Robert Baumgartner’s Post

🐍📰 In this tutorial, you'll learn all about the k-Nearest Neighbors (kNN) algorithm in Python, including how to implement kNN from scratch, kNN hyperparameter tuning, and improving kNN performance using bagging #python

👉 PYTHON FOR AI Python didn’t become the default for AI because it’s easy. It became default because it fits into the entire AI lifecycle. 👉 AI is not just about training a model. It’s about moving data, invoking models, handling outputs, and integrating systems. That’s where Python becomes critical. 👉 What makes Python critical in AI systems: • Interface layer → Interacts with models, APIs, and external services • Data layer → Handles preprocessing, transformations, and pipelines • Control layer → Manages workflows, decisions, and orchestration 👉 Most discussions stop at frameworks. But in real-world systems, Python is doing much more: • Structuring inputs before they reach the model • Managing responses after the model generates output • Connecting AI with applications, databases, and tools 👉 Key Insight: Python doesn’t just build models — it connects models to real-world systems. #Python #PythonForAI #AIEngineering #SystemDesign #LearningInPublic #GenAIJourney

Machine Learning Audio Data using python speech features #machinelearning #datascience #audiodata #pythonspeechfeatures SpeechPy is an open source Python package that contains speech preprocessing techniques, speech features, and important post-processing operations. It provides most frequent used speech features including MFCCs and filterbank energies alongside with the log-energy of filter-banks. The aim of the package is to provide researchers with a simple tool for speech feature extraction and processing purposes in applications such as Automatic Speech Recognition and Speaker Verification. Automatic Speech Recognition (ASR) requires three main components for further analysis: Preprocessing, feature extraction, and post-processing. Feature extraction, in an abstract meaning, is extracting descriptive features from raw signal for speech classification purposes. Due to the high dimensionality, the raw signal can be less informative compared to extracted higher level features. Feature extraction comes to our rescue for turning the high dimensional signal to a lower dimensional and yet more informative version of that for sound recognition and classification. Feature extraction, in essence, should be done considering the specific application at hand. For example, in ASR applications, the linguistic characteristics of the raw signal are of great importance and the other characteristics must be ignored. On the other hand, in Speaker Recognition (SR) task, solely voice-associated information must be contained in extracted feature. So the feature extraction goal is to extract the relevant feature from the raw signal and map it to a lower dimensional feature space. The problem of feature extraction has been investigated in pattern classification aimed at preventing the curse of dimensionality. There are some feature extraction approaches based on information theory applied to multimodal signals and demonstrated promising results. https://lnkd.in/gb9Ph5MX

In this article, you will learn how to build a local, privacy-first tool-calling agent using the Gemma 4 model family and Ollama. Topics we will cover include: An overview of the Gemma 4 model family and its capabilities. How tool calling enables language models to interact with external functions. How to implement a local tool calling system using Python and Ollama. https://lnkd.in/d6Wa86Gx

Building with LLMs in Python involves a lot of moving parts. You need to call model APIs, write prompts that produce reliable results, set up retrieval pipelines, and eventually build agents that can reason and use tools. We put together a learning path that walks through all of it, step by step: - Call LLM APIs from OpenAI, Ollama, and OpenRouter - Write effective prompts that return structured output - Build RAG pipelines with LlamaIndex, ChromaDB, and LangChain - Create AI agents using Pydantic AI and LangGraph - Connect agents to external tools and data via MCP It's aimed at Python developers who are comfortable with the language and want to start building real applications on top of language models. https://lnkd.in/ggdqNgNu

Users of Python Fundamentals, 2/e on O'Reilly: The new Lesson 5, Lists and Tuples, is live, and I am sending Lesson 06, Dictionaries and Sets, for processing right now. Should be live before the end of the week. https://lnkd.in/ePMpTP5t #Python Pearson Deitel & Associates

New article: Sample Paths for Uncertainty Quantification in Time Series Forecasting In this article, we explore the difference between marginal and joint distributions, and how they answer different questions when quantifying uncertainty in time series forecasting. Plus, we get a hands-on experiment with the latest release of #neuralforecast which now supports sample paths across all models. Enjoy the read! #timeseries #forecasting #deeplearning #machinelearning #python #artificialintelligence https://lnkd.in/ekrZPn8S

⭐️ An insightful article on uncertainty quantification in time series forecasting, featuring the latest #neuralforecast 🧠 release. Check it out 👇

Learn deep learning with Python and Keras, including basics, key concepts, and applications of deep learning with Python https://lnkd.in/g-4fAHDe #DeepLearningPython Read the full article https://lnkd.in/g-4fAHDe

Learn deep learning with Python and Keras, including basics, key concepts, and applications of deep learning with Python https://lnkd.in/g-4fAHDe #DeepLearningPython Read the full article https://lnkd.in/g-4fAHDe