×
Dodano do koszyka:
Pozycja znajduje się w koszyku, zwiększono ilość tej pozycji:
Zakupiłeś już tę pozycję:
Książkę możesz pobrać z biblioteki w panelu użytkownika
Pozycja znajduje się w koszyku
Przejdź do koszyka

Zawartość koszyka

ODBIERZ TWÓJ BONUS :: »

Data Augmentation with Python. Enhance deep learning accuracy with data augmentation methods for image, text, audio, and tabular data Duc Haba

(ebook) (audiobook) (audiobook) Książka w języku 1
Data Augmentation with Python. Enhance deep learning accuracy with data augmentation methods for image, text, audio, and tabular data Duc Haba - okladka książki

Data Augmentation with Python. Enhance deep learning accuracy with data augmentation methods for image, text, audio, and tabular data Duc Haba - okladka książki

Data Augmentation with Python. Enhance deep learning accuracy with data augmentation methods for image, text, audio, and tabular data Duc Haba - audiobook MP3

Data Augmentation with Python. Enhance deep learning accuracy with data augmentation methods for image, text, audio, and tabular data Duc Haba - audiobook CD

Autor:
Duc Haba
Ocena:
Bądź pierwszym, który oceni tę książkę
Stron:
394
Dostępne formaty:
     PDF
     ePub
Data is paramount in AI projects, especially for deep learning and generative AI, as forecasting accuracy relies on input datasets being robust. Acquiring additional data through traditional methods can be challenging, expensive, and impractical, and data augmentation offers an economical option to extend the dataset.
The book teaches you over 20 geometric, photometric, and random erasing augmentation methods using seven real-world datasets for image classification and segmentation. You’ll also review eight image augmentation open source libraries, write object-oriented programming (OOP) wrapper functions in Python Notebooks, view color image augmentation effects, analyze safe levels and biases, as well as explore fun facts and take on fun challenges. As you advance, you’ll discover over 20 character and word techniques for text augmentation using two real-world datasets and excerpts from four classic books. The chapter on advanced text augmentation uses machine learning to extend the text dataset, such as Transformer, Word2vec, BERT, GPT-2, and others. While chapters on audio and tabular data have real-world data, open source libraries, amazing custom plots, and Python Notebook, along with fun facts and challenges.
By the end of this book, you will be proficient in image, text, audio, and tabular data augmentation techniques.

Wybrane bestsellery

O autorze książki

Mr. Duc Haba is a lifelong technologist and researcher specializing in Deep Learning and Generative AI. He has been a programmer, Enterprise Mobility Solution Architect, AI Solution Architect, Principal, VP, CTO, and CEO. The companies range from startups and IPOs to enterprise companies.
Duc’s career started with Xerox Palo Alto Research Center (PARC), researching expert systems (ruled-based) for Xerox copier diagnostics. After PARC, he joined Oracle, following Viant Consulting as a founding member. He jumped headfirst into the entrepreneurial culture in Silicon Valley. There were slightly more failures than successes, but the highlights are working with Oracle, Viant, and RRKidz. Currently, he is happy working at YML as the AI Solution Architect.

Packt Publishing - inne książki

Zamknij

Przenieś na półkę

Proszę czekać...
ajax-loader

Zamknij

Wybierz metodę płatności

Ebook
116,10 zł
Dodaj do koszyka
Zamknij Pobierz aplikację mobilną Ebookpoint
Zabrania się wykorzystania treści strony do celów eksploracji tekstu i danych (TDM), w tym eksploracji w celu szkolenia technologii AI i innych systemów uczenia maszynowego. It is forbidden to use the content of the site for text and data mining (TDM), including mining for training AI technologies and other machine learning systems.