Hyperopt for Machine Learning Hyperparameter Optimization

I adapted Karpathy's microGPT to predict hourly temperatures using one year of real meteorological data from Basel. This project was built entirely in pure Python, without the use of any deep learning libraries. A full writeup is available on Medium.

Most Python workflows rely on heuristics. They’re quick, intuitive, but usually not optimal. A simple greedy approach might get you a solution, but it often leaves efficiency, performance, and cost savings on the table. GAMSPy brings algebraic modeling into Python, so you can express constraints and objectives directly and solve for a true optimum. At PyConDE & PyData 2026, Justine Broihan and Muhammet Soyturk will walk through this using a classic operations example, and then extend it into machine learning. They'll cover: 🔸 How optimization compares to rule-based heuristics and 🔸 How it can be used to test ML models (e.g. minimal changes needed to trigger misclassification) 🔸 The Art of the Optimal: A Pythonic Approach to Complex Decision-Making 📍 April 14 · 16:30 📍 Platinum (2nd Floor) If you're building decision-making systems in Python, this is worth a look. More details 👉 https://lnkd.in/dyifGdVi #PyConDE #PyData #Optimization #GAMSPy #GAMS #Python

Hyperparameter Optimization Machine Learning using MLBox #machinelearning #datascience #hyperparameteroptimization #mlbox MLBox is a powerful Automated Machine Learning python library. It provides the following features: Fast reading and distributed data preprocessing/cleaning/formatting. Highly robust feature selection and leak detection. Accurate hyper-parameter optimization in high-dimensional space. State-of-the art predictive models for classification and regression (Deep Learning, Stacking, LightGBM,…). Prediction with models interpretation. https://lnkd.in/gN4BuVTM

If Python feels inconsistent… you’re probably missing this. Day 15 — Polymorphism & Method Overloading Quick recap: Explored how the same method can behave differently based on context. Here’s what clicked: → Polymorphism isn’t theory — it’s flexibility in design One interface, multiple behaviors = cleaner, scalable code → Python doesn’t support traditional method overloading But it simulates it using default arguments & dynamic typing → Real power = writing code that adapts without rewriting logic The struggle? I kept trying to force Java-style overloading. Didn’t work. Breakthrough came when I stopped fighting Python… and started thinking in Pythonic design patterns. That shift changes everything. Showing up daily. No skips. No shortcuts. If you’re building real skills, consistency > intensity. What confused you the most about polymorphism? Or what should I break down next?

I'm learning how to build AI agents from scratch. I just finished Ask My Docs — a CLI agent that answers questions from documents using Python and the Anthropic SDK. Point it at a folder of text files and ask anything in plain English. The agent decides which files to read and returns a structured answer with sources and confidence level. Three things I learned building this: → How LLM tool calling actually works under the hood → Why forcing structured output via tool_use is more reliable than asking the model nicely for JSON → How Pydantic enforces typed responses from an LLM Stack: Python · Anthropic SDK · Pydantic GitHub: https://lnkd.in/ghwRVxgU #BuildInPublic #LearningInPublic #AIAgents #Python #Anthropic

Day 10/30 – Exploring NumPy Today I explored NumPy, the backbone of numerical computing in Python. Why NumPy? NumPy makes working with arrays fast, efficient, and way more powerful than traditional Python lists. What I learned: - Creating and manipulating arrays (ndarray) - Performing fast mathematical operations (element-wise calculations) - Understanding broadcasting to apply operations without loops - Working with multi-dimensional arrays - Using built-in functions for mean, median, standard deviation Key Takeaways: - NumPy is highly optimized → faster than lists - Reduces the need for manual loops - Forms the base for libraries like Pandas, Matplotlib, and ML frameworks From simple calculations to complex data processing, NumPy simplifies everything. A must-know library for anyone stepping into Data Science or Machine Learning #Python #NumPy #DataScience #MachineLearning #CodingJourney

Python is more than just code; it’s a powerful calculator! 🧮 Today, while diving deeper into my Data Science journey, I spent some time mastering Python's mathematical operators. It’s not just about simple math; it's about understanding how the machine processes different operations to build solid business logic. From basic addition to Floor Division and Exponentiation, understanding these basics is crucial for building accurate data models later on at Data Hub. 📊 In this snippet: Handled different types of operations. Explored how Python handles float results vs integers. Question for the experts: What’s the most common mathematical error you faced when you first started coding? 🧐 #DataHub #Python #Coding #DataAnalysis #LearningJourney #TechCommunity

🚀 Python Series – Day 16: File Handling Basics Real-world applications me data store karna important hota hai. Aaj humne seekha: 👉 How to create, read, write and manage files using Python 📌 Key Highlights: ✔ Persistent data storage ✔ Read / Write operations ✔ Clean coding with with open() 📌 Practical Use Cases: Reports generate karna Logs save karna User data store karna 💡 Practice Task: Create a text file Write sample data Read and display content 📈 Strong fundamentals = real project readiness 🔔 Follow Logic Gurukul for daily Python learning 💬 Comment "DAY16" for complete roadmap #Python #Programming #DataScience #AI #MachineLearning #Coding #LearnPython #TechSkills #CareerGrowth #LogicGurukul

🚀 Day 9 of My Python Learning Journey Today, I explored NumPy — a powerful library for numerical computing in Python 🐍 Here’s what I learned: ✔️ Creating and working with arrays ✔️ Performing fast mathematical operations ✔️ Understanding why NumPy is faster than regular Python lists I realized how efficiently large datasets can be handled using NumPy, making it a core tool for data analysis and machine learning 💡 This step brought me closer to understanding how real-world data is processed at scale. Excited to continue exploring more libraries and build practical projects 🚀 Consistency is turning into confidence! If you have tips or resources for mastering NumPy, feel free to share 🙌 #Python #NumPy #DataScience #Day9 #LearningJourney #Coding #Programming #Growth

While learning LangGraph, one small Python concept suddenly became much more important to me: TypedDict. At first, I thought it was just “type annotations for dictionaries.” Useful, sure—but nothing special. Then I started thinking about state. When multiple nodes in a workflow keep reading and updating shared data, an unstructured dict becomes chaos very quickly. - Missing keys. - Unexpected values. - Confusing debugging. TypedDict solves that by forcing structure into state. That was my takeaway: - Sometimes tools that look “optional” become essential once systems start growing. #Python #BackendDevelopment #LangGraph #AIEngineering #BuildInPublic