Matthew Fitzpatrick’s Post

PandasAI (YC W24) just secured $1.1M in Pre-Seed funding to revolutionize data analysis with AI. This German startup is blending the power of generative AI with the popular Python library, pandas, making data querying easier and more effective. Here’s why this is game-changing: 1. AI-Enhanced Analysis: PandasAI turns complex data questions into simple, conversational queries. 2. Open-Source Momentum: With 8,600+ stars on GitHub, it's already gaining traction among Fortune 500 companies. 3. Scalable Vision: The funding will help founder Gabriele Venturi scale up, bringing advanced data insights to decision-makers faster. Gabriele Venturi, Adam Shuaib, PhD What’s your biggest data analysis challenge right now? https://lnkd.in/dRvTe47y #AI #DataAnalysis #StartupInnovation #mondayfunding

Hey There! I'm excited to share something new—a simple comprehensive guide on Big O notation that I've put together. This guide is meant to demystify algorithm efficiency and help anyone, whether just starting out or looking to refine their skills, gain a clear understanding of Big O. So, Why Big O? Big O notation is the key to: - Understanding how algorithms handle larger data sizes📊 - Choosing the best approach for faster, more efficient code⚡ In this guide, you'll find: - Simple explanations of common time complexities, from O(1) to O(n²) - Examples like Binary Search and Merge Sort - Visual aids to make complex concepts easier to digest I’d love for you to take a look and share your thoughts! Whether you're diving into computer science or simply curious about algorithm analysis, I hope you find it helpful. 🔗GitHub Link [https://lnkd.in/d9ps-gai] #BigONotation #AlgorithmEfficiency #AI #Coding #ProgrammingTips

🚀 Empowering Innovation with Izai At IZAI, we bridge the gap between cutting-edge technology and business needs by providing highly skilled AI engineers and software developers. Our experts bring a robust suite of skills, including: 🔹 LangChain & LangGraph 🔹 PyTorch, TensorFlow, Keras 🔹 Python (FastAPI/Flask), GUnicorn 🔹 MongoDB, MySQL 🔹 Computer Vision (OpenCV, YOLO) 🔹 LLMs (vLLM, LlamaCpp, HuggingFace) 🔹 Advanced knowledge of transformer architecture What we offer: 1️⃣ Software Developers Skilled in building scalable, efficient applications tailored to your needs. 2️⃣ AI Engineers Experts in training and deploying custom AI models, empowering your company with innovative solutions. Whether you're looking to revolutionize your processes or develop new AI-driven products, our team has the tools and expertise to make it happen. 💡 Ready to bring your ideas to life with the power of AI? Let’s connect and build something extraordinary together! #AI #SoftwareDevelopment #Innovation #IZAI #MachineLearning #LLM #ComputerVision

McKinsey releases a new report that dissects how the explosive growth generative is leading to a significant capacity shortfall due to the growing demand of AI data centers, which at this rate would require tripling in capacity by 2030: An interesting analysis diving into how this surge has been driven primarily by hyperscalers hosting advanced AI workloads, and which limit computational resources for production machine learning practitioners - it is clear that the industry will need to adapt strategies for efficient resource utilization. Report: https://lnkd.in/eHHcM_U5 -- If you liked this article you can join 60,000+ practitioners for weekly tutorials, resources, OSS frameworks, and MLOps events across the machine learning ecosystem: https://lnkd.in/eRBQzVcA #ML #MachineLearning #ArtificialIntelligence #AI #MLOps #AIOps #DataOps #augmentedintelligence #deeplearning #privacy #kubernetes #datascience #python #bigdata

Join AI founder & Author of O'Reilly Book "Designing ML Systems" Chip Huyen at this upcoming ACM fireside chat discussing practical advice on "AI Engineering" - thrilled to be moderating this session where we'll be exploring the unique challenges of productionizing foundation models compared to traditional machine learning models. Despite sharing some core principles, foundation models introduce new complexities due to their open-ended nature, advanced capabilities, and computational demands. Key changes include shifting from closed-ended to open-ended evaluation, from feature engineering to context construction, and from structured data to unstructured data. This will be a great session so don't forget to RSVP! Announcement: https://lnkd.in/ehQP_5Bd RSVP: https://lnkd.in/e-niBbHF -- If you liked this article you can join 60,000+ practitioners for weekly tutorials, resources, OSS frameworks, and MLOps events across the machine learning ecosystem: https://lnkd.in/eRBQzVcA #ML #MachineLearning #ArtificialIntelligence #AI #MLOps #AIOps #DataOps #augmentedintelligence #deeplearning #privacy #kubernetes #datascience #python #bigdata

McKinsey releases a new report that dissects how the explosive growth generative is leading to a significant capacity shortfall due to the growing demand of AI data centers, which at this rate would require tripling in capacity by 2030: An interesting analysis diving into how this surge has been driven primarily by hyperscalers hosting advanced AI workloads, and which limit computational resources for production machine learning practitioners - it is clear that the industry will need to adapt strategies for efficient resource utilization. Report: https://lnkd.in/eAFgnX-z -- If you liked this article you can join 60,000+ practitioners for weekly tutorials, resources, OSS frameworks, and MLOps events across the machine learning ecosystem: https://lnkd.in/eRBQzVcA #ML #MachineLearning #ArtificialIntelligence #AI #MLOps #AIOps #DataOps #augmentedintelligence #deeplearning #privacy #kubernetes #datascience #python #bigdata

🚀 Day 23 of Coding Challenge: Learning by Fitting a Model to Data – A Classification Approach! 📊 Today, I dove deep into the world of classification models and explored a range of techniques that are essential for making predictions and understanding patterns in data. Here’s a quick overview of what I learned: 1️⃣ Multilabel Classification: Predict multiple labels for each instance (e.g., an image of a dog and a cat). 2️⃣ Support Vector Machines (SVM): Find optimal hyperplanes for binary and multiclass classification. 3️⃣ Stochastic Gradient Descent (SGD) Classifier: Efficiently trains models with large datasets by updating parameters on small random subsets. 4️⃣ Standardization & Preprocessing: Scale data for models sensitive to feature scales—essential for optimal performance! 5️⃣ Cross-Validation Techniques: Evaluate model performance and generalizability through techniques like K-Folds Cross-Validation. 6️⃣ Error Analysis & Debugging: Analyze false positives/negatives to refine model accuracy using metrics like precision, recall, and F1-score. 7️⃣ Multiclass Metrics: Go beyond accuracy—evaluate using precision, recall, and weighted averages. 8️⃣ Chain Classifiers: Use a sequence of binary classifiers to capture label dependencies in multilabel classification. 9️⃣ Multioutput Classification: Predict multiple outputs for tasks like image processing (e.g., plant species and height). 🔟 Data Manipulation & Noise Addition: Simulate real-world conditions to enhance model robustness. 1️⃣1️⃣ Image Processing & Visualization: Prepare images for training and gain insights into data distribution using libraries like Matplotlib. 1️⃣2️⃣ K-Nearest Neighbors (KNN): Classify based on the majority class of nearest neighbors—great for smaller datasets! I’ve documented all code snippets on my GitHub. Feel free to check it out and share your thoughts! [GitHub Link] (https://lnkd.in/dEznchwT) #MachineLearning #DataScience #ClassificationModels #LearningJourney #AI

Pioneering Python for data science, our partners at Anaconda, Inc. lead community-driven open-source projects that fuel global innovation. Their solutions empower organizations to leverage open source for breakthroughs in research and strategy. McKinsey's "The State of AI in 2023" survey shows niche AI adoption, with one-third of companies integrating AI in key areas. Discover how leaders utilize AI in Anaconda's "9 Enterprise AI Use Cases." Download Anaconda’s AI guide here: https://lnkd.in/exVFT8fS

#76of100daysCodingChallenge 🌟Solved "Merge Two Sorted Lists" DSA Question! 🌟 I'm excited to share that I have successfully solved the "Merge Two Sorted Lists" data structure and algorithm problem today. This exercise was a great way to enhance my understanding of linked lists and practice efficient merging techniques. As I continue my journey in mastering data structures and algorithms, this accomplishment is a motivating milestone. Looking forward to tackling more challenging problems and expanding my knowledge! #DataStructures #Algorithms #LinkedLists #Coding #ProblemSolving #LearningJourney #MCA #DataScience #AI

One of the biggest challengers to productivity in development is cognitive load, and this is a great resource that dives into key areas to reduce the mental effort needed to understand code. Extraneous cognitive load, caused by overly complex conditionals, excessive small modules or microservices, and unnecessary abstractions, can be reduced by simplifying code, favoring deep modules with simple interfaces, and using language features sparingly. Properly applying principles like Domain-Driven Design (DDD) and avoiding unnecessary complexity ensures that code remains understandable and maintainable, improving productivity and collaboration across teams. Repo: https://lnkd.in/e5m9TGQ4 -- If you liked this article you can join 60,000+ practitioners for weekly tutorials, resources, OSS frameworks, and MLOps events across the machine learning ecosystem: https://lnkd.in/eRBQzVcA #ML #MachineLearning #ArtificialIntelligence #AI #MLOps #AIOps #DataOps #augmentedintelligence #deeplearning #privacy #kubernetes #datascience #python #bigdata