Data Augmentation with Python: Enhance deep learning accuracy with data augmentation methods for image, text, audio, and tabular data (Paperback)

Boost your AI and generative AI accuracy using real-world datasets with over 150 functional object-oriented methods and open source libraries

Purchase of the print or Kindle book includes a free PDF eBook

Key Features

• Explore beautiful, customized charts and infographics in full color
• Work with fully functional OO code using open source libraries in the Python Notebook for each chapter
• Unleash the potential of real-world datasets with practical data augmentation techniques

Book Description

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.

What you will learn

• Write OOP Python code for image, text, audio, and tabular data
• Access over 150,000 real-world datasets from the Kaggle website
• Analyze biases and safe parameters for each augmentation method
• Visualize data using standard and exotic plots in color
• Discover 32 advanced open source augmentation libraries
• Explore machine learning models, such as BERT and Transformer
• Meet Pluto, an imaginary digital coding companion
• Extend your learning with fun facts and fun challenges

Who this book is for

This book is for data scientists and students interested in the AI discipline. Advanced AI or deep learning skills are not required; however, knowledge of Python programming and familiarity with Jupyter Notebooks are essential to understanding the topics covered in this book.

提升您的人工智慧和生成式人工智慧準確度，使用超過150種功能性物件導向方法和開源庫的真實世界數據集。

購買印刷版或Kindle電子書，即可免費獲得PDF電子書。

主要特點：

- 探索美麗的定制圖表和信息圖，全彩顯示。
- 使用Python Notebook中的開源庫，進行每個章節的完全功能物件導向編程。
- 通過實用的數據擴增技術，發揮真實世界數據集的潛力。

書籍描述：

數據在AI項目中至關重要，特別是對於深度學習和生成式AI來說，因為預測準確性依賴於輸入數據集的穩健性。通過傳統方法獲取額外數據可能具有挑戰性、昂貴且不切實際，而數據擴增提供了一種經濟實惠的選擇來擴展數據集。

本書使用七個真實世界數據集，教授您超過20種幾何、光度和隨機擦除的擴增方法，用於圖像分類和分割。您還將回顧八個圖像擴增的開源庫，使用Python Notebook中的物件導向編程（OOP）包裝函數，查看彩色圖像擴增效果，分析安全水平和偏差，以及探索有趣的事實和挑戰。隨著您的進步，您將發現超過20種用於文本擴增的字符和單詞技術，使用兩個真實世界數據集和四本經典書籍的摘錄。高級文本擴增章節使用機器學習來擴展文本數據集，例如Transformer、Word2vec、BERT、GPT-2等。音頻和表格數據的章節包含真實世界數據、開源庫、驚人的自定義圖表和Python Notebook，以及有趣的事實和挑戰。

通過閱讀本書，您將熟練掌握圖像、文本、音頻和表格數據擴增技術。

您將學到的內容：

- 使用OOP Python代碼處理圖像、文本、音頻和表格數據。
- 從Kaggle網站獲取超過150,000個真實世界數據集。
- 分析每種擴增方法的偏差和安全參數。
- 使用標準和特殊的彩色圖表來可視化數據。
- 探索32種高級開源擴增庫。
- 研究BERT和Transformer等機器學習模型。
- 認識Pluto，一個虛構的數字編程伴侶。
- 通過有趣的事實和挑戰擴展您的學習。

本書適合數據科學家和對AI學科感興趣的學生。不需要高級AI或深度學習技能，但需要具備Python編程知識和熟悉Jupyter Notebooks，以理解本書涵蓋的主題。

1. Data Augmentation Made Easy
2. Biases in Data Augmentation
3. Image Augmentation for Classification
4. Image Augmentation for Segmentation
5. Text Augmentati
6. Text Augmentation with Machine Learning
7. Audio Data Augmentation
8. Audio Data Augmentation with Spectrogram
9. Tabular Data Augmentation

1. 輕鬆實現資料擴增
2. 資料擴增中的偏差
3. 用於分類的影像擴增
4. 用於分割的影像擴增
5. 文本擴增
6. 利用機器學習進行文本擴增
7. 音訊資料擴增
8. 利用頻譜圖進行音訊資料擴增
9. 表格資料擴增