Machine Learning Using TensorFlow Cookbook: Over 60 recipes on machine learning using deep learning solutions from Kaggle Masters and Google Developer

Audevart, Alexia, Banachewicz, Konrad, Massaron, Luca

Machine Learning Using TensorFlow Cookbook: Over 60 recipes on machine learning using deep learning solutions from Kaggle Masters and Google Developer

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商品描述

Master TensorFlow to create powerful machine learning algorithms, with valuable insights on Keras, Boosted Trees, Tabular Data, Transformers, Reinforcement Learning and more

Key Features

  • Work with the latest code and examples for TensorFlow 2
  • Get to grips with the fundamentals including variables, matrices, and data sources
  • Learn advanced deep learning techniques to make your algorithms faster and more accurate

Book Description

The independent recipes in Machine Learning Using TensorFlow Cookbook will teach you how to perform complex data computations and gain valuable insights into your data. You will work through recipes on training models, model evaluation, sentiment analysis, regression analysis, artificial neural networks, and deep learning - each using Google's machine learning library, TensorFlow.

This cookbook begins by introducing you to the fundamentals of the TensorFlow library, including variables, matrices, and various data sources. You'll then take a deep dive into some real-world implementations of Keras and TensorFlow and learn how to use estimators to train linear models and boosted trees, both for classification and for regression to provide a baseline for tabular data problems.

As you progress, you'll explore the practical applications of a variety of deep learning architectures, such as recurrent neural networks and Transformers, and see how they can be applied to computer vision and natural language processing (NLP) problems. Once you are familiar with the TensorFlow ecosystem, the final chapter will teach you how to take a project to production.

By the end of this machine learning book, you will be proficient in using TensorFlow 2. You'll also understand deep learning from the fundamentals and be able to implement machine learning algorithms in real-world scenarios.

What you will learn

  • Grasp linear regression techniques with TensorFlow
  • Use Estimators to train linear models and boosted trees for classification or regression
  • Execute neural networks and improve predictions on tabular data
  • Master convolutional neural networks and recurrent neural networks through practical recipes
  • Apply reinforcement learning algorithms using the TF-Agents framework
  • Implement and fine-tune Transformer models for various NLP tasks
  • Take TensorFlow into production

Who this book is for

If you are a data scientist or a machine learning engineer, and you want to skip detailed theoretical explanations in favor of building production-ready machine learning models using TensorFlow, this book is for you.

Basic familiarity with Python, linear algebra, statistics, and machine learning is necessary to make the most out of this book.

商品描述(中文翻譯)

《使用 TensorFlow 的機器學習食譜》

掌握 TensorFlow，創建強大的機器學習演算法，並深入了解 Keras、Boosted Trees、表格數據、Transformers、強化學習等寶貴的見解

主要特點


  • 使用最新的 TensorFlow 2 代碼和示例

  • 熟悉基礎知識，包括變量、矩陣和數據源

  • 學習高級深度學習技術，使您的演算法更快、更準確

書籍描述

《使用 TensorFlow 的機器學習食譜》中的獨立食譜將教您如何執行複雜的數據計算並獲得有價值的數據見解。您將通過使用 Google 的機器學習庫 TensorFlow 來進行模型訓練、模型評估、情感分析、回歸分析、人工神經網絡和深度學習等食譜。

本食譜從介紹 TensorFlow 库的基礎知識開始，包括變量、矩陣和各種數據源。然後，您將深入研究一些基於 Keras 和 TensorFlow 的真實世界實現，並學習如何使用估算器來訓練線性模型和提升樹模型，無論是用於分類還是回歸，以提供表格數據問題的基準線。

隨著進一步的學習，您將探索各種深度學習架構的實際應用，例如循環神經網絡和 Transformers，並了解它們如何應用於計算機視覺和自然語言處理（NLP）問題。一旦您熟悉 TensorFlow 生態系統，最後一章將教您如何將項目投入生產。

通過閱讀本機器學習書籍，您將能夠熟練使用 TensorFlow 2。您還將從基礎知識中了解深度學習，並能夠在實際情境中實施機器學習演算法。

您將學到什麼


  • 掌握使用 TensorFlow 進行線性回歸技術

  • 使用估算器來訓練線性模型和提升樹模型，用於分類或回歸

  • 執行神經網絡，並改進表格數據的預測

  • 通過實用食譜掌握卷積神經網絡和循環神經網絡

  • 使用 TF-Agents 框架應用強化學習演算法

  • 實施和微調 Transformer 模型，應用於各種 NLP 任務

  • 將 TensorFlow 應用於生產環境

適合閱讀對象

如果您是一位數據科學家或機器學習工程師，並且希望跳過詳細的理論解釋，而是選擇使用 TensorFlow 構建可投入生產的機器學習模型，那麼本書適合您。

基本熟悉 Python、線性代數、統計學和機器學習對於充分利用本書是必要的。

作者簡介

Alexia Audevart, also a Google Developer Expert in machine learning, is the founder of datactik. She is a data scientist and helps her clients solve business problems by making their applications smarter. Her first book is a collaboration on artificial intelligence and neuroscience.

Konrad Banachewicz holds a PhD in statistics from Vrije Universiteit Amsterdam. He is a lead data scientist at eBay and a Kaggle Grandmaster. He worked in a variety of financial institutions on a wide array of quantitative data analysis problems. In the process, he became an expert on the entire lifetime of a data product cycle.

Luca Massaron is a Google Developer Expert in machine learning with more than a decade of experience in data science. He is also the author of several best-selling books on AI and a Kaggle master who reached number 7 for his performance in data science competitions.

作者簡介(中文翻譯)

Alexia Audevart，也是一位擁有機器學習專家證書的Google開發者專家，是datactik的創始人。她是一位數據科學家，通過使應用程序更智能來幫助客戶解決業務問題。她的第一本書是關於人工智能和神經科學的合作作品。

Konrad Banachewicz擁有阿姆斯特丹自由大學的統計學博士學位。他是eBay的首席數據科學家和Kaggle大師。他在各種金融機構工作，解決各種量化數據分析問題。在此過程中，他成為了一個關於數據產品生命周期的專家。

Luca Massaron是一位擁有十多年數據科學經驗的Google開發者專家。他還是幾本暢銷書籍的作者，內容涉及人工智能，並且是一位在數據科學競賽中表現出色的Kaggle大師，曾經排名第7。

目錄大綱

  1. Getting Started with TensorFlow 2.x
  2. The TensorFlow Way
  3. Keras
  4. Linear Regression
  5. Boosted Trees
  6. Neural Networks
  7. Predicting with Tabular Data
  8. Convolutional Neural Networks
  9. Recurrent Neural Networks
  10. Transformers
  11. Reinforcement Learning with TensorFlow and TF-Agents
  12. Taking TensorFlow to Production

目錄大綱(中文翻譯)

- Getting Started with TensorFlow 2.x
- TensorFlow 的入門指南 2.x
- The TensorFlow Way
- TensorFlow 的方法論
- Keras
- Keras
- Linear Regression
- 線性回歸
- Boosted Trees
- 提升樹
- Neural Networks
- 神經網路
- Predicting with Tabular Data
- 利用表格資料進行預測
- Convolutional Neural Networks
- 卷積神經網路
- Recurrent Neural Networks
- 循環神經網路
- Transformers
- 轉換器
- Reinforcement Learning with TensorFlow and TF-Agents
- 使用 TensorFlow 和 TF-Agents 進行強化學習
- Taking TensorFlow to Production
- 將 TensorFlow 應用於生產環境

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