Читать онлайн книгу "Instructions for creating artificial intelligence"

Instructions for creating artificial intelligence
Александр Чичулин


В«Creating Artificial IntelligenceВ» is a comprehensive guide that outlines the step-by-step process of building effective AI systems. From defining the problem to deploying the model, this book provides practical insights and tips to help you create AI solutions that solve real-world problems.





Instructions for creating artificial intelligence



Alexander Chichulin



© Alexander Chichulin, 2023



ISBNВ 978-5-0060-0357-6

Created with Ridero smart publishing system




Step 1: Define the problem


Creating artificial intelligence is aВ complex and multidisciplinary field that requires expertise inВ computer science, mathematics, statistics, and other related fields. Here are some high-level steps you can follow toВ create artificial intelligence





– Start by defining the problem you want the artificial intelligence system to solve. This can be anything from recognizing objects in an image to predicting customer behavior


Defining the problem is the first and most critical step in creating an artificial intelligence system. It’s important to have a clear understanding of the problem you want to solve and the end goal you want to achieve. Here are some tips for defining the problem:

1. Start with a high-level goal: Identify the overarching goal that the AI system should achieve. For example, if you’re creating a chatbot, the high-level goal might be to provide customer support 24/7.

2. Break down the goal into subproblems: Once you have identified the high-level goal, break it down into smaller subproblems. This will help you toВ identify the specific tasks that the AI system should perform. For example, for the chatbot, the subproblems might include understanding user queries, providing relevant responses, and handling multiple queries simultaneously.

3. Determine the input and output: Clearly define the input and output for the AI system. This will help you toВ understand the type ofВ data you need toВ collect and how the AI system will generate output. For example, for an image recognition system, the input would be an image, and the output would be the object or objects recognized inВ the image.

4. Identify the limitations: It’s important to identify the limitations of the AI system upfront. This includes limitations in terms of data availability, computing resources, and the accuracy of the system.

5. Refine the problem: Once you have defined the problem, refine it further based on feedback from stakeholders and other experts inВ the field. This will help you toВ ensure that the problem is well-defined and achievable.

ByВ following these steps, you can define aВ clear and well-structured problem statement that will guide the development ofВ your artificial intelligence system.




Step 2: GatherВ data





– The success of an AI system depends heavily on the quality and quantity of data used to train it. Collect as much relevant data as possible for your problem


Data is the foundation ofВ any artificial intelligence system, and collecting high-quality and relevant data is critical for the success ofВ the system. Here are some steps you can follow toВ collect and prepare data for training your AI system:

1. Identify the data sources: Start byВ identifying the sources ofВ data that you can use toВ train your AI system. This might include publicly available datasets, proprietary data, or data generated byВ sensors or other devices.

2. Define the data parameters: Define the data parameters that are relevant toВ your problem. This might include data type (e.g., text, images, audio), data format (e.g., CSV, JSON, binary), and data quality (e.g., resolution, noise).

3. Collect and preprocess the data: Collect as much data as possible, but make sure that the data is relevant and ofВ high quality. Preprocess the data byВ cleaning, normalizing, and transforming it so that it can be used for training.

4. Label the data: If your AI system is aВ supervised learning system, you will need toВ label the data. Labeling involves assigning aВ category or tag toВ each data point, so that the AI system can learn toВ recognize patterns and make predictions.

5. Augment the data: InВ some cases, you may not have enough data toВ train your AI system. InВ this case, you can use data augmentation techniques toВ generate more data from existing data. This might include techniques like flipping, rotating, or scaling images.

6. Split the data: Split the data into training, validation, and test sets. The training set is used toВ train the AI system, the validation set is used toВ tune the hyperparameters ofВ the model, and the test set is used toВ evaluate the performance ofВ the model.

By following these steps, you can collect and prepare high-quality data that will help you to train your AI system effectively. It’s important to note that data collection and preparation can be a time-consuming and resource-intensive process, but it’s a critical step in the development of an effective AI system.




Конец ознакомительного фрагмента.


Текст предоставлен ООО «ЛитРес».

Прочитайте эту книгу целиком, купив полную легальную версию (https://www.litres.ru/pages/biblio_book/?art=69247051) на ЛитРес.

Безопасно оплатить книгу можно банковской картой Visa, MasterCard, Maestro, со счета мобильного телефона, с платежного терминала, в салоне МТС или Связной, через PayPal, WebMoney, Яндекс.Деньги, QIWI Кошелек, бонусными картами или другим удобным Вам способом.



Если текст книги отсутствует, перейдите по ссылке

Возможные причины отсутствия книги:
1. Книга снята с продаж по просьбе правообладателя
2. Книга ещё не поступила в продажу и пока недоступна для чтения