I Thought Private Variables Were Actually Private (Then I Accessed Them From Outside the Class)
Author(s): Dua Asif Originally published on Towards AI. AI GENERATED class BankAccount: def __init__(self, balance): self.__balance = balance # Private variable def get_balance(self): return self.__balance def withdraw(self, amount): if amount <= self.__balance: self.__balance -= amount return True return False Two underscores. That …
Benchmarking Zero‑Shot Object Detection: A Practical Comparison of SOTA models
Author(s): Mohsin Khan Originally published on Towards AI. 1. Introduction In the first blog of this series — “Practical Guide to Zero‑Shot Object Detection: Detect Unseen Objects Without Retraining” — we explored how Zero‑Shot Object Detection (ZSOD) works and why it’s becoming …
Training the Same Neural Network with Different Optimizers
Author(s): Gradient Thoughts Originally published on Towards AI. Source: Image by Conny Schneider on Unsplash Optimizers are often discussed under a simplistic, surface level lens: adaptive methods like Adam are said to converge faster, while SGD is believed to generalize relatively better …
Master Pandas Performance with Python: 7 Lessons Every Junior Data Scientist Needs
Author(s): Mouez Yazidi Originally published on Towards AI. Hands-on examples that show you how to optimize memory and execution without leaving Pandas. If you’re new to Pandas and want a quick introduction before diving in, check out this beginner-friendly guide to get …
What Makes a Career in Data Science Future-Proof in the Age of Automation
Author(s): Shrutisharmax Originally published on Towards AI. Automation is changing how work gets done across industries. Tasks that were once handled manually are now managed by algorithms, software systems, and machines. While this shift has created uncertainty around many traditional roles, it …
Text Summarization: Comprehensive Overview with and without RAG
Author(s): Rashmi Originally published on Towards AI. Text Summarization: Comprehensive Overview with and without RAG Text summarization is the process of automatically condensing longer text documents into shorter versions while preserving the key information and main ideas. No image caption availableThis article …
LLM & AI Agent Applications with LangChain and LangGraph — Part 11: Tools
Author(s): Michalzarnecki Originally published on Towards AI. Welcome to the next part of the course dedicated to LLM-driven applications development. In this episode we’ll cover another key building block in the LangChain ecosystem: tools. Language models are incredibly powerful on their own, …
LLM & AI Agent Applications with LangChain and LangGraph — Part 10- Chains and LCEL
Author(s): Michalzarnecki Originally published on Towards AI. Designing clear data flows in LangChain Welcome back to another module related to LLM-driven applications development. In the previous parts we introduced the idea of chains — sequences of steps that connect prompts, models, parsers …
LLM & AI Agent Applications with LangChain and LangGraph — Part 9 — Conversation Memory
Author(s): Michalzarnecki Originally published on Towards AI. Welcome back to another article focused on the LLM-driven applications development. In this part of the course we’ll look at memory in LangChain — in other words, how to make sure that the assistant you’re …
LLM & AI Agent Applications with LangChain and LangGraph — Part 8 — Temperature, Top-p, Top-k and Max Tokens: How to Shape Model Behavior
Author(s): Michalzarnecki Originally published on Towards AI. Welcome back to another article focused on the LLM-driven applications development. In this part of the course I want to focus on something very practical: the main generation parameters you can control when working with …
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