! We hope you've missed our updates as much as we've missed sharing them with you. 😊

We’ve been working on something exciting to make your learning journey even easier, and we’d love for you to help co-create it with us!

Before we dive in, take a quick moment to fill out our survey. As a thank you, we’ll give you access to a free AI Crash Course eBook!

Now, let’s jump into this week's exciting updates:

📚 New Releases You Can't Miss:

✦ Causal Inference in R

✦ Python Feature Engineering Cookbook

✦ Quantum Machine Learning and Optimisation in Finance

🔍 Fresh Insights:

✦ Wake Vision: Solving the TinyML Dataset Crisis

✦ Microsoft’s CoRAG: Raising the Bar for Data Science

✦ DeepSeek-R1: Advancing Reasoning and Affordability

✦ Meta AI Launches MR.Q: Revolutionizing Reinforcement Learning

🚀 Trendspotting:

✦ Coding with Qwen 2.5

✦ 10 Advanced Python Tricks for Data Scientists

✦ ChatGPT Gov - OpenAI

✦ Vertex AI RAG Engine

Stay on Top of the DS & ML World with Innovative Tools, Insights, and Strategies. This week, we’ve gathered trending resources to fine-tune your projects and ignite your next breakthrough. Let’s go!

🌟Help Us Make Your Learning Journey Even Better!🌟

As we mentioned earlier, we've got something exciting in the works to make your experience with Data Science, BI, and ML even easier, and we’d absolutely love forYOUto be a part of it!

Your input will help us create the perfect learning experience for you! It’ll only take a few minutes, and as a thank-you, you’ll get full access to a free ebook on theAI CrashCourse!

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Let's make learning even more amazing, together! 💡

Cheers,

Merlyn Shelley

Growth Lead, Packt.

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❯❯❯❯ Causal Inference in R: Written by Subhajit Das, this book offers a deep dive into causal inference using R, guiding readers through foundational concepts and advanced techniques like propensity score matching and instrumental variables. It helps you develop skills to construct and interpret causal models, address challenges in controlled experiments, and apply doubly robust estimation. With real-world case studies and hands-on examples, the book empowers readers to make informed, data-driven decisions by understanding and establishing causal relationships with precision. Start your free trial for access, renewing at $19.99/month.

❯❯❯❯ Python Feature Engineering Cookbook: Written by Soledad Galli, this third edition of the Python Feature Engineering Cookbook provides a complete guide to crafting powerful features for machine learning models. It covers practical solutions for common challenges, such as imputing missing values and encoding categorical variables, while optimizing data transformation processes. The book explores advanced techniques like feature extraction from dates, times, text, and time series data, as well as using tools like Featuretools and tsfresh. With step-by-step instructions and real-world examples, it helps readers build reproducible feature engineering pipelines, ultimately enhancing machine learning model performance. Start your free trial for access, renewing at $19.99/month.

❯❯❯❯ Quantum Machine Learning and Optimisation in Finance: Written by Antoine Jacquier and Oleksiy Kondratyev, this second edition of Quantum Machine Learning and Optimisation in Finance explores how quantum algorithms enhance financial modeling and decision-making. The book focuses on quantum machine learning (QML) and optimization algorithms, with an emphasis on near-term applications using NISQ systems. It offers practical insights into hybrid quantum-classical computational protocols and addresses the limitations of current quantum hardware. The authors provide an accessible yet rigorous approach to QML, covering topics like quantum neural networks, quantum annealing, and variational algorithms, equipping readers with the knowledge to apply quantum techniques in financial innovation. Start your free trial for access, renewing at $19.99/month.

❯❯❯❯ Wake Vision: Solving the TinyML Dataset Crisis. This blog introduces Wake Vision, a dataset designed to tackle the challenges of TinyML by addressing data scarcity and quality issues. With 6 million images, it offers both large and high-quality training sets, improving model accuracy and performance across real-world conditions like distance, lighting, and bias detection.

❯❯❯❯ Microsoft’s CoRAG: Raising the Bar for Accuracy and Efficiency in Data Science. This blog introduces Microsoft’s Chain-of-Retrieval Augmented Generation (CoRAG) model, a breakthrough in machine learning. CoRAG improves knowledge-intensive tasks by using multiple retrieval steps for complex queries, enhancing accuracy, efficiency, and relevance in real-world applications like customer support, healthcare, and legal analysis.

❯❯❯❯ DeepSeek-R1: Advancing Reasoning and Affordability. This blog highlights DeepSeek-R1, an affordable AI model that delivers powerful performance without high costs. It excels in multi-step reasoning tasks, offering real-world applications like running AI on smartphones, chatting with PDFs, and distributed AI across devices, all while keeping API prices low.

❯❯❯❯ Learn-by-Interact: Google Cloud’s Data-Centric Framework Redefining AI Agents. This blog introduces Google Cloud’s Learn-by-Interact, a revolutionary AI framework that enables autonomous learning through agent-environment interactions. By generating high-quality training data and adapting instructions based on agent experiences, it enhances performance and efficiency, eliminating the need for manual annotations.

❯❯❯❯ How DeepSeek-V3 is Revolutionizing AI: A Technical Report on Solving Real-World Challenges? This blog introduces DeepSeek-V3, a powerful yet cost-effective AI model designed for businesses and developers. With features like efficient load balancing, multi-token prediction, and mixed precision training, DeepSeek-V3 offers scalable solutions for coding, scientific research, customer service, and knowledge retrieval without the high costs of traditional models.

✔️ Follow us on Medium for exclusive updates and deep dives into the trends. Packt Hub – Medium

❯❯❯❯ Don’t Manage Your Python Environments, Just Use Docker Containers: This blog explains how to manage Python environments using Docker containers to avoid dependency headaches and version conflicts. It provides a step-by-step guide for setting up a Docker-based environment, including creating a Dockerfile, building an image, and managing containers. Docker’s isolation ensures clean setups for multiple projects, allowing developers to share environments with ease.

❯❯❯❯ Using DeepSeek-R1 Locally: This blog introduces DeepSeek-R1, an advanced reasoning AI model that rivals OpenAI's performance on benchmarks like MMLU and Math-500. It guides you through setting up the DeepSeek-R1 Distill version locally using Ollama, Docker, and Open WebUI. You’ll learn how to run a model with a ChatGPT-like interface, perform tasks such as code generation and logical reasoning, and access it entirely offline without relying on cloud services.

❯❯❯❯ Coding with Qwen 2.5: An Overview: This blog introduces Qwen2.5, a powerful AI model series from Alibaba, designed to compete with top-tier models. It explores various applications like text generation, sentiment analysis, coding, and mathematical reasoning. The blog guides users through using Qwen2.5 locally with PyTorch, showcasing its capabilities.

❯❯❯❯ Data Wrangling in Rust with Polars: This blog explores Polars, a fast, memory-efficient data wrangling library built in Rust, designed for handling large datasets. It covers essential features like data filtering, aggregation, sorting, joining, and lazy execution. Polars offers superior performance and low memory usage compared to Pandas, making it ideal for big data tasks.

❯❯❯❯ 10 Advanced Python Tricks for Data Scientists: This blog introduces 10 advanced Python tricks every data professional should know, from using pandas_profiling for quick dataset summaries to applying f-strings for cleaner formatting. It also covers lambda functions, NumPy broadcasting, itertools, matplotlib subplots, and more to optimize data wrangling and machine learning workflows. These tricks will help make your code cleaner, faster, and more efficient.

❯❯❯❯ Deploy DeepSeek-R1 Distilled Llama models in Amazon Bedrock: This blog explores how to deploy DeepSeek-R1 distilled models using Amazon Bedrock Custom Model Import. It highlights the DeepSeek-R1-Distill-Llama-8B and DeepSeek-R1-Distill-Llama-70B models, which offer a balance between performance and efficiency. These models, derived from the larger DeepSeek-R1 family, are more cost-effective and faster for production deployments, making them ideal for businesses using Amazon Bedrock. The article walks through importing models from Amazon S3 and deploying them in a fully managed, serverless environment, eliminating infrastructure management and ensuring scalability.

❯❯❯❯ ChatGPT Gov - OpenAI: This blog introduces ChatGPT Gov, a tailored version of ChatGPT designed to streamline U.S. government agencies' access to OpenAI’s frontier models. Hosted on Microsoft Azure, it allows agencies to manage security, privacy, and compliance requirements, enabling the handling of non-public sensitive data. ChatGPT Gov includes GPT-4, customizable GPTs, and tools for improving efficiency in government operations. It has already been adopted by over 90,000 users across 3,500 agencies, supporting tasks in areas like coding, research, and translation.

❯❯❯❯ Prompting Vision Language Models. Exploring techniques to prompt VLMs: This blog explores Vision Language Models (VLMs), which combine text and image inputs for tasks like image captioning and visual question answering. It covers zero-shot, few-shot, and chain-of-thought prompting techniques, demonstrating how VLMs can analyze and generate captions for images using GPT-4o-mini.

❯❯❯❯ Vertex AI RAG Engine: Build & deploy RAG implementations with your data. This blog introduces Vertex AI's RAG Engine, a fully managed service for building and deploying retrieval-augmented generation (RAG) applications. It offers flexibility with model selection, vector databases, and data sources, improving performance and scalability while ensuring high-quality, context-aware AI outputs for enterprise applications.

❯❯❯❯ The Invisible Revolution: How Vectors Are (Re)defining Business Success: This article discusses the importance of vector thinking in business, explaining how vectors help uncover complex relationships in data. It highlights the benefits of understanding vector-based computing for tasks like fraud detection and customer analysis, emphasizing its role in enhancing decision-making and leveraging AI.

❯❯❯❯ Build a Decision Tree in Polars from Scratch: This article explores using Polars for building a decision tree classifier from scratch. It highlights how Polars' efficient data handling, including streaming capabilities and optimized memory usage, improves decision tree training and prediction. The approach involves applying categorical mappings, target encoding, and recursive tree-building methods.

❯❯❯❯ NVIDIA AI Launches Eagle2: Setting SOTA Benchmarks in Vision-Language Models. This paper discusses Eagle2, a set of vision-language models (VLMs) developed with a focus on post-training data strategies. By building these strategies from scratch, the authors highlight the importance of data-centric approaches in enhancing model performance, with Eagle2-9B achieving state-of-the-art results in multimodal benchmarks.

❯❯❯❯ Qwen2.5-Max: Exploring the Intelligence of Large-scale MoE Model. This article introduces Qwen2.5-Max, a large-scale MoE model pretrained on 20 trillion tokens. It highlights Qwen2.5-Max's performance across various benchmarks, outperforming other models like DeepSeek V3, and discusses its availability via Alibaba Cloud API, along with future advancements in model intelligence.

❯❯❯❯ Generative AI vs. Predictive AI: This article explores the differences between Generative AI and Predictive AI, highlighting their objectives, methodologies, and applications. Generative AI focuses on creating new data, while Predictive AI aims to forecast outcomes based on historical data. The article also discusses their convergence and real-world impact.

❯❯❯❯ Beyond Open Source AI: How Bagel’s Cryptographic Architecture, Bakery Platform, and ZKLoRA Drive Sustainable AI Monetization. Bagel introduces a transformative AI architecture integrating cryptography and machine learning to foster decentralized, secure collaboration in model fine-tuning. The ZKLoRA protocol enables efficient, privacy-preserving verification of LoRA updates, ensuring scalability, intellectual property protection, and trust within decentralized AI development. Bagel’s Bakery platform monetizes contributions.

❯❯❯❯ Meta AI Launches MR.Q: Redefining Reinforcement Learning for Better Generalization. MR.Q is a model-free reinforcement learning (RL) algorithm that incorporates model-based representations for improved efficiency and generalization. It achieves strong performance across various benchmarks with minimal tuning, outperforming traditional methods while maintaining computational efficiency, making it a versatile and practical solution for RL applications.

❯❯❯❯ DeepSeek-AI Releases Janus-Pro 7B: An Open-Source multimodal AI that Beats DALL-E 3 and Stable Diffusion. This article discusses Janus-Pro, a refined multimodal AI model that improves on its predecessor by addressing inefficiencies in visual encoding and training. It highlights its advancements in understanding and generating both text and images, demonstrating superior performance in various benchmarks through architectural innovation and enhanced training strategies.

❯❯❯❯ TensorLLM: Enhancing Reasoning and Efficiency in Large Language Models through Multi-Head Attention Compression and Tensorisation. This article introduces a framework developed by Imperial College London to compress the Multi-Head Attention (MHA) block in transformer-based large language models (LLMs). By applying multi-head tensorisation and Tucker decomposition, it enhances reasoning abilities and achieves up to 250x parameter compression, improving efficiency without additional training.

❯❯❯❯ Parlant: The Open-Source Framework for Reliable AI Agents. This article introduces Parlant, an open-source AI system designed to improve chatbot performance by addressing common failures in task execution. It uses a dynamic control system with contextual evaluation, behavioral guidelines, and self-critique mechanisms, ensuring agents follow business rules, maintain coherence, and provide consistent, reliable responses.

We’ve got more great things coming your way, see you soon!