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#General #Development #Mobile Development #Cloud & DevOps #Cybersecurity & Privacy #AI & Data Science #Hardware & Gadgets #Design & UXLessons Learned After 6.5 Years Of Machine Learning
Deep work, trends, data, and research The post Lessons Learned After 6.5 Years Of Machine Learning appeared first on Towards Data Science.
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From Pixels to Plots
How I built an AI-powered prototype to turn images into insights The post From Pixels to Plots appeared first on Towards Data Science.
Become a Better Data Scientist with These Prompt Engineering Tips and Tricks
Part 1: prompt engineering for planning, cleaning, and EDA The post Become a Better Data Scientist with These Prompt Engineering Tips and Tricks appeared first on Towards Data Science.
Your First Local LLM API Project in Python Step-By-Step
Interested in leveraging a large language model (LLM) API locally on your machine using Python and not-too-overwhelming tools frameworks? In this step-by-step article, you will set up a local API where you'll be able to send prompts to an LLM downloaded on your machine and obtain responses back.
A Beginnerβs Guide to Mastering Gemini + Google Sheets
In this article, we'll go through the implementation of Gemini with Google Sheets.
Linear Layers and Activation Functions in Transformer Models
This post is divided into three parts; they are: β’ Why Linear Layers and Activations are Needed in Transformers β’ Typical Design of the Feed-Forward Network β’ Variations of the Activation Functions The attention layer is the core function of a transformer model.
LayerNorm and RMS Norm in Transformer Models
This post is divided into five parts; they are: β’ Why Normalization is Needed in Transformers β’ LayerNorm and Its Implementation β’ Adaptive LayerNorm β’ RMS Norm and Its Implementation β’ Using PyTorch's Built-in Normalization Normalization layers improve model quality in deep learning.
A Developerβs Guide to Building Scalable AI: Workflows vs Agents
A practical guide to choosing between AI agents and workflows for production systems, covering the hidden costs, architectural trade-offs, and decision framework that can save you thousands in deployment mistakes. Includes real-world examples and a scoring system to determine which approach fits your specific use case. The post A Developerβs Guide to Building Scalable AI: Workflows vs Agents appeared first on Towards...
How to Combine Streamlit, Pandas, and Plotly for Interactive Data Apps
With just two Python files and a handful of methods, you can build a complete dashboard that rivals expensive business intelligence tools.
How to Learn AI for Data Analytics in 2025
Learn these AI tools to stay relevant as a data professional in 2025.
Pipelining AI/ML Training Workloads with CUDA Streams
PyTorch Model Performance Analysis and Optimization β Part 9 The post Pipelining AI/ML Training Workloads with CUDA Streams appeared first on Towards Data Science.
A Caching Strategy for Identifying Bottlenecks on the Data Input Pipeline
PyTorch model performance analysis and optimization β Part 8 The post A Caching Strategy for Identifying Bottlenecks on the Data Input Pipeline appeared first on Towards Data Science.
Hitchhikerβs Guide to RAG with ChatGPT API and LangChain
Build a simple Python RAG pipeline using your local files as context The post Hitchhikerβs Guide to RAG with ChatGPT API and LangChain appeared first on Towards Data Science.
Data Science: From School to Work, Part V
How to profile your Python project The post Data Science: From School to Work, Part V appeared first on Towards Data Science.
The Mythical Pivot Point from Buy to Build for Data Platforms
For companies with data-intensive architectures, there often comes a pivotal point where building in-house data platforms makes more sense than buying off-the-shelf solutions The post The Mythical Pivot Point from Buy to Build for Data Platforms appeared first on Towards Data Science.
Automate Data Quality Reports with n8n: From CSV to Professional Analysis
Analyze any CSV dataset from a URL and generate professional quality reports with n8n
How to Unlock the Power of Multi-Agent Apps
This week, we focus on helping you reap the benefits of multi-agent systems without adding unnecessary complexity. The post How to Unlock the Power of Multi-Agent Apps appeared first on Towards Data Science.
7 AI Agent Frameworks for Machine Learning Workflows in 2025
Machine learning practitioners spend countless hours on repetitive tasks: monitoring model performance, retraining pipelines, data quality checks, and experiment tracking.
A Gentle Introduction to Attention Masking in Transformer Models
This post is divided into four parts; they are: β’ Why Attention Masking is Needed β’ Implementation of Attention Masks β’ Mask Creation β’ Using PyTorch's Built-in Attention In the
Economic Cycle Synchronization with Dynamic Time Warping
The case of theEurozone The post Economic Cycle Synchronization with Dynamic Time Warping appeared first on Towards Data Science.
How to Train a Chatbot Using RAG and CustomΒ Data
Retrieval-Augmented Generation made easy withLlama The post How to Train a Chatbot Using RAG and CustomData appeared first on Towards Data Science.
Stop Chasing βEfficiency AI.β The Real Value Is in βOpportunity AI.β
Companies pursuing incremental productivity gains risk being displaced by AI-native competitors building entirely new business models The post Stop Chasing βEfficiency AI.β The Real Value Is in βOpportunity AI.β appeared first on Towards Data Science.
Vibe Coding a Speed Reading App with Python in Just 15 Minutes
Less scrolling, more focus. In this 15-minute Python project, weβll vibe-code a clean, distraction-free speed reading app.
AlphaGenome: AI for better understanding the genome
Introducing a new, unifying DNA sequence model that advances regulatory variant-effect prediction and promises to shed new light on genome function β now available via API.
10 FREE AI Tools Thatβll Save You 10+ Hours a Week
No tech skills needed. Just tools that work, free to use, and actually helpful in your daily work life.
10 Essential Machine Learning Key Terms Explained
Artificial intelligence (AI) is an umbrella computer science discipline focused on building software systems capable of mimicking human or animal intelligence capabilities to solve a task.
Why Your Next LLM Might Not Have A Tokenizer
The Tokenizer Has Been a Necessary Evil, but This Radical Approach Shows That It Might Not Be Necessary Anymore. The post Why Your Next LLM Might Not Have A Tokenizer appeared first on Towards Data Science.
Build Multi-Agent Apps with OpenAIβs Agent SDK
Creating multi-agent apps is simple with this open-source SDK, and it can be used with any OpenAI-compatible LLM The post Build Multi-Agent Apps with OpenAIβs Agent SDK appeared first on Towards Data Science.
Make Sense of a 10K+ Line GitHub Repos Without Reading the Code
No time to read huge GitHub projects? This tool builds interactive diagrams from the code β FastAPI tested.
Build a Data Cleaning & Validation Pipeline in Under 50 Lines of Python
Clean and validate messy data with a compact Python pipeline that fits into any workflow.
Gemini Robotics On-Device brings AI to local robotic devices
Weβre introducing an efficient, on-device robotics model with general-purpose dexterity and fast task adaptation.
Building AI Agents with llama.cpp
This guide will walk you through the entire process of setting up and running a llama.cpp server on your local machine, building a local AI agent, and testing it with a variety of prompts.
Combining XGBoost and Embeddings: Hybrid Semantic Boosted Trees?
The intersection of traditional machine learning and modern representation learning is opening up new possibilities.
Reinforcement Learning from HumanΒ Feedback, Explained Simply
The one technique that made ChatGPT sosmart The post Reinforcement Learning from HumanFeedback, Explained Simply appeared first on Towards Data Science.
Programming, Not Prompting: A Hands-On Guide toΒ DSPy
A practical deep dive into declarative AI programming The post Programming, Not Prompting: A Hands-On Guide toDSPy appeared first on Towards Data Science.