AI and machine learning for coders
￥ 76.04 5.7折 ￥ 134 全新
库存10件
作者(美)劳伦斯·莫若尼
出版社东南大学出版社
ISBN9787564195557
出版时间2021-07
装帧平装
开本16开
定价134元
货号9787564195557
上书时间2024-04-29
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目录
Foreword
Preface
Part Ⅰ Building Models
1.Introduction to TensorFlow
What Is Machine Learning
Limitations of Traditional Programming
From Programming to Learning
What Is TensorFlow
Using TensorFlow
Installing TensorFlow in Python
Using TensorFlow in PyCharm
Using TensorFlow in Google Colab
Getting Started with Machine Learning
Seeing What the Network Learned
Summary
2.Introduction to Computer Vision
Recognizing Clothing Items
The Data:Fashion MNIST
Neurons for Vision
Designing the Neural Network
The Complete Code
Training the Neural Network
Exploring the Model Output
Training for Longer-Discovering Overfitting
Stopping Training
Summary
3.Going Beyond the Basics:Detecting Features in Images
Convolutions
Pooling
Implementing Convolutional Neural Networks
Exploring the Convolutional Network
Building a CNN to Distinguish Between Horses and Humans
The Horses or Humans Dataset
The Keras Image Data Generator
CNN Architecture for Horses or Humans
Adding Validation to the Horses or Humans Dataset
Testing Horse or Human Images
Image Augmentation
Transfer Learning
Multiclass Classification
Dropout Regularization
Summary
4.Using Public Datasets with TensorFIow Datasets
Getting Started with TFDS
Using TFDS with Keras Models
Loading Specific Versions
Using Mapping Functions for Augmentation
Using TensorFlow Addons
Using Custom Splits
Understanding TFRecord
The ETL Process for Managing Data in TensorFlow
Optimizing the Load Phase
Parallelizing ETL to Improve Training Performance
Summary
5.Introduction to Natural Language Processing
Encoding Language into Numbers
Getting Started with T0kenization
Turning Sentences into Sequences
Removing Stopwords and Cleaning Text
Working with Real Data Sources
……
Part Ⅱ Using Models
Index
内容摘要
如果你想从程序员转型为人工智能专家，这里是一个理想的起点。基于Laurence Moroney极其成功的人工智能课程，这本入门书提供了一种面向实践、代码优先的方法，帮助你在学习关键主题的同时建立信心。你所需要的只是Python的使用经验，了解其处理数据和数组的写法。你将学习如何实现机器学习中很好常见的场景，包括计算机视觉、自然语言处理（NLP）以及用于Web、移动、云端、嵌入式运行时的序列建模。大多数与机器学习相关的书开篇就是令人生畏的不错数学知识。这本指南提供了实用的课程，你可以直接同代码打交道。
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