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ASCII, Unicode, and UTF-8 — a Practical Guide

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  Text looks simple until you ship it. Then “é” becomes “é”, emoji break your logs, and databases refuse to sort correctly. This article gives you a solid mental model of  how text becomes bytes , why  ASCII  still matters, how  Unicode  fixes the global text problem, and why  UTF-8  is the default encoding you should reach for. 1) Characters, code points, bytes Character : the abstract “letter/symbol” humans see (e.g.,  A ,  é ,  🙂 ). Code point : a number assigned to a character. In Unicode,  A  is U+0041,  é  is U+00E9,  🙂  is U+1F642. Encoding : a method that turns code points into  bytes  (binary) and back. Computers store and transmit  bytes , not characters. Encodings are the  agreement  for mapping between the two. 2) ASCII: the OG mapping (7-bit) ASCII  defines 128 code points (0–127). It fits in 7 bits, commonly stored as a full byte (the top bit is 0). That’s ...
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Hello Minikube

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Minikube is a tool that allows you to set up a local Kubernetes cluster on your development machine. It's a valuable resource for data engineers who want to experiment, develop, and test data processing workflows in a Kubernetes environment. Here's a beginner's guide to help you get started with Minikube for data engineering: 1. Installation: Start by installing Minikube on your development machine. You can typically download the installer for your operating system from the official Minikube website or use a package manager like Homebrew (on macOS) or Chocolatey (on Windows). 2. Install Hypervisor (Optional): Depending on your platform, you might need to install a hypervisor such as VirtualBox, KVM, or Hyper-V. Minikube uses the hypervisor to create virtual machines for your Kubernetes cluster. 3. Initialize a Kubernetes Cluster: Open a terminal and run the minikube start command to create a local Kubernetes cluster. Minikube will set up a single-node cluster that you can ...
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Fine-Tune a Pretrained Model

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Hugging Face Transformers is a powerful library for natural language processing (NLP) that provides access to a wide range of pre-trained language models. Fine-tuning allows you to take these pretrained models and adapt them to your specific NLP tasks, whether it's text classification, named entity recognition, text generation, or any other NLP task. Here's how to get started: 1. Install the Transformers Library: Begin by installing the Transformers library using pip: 1 pip install transformers 2. Choose a Pretrained Model: Select a pretrained model from the Hugging Face model hub. You can choose from a variety of models, including BERT, GPT-2, RoBERTa, and many others, each fine-tuned on vast text corpora. 3. Prepare Your Data: Format your training data for your specific NLP task. Data should be organized into text sequences and corresponding labels. Depending on your task, you might need a dataset for text classification, sequence labeling, or other NLP tasks. 4. Tokenize Y...
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