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Training Tesseract · tesseract-ocr/tesseract Wiki
How to use the tools provided to train Tesseract 3.03–3.04 for a new language. Important note: Before you invest time and efforts on training Tesseract, it is highly recommended to read the ImproveQuality page. Tesseract 3.04 provides a script for an easy way to execute the various phases of training Tesseract. More information on using it can be found on the tesstrain.sh page. For training Tesseract 3.00–3.02 see Training Tesseract 3.00–3.02. Questions about the training process Training Procedure Appendices Questions about the training process If you had some problems during the training process and you need help, use tesseract-ocr mailing-list to ask your question(s). PLEASE DO NOT report your problems and ask questions about training as issues! Introduction Tesseract 3.0x is fully trainable. Please check the list of languages for which traineddata is already available as of release 3.04 before embarking on training. 3rd Party training tools are also available for training. Data files required
DiskStation Manager - Knowledge Base | Synology Inc.
Overview When you purchase a new Synology NAS, your existing data can be moved from the old Synology NAS to the newly acquired one. This simple process is called "migration" but needs to be performed with care, so please read the instructions below to avoid any accidental data loss due to human error. Depending on your Synology product or individual setup, there are several methods to perform migration. Contents 1. 1.1 Source and target Synology NAS Performing migration moves data and drives from one Synology NAS to another. 1.2 Always back up your data The migration procedures mentioned in this article allow you to keep most of your data. Note: Performing migration requires Synology Assistant 5.0 and DSM 5.0 or higher. 2. This section provides steps to perform migration. Once you are prepared, please see the sections below for instructions on how to perform migration. 2.1 Migrating between two identical Synology NAS models Before you start:Prepare a temporary SATA hard drive. Important: Note!
Google Cloud Platform Blog: Google Cloud Machine Learning family grows with new API, editions and pricing
Posted by Rob Craft, Group Lead for Google Cloud Machine LearningGoogle Cloud Machine Learning is one of our fastest growing products areas. Since we first announced our machine learning offerings earlier this year, we’ve released a steady stream of new APIs, tools and services to help you harness the power of machine learning. We’ve seen machine learning transform users’ experiences, accelerate business operations by solving problems that have existed for decades and delight us with novel applications. Introducing Google Cloud Jobs API Machine learning presents new opportunities to solve some pretty difficult business problems. “Large enterprises have come to expect that integrating new solutions takes month or years, and these long implementation cycles are a major roadblock in delivering innovation. Dice, a career website that serves opportunities for technology and engineering professionals, is a launch tester of the API to help job candidates browse over 80,000 tech job listings.
O câncer em uma lata: este é um dos produtos mais tóxicos e quase todos consomem sem saber!
Você certamente conhece a batatinha tipo chips. Trata-se de um produto muito apreciado, distribuído em larga escala por todo o mundo. Ela normalmente é vendida em caprichadas e vistosas embalagens de plástico, mas também é comum a embalagem em lata. Apesar de ser muito vendida e apreciada, é um alimento muito prejudicial. Entenda: na primeira fase de produção das batatas chips, os ingredientes são comuns e podem ser consumidos, como: arroz, trigo, flocos de milho e flocos de batata. Eles são misturados para formar um tipo de massa fininha. Em seguida, inicia-se o processo de moldar as batatas. Depois que elas estão na forma que as conhecemos, são levadas para assar numa temperatura altíssima. Na última etapa, antes da embalagem, é a dos temperos. Uma máquina sopra nas batatas para eliminar o excesso de gordura, depois as chips recebem sabores artificiais em pó, como bacon, queijo, cebola... O perigo está justamente no aquecimento das batatinhas industrializadas. Além disso, ela:
Reinforcement Learning for Torch: Introducing torch-twrl
Introducing torch-twrl Advances in machine learning have been driven by innovations and ideas from many fields. Inspired by the way that humans learn, Reinforcement Learning (RL) is concerned with algorithms which improve with trial-and-error feedback to optimize future performance. Board games and video games often have well-defined reward functions which allow for straightforward optimization with RL algorithms. Twitter Cortex invests in novel state-of-the-art machine learning methods to improve the quality of our products. RL algorithms (or agents) aim to learn to perform complex, novel tasks through interaction with the task (or environment). Inspired by other RL frameworks, torch-twrl aims to provide: a RL framework in Lua/Torch with minimal dependencies;rapid development with well defined, modular code; andseamless integration with OpenAI’s RL benchmark framework Gym. git clone -- recursive cd torch-twrl luarocks make Enjoy torch-twrl.
How to Retire at 40
Three early retirees tell their story of living on 4 percent or less. By Ben Steverman | September 29, 2016 From The “4 percent rule” is a bedrock of retirement planning. But does it apply to those who quit working before 65? The rule of thumb holds that retirees who spend only 4 percent of their investment portfolio annually, adjusted for inflation, will be able to stretch out their savings for the rest of their life. Lately, the 4 percent rule has been under assault, with experts warning that the future could bring weaker market returns, an increased life span, or both. Evan Inglis, an actuary at Nuveen Asset Management, offers an alternative rule: Divide your age by 20—couples should use the younger partner’s age—to get the percentage that you can safely spend. How do these concepts play out in the real world? The Stay-at-Home Dad Joe Udo retired in 2012, at 38, after spending 16 years as a computer hardware engineer at Intel and saving aggressively. “Work seems to chase me down”
baidu-research/warp-ctc: Fast parallel CTC.
