TensorFlow 1.x机器学习(影印版)
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作者(越)全华(Quan Hua),(巴)夏姆斯·乌尔·阿齐姆(Shams Ul Azeem),(美)赛义夫·阿哈迈德(Saif Ahmed)
出版社东南大学出版社
ISBN9787564177546
出版时间2018-08
装帧平装
开本16开
定价82元
货号1201754925
上书时间2024-09-30
商品详情
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作者简介
Quan Hua is a Computer Vision and Machine Learning Engineer at BodiData, a dataplatform for body measurements, where he focuses on developing computer vision andmachine learning applications for a handheld technology capable of acquiring a bodyavatar while a person is fully clothed.He earned a bachelor of science degree from theUniversity of Science, Vietnam, speizing in Computer Vision.He has been working inthe field of computer vision and machine learning for about 3 years at start-ups.
Shams Ul Azeem is an undergraduate in electrical engineering from NUST Islamabad,Pakistan.He has a great interest in the computer science field, and he started his journeywith Android development.Now, he's pursuing his career in Machine Learning,particularly in deep learning, by doing medical-related freelancing projects with differentcompanies.He was also a member of the RISE lab, NUST, and he has a publication credit at the IEEEInternational Conference, ROBIO as a co-author of Designing of motions for humanoidgoalkeeper robots.
Saif Ahmed is an accomplished quantitative analyst and data scientist with 15 years ofindustry experience.His career started in management consulting at Accenture and leadhim to quantitative and senior management roles at Goldman Sachs and AIG Investments.Most recently, he co-founded and runs a start-up focused on applying Deep Learning toautomating medical imaging.He obtained his bachelor's degree in computer science fromCornell University and is currently pursuing a graduate degree in data science at U.C.Berkeley.
目录
Preface
Chapter 1：Getting Started with TensorFiow
Current use
Installing TensorFIow
Ubuntu installation
macOS installation
Windows installation
Virtual machine setup
Testing the installation
Summary
Chapter 2：Your First Classifier
The key parts
Obtaining training data
Downloading training data
Understanding classes
Automating the training data setup
Additional setup
Converting images to matrices
Logical stopping points
The machine learning briefcase
Training day
Saving the model for ongoing use
Why hide the test set?
Using the classifier
Deep diving into the network
Skills learned
Summary
Chapter 3：The TensorFIow Toolbox
A quick preview
Installing TensorBoard
Incorporating hooks into our code
Handwritten digits
AlexNet
Automating runs
Summary
Chapter 4：Cats and Dogs
Revisiting notMNIST
Program configurations
Understanding convolutional networks
Revisiting configurations
Constructing the convolutional network
Fulfilment
Training day
Actual cats and dogs
Saving the model for ongoing use
Using the classifier
Skills learned
Summary
Chapter 5：Sequence to Sequence Models-Parlez-vous Fran~：ais?
A quick preview
Drinking from the firehose
Training day
Summary
Chapter 6：Finding Meaning
Additional setup
Skills learned
Summary
Chapter 7：Making Money with Machine Learning
Inputs and approaches
Getting the data
Approaching the problem
Downloading and modifying data
Viewing the data
Extracting features
Preparing for training and testing
Building the network
Training
Testing
Taking it further
Practical considerations for the individual
Skills learned
Summary
Chapter 8：The Doctor Will See You Now
The challenge
The data
The pipeline
Understanding the pipeline
Preparing the dataset
Explaining the data preparation
Training routine
Validation routine
Visualize outputs with TensorBoard
Inception network
Going further
Other medical data challenges
The ISBI grand challenge
Reading medical data
Skills Learned
Summary
Chapter 9：Cruise Control-Automation
An overview of the system
Setting up the project
Loading a pre-trained model to speed up the training
Testing the pre-trained model
Training the model for our dataset
Introduction to the Oxford-lilT Pet dataset
Dataset Statistics
Downloading the dataset
Preparing the data
Setting up input pipelines for training and testing
Defining the model
Defining training operations
Performing the training process
Exporting the model for production
Serving the model in production
Setting up TensorFIow Serving
Running and testing the model
Designing the web sewer
Testing the system
Automatic fine-tune in production
Loading the user-labeled data
Performing a fine-tune on the model
Setting up cronjob to run every day
Summary
Chapter 10：Go Live and Go Big
Quick look at Amazon Web Services
P2 instances
G2 instances
F1 instances
Pricing
Overview of the application
Datasets
Preparing the dataset and input pipeline
Pre-processing the video for training
Input pipeline with RandomShuffleQueue
Neural network architecture
Training routine with single GPU
Training routine with multiple GPU
Overview of Mechanical Turk
Summary
Chapter 11：Going Further-21 Problems
Dataset and challenges
Problem 1-ImageNet dataset
Problem 2-COCO dataset
Problem 3-Open Images dataset
Problem 4-YouTube-8M dataset
Problem 5-AudioSet dataset
Problem 6-LSUN challenge
Problem 7-MegaFace dataset
Problem 8-Data Science Bowl 2017 challenge
Problem 9-StarCraft Game dataset
TensorFIow-based Projects
Problem 10-Human Pose Estimation
Problem 11-Object Detection-YOLO
Problem 12-Object Detection-Faster RCNN
Problem 13-Person Detection-tensorbox
Problem 14-Magenta
Problem 15-Wavenet
Problem 16-Deep Speech
Interesting Projects
Problem 17-Interactive Deep Colorization-iDeepColor
Problem 18-Tiny face detector
Problem 19-People search
Problem 20-Face Recognition-MobilelD
Problem 21-Question answering-DrQA
Gaffe to TensorFlow
TensorFIow-Slim
Summary
Appendix：Advanced Installation
Installation
Installing Nvidia driver
Installing the CUDA toolkit
Installing cuDNN
Installing TensorFIow
Verifying TensorFIow with GPU support
Using TensorFIow with Anaconda
Summary
Index
内容摘要
Google的TensorFlow是机器学习世界的游戏规则改变者。《TensorFlow 1.x机器学习（影印版·英文版）》将教你如何发挥Python和TensorFlow 1.x的威力更容易地入门机器学习。首先，你将了解基础的安装过程并浏览TensorFlow 1.x的各种能力。然后是训练和运行分类器，以及介绍库中的特性，包括TensorBoard的数据流图、训练和性能可视化——全部通过一个例子展现——富含背景信息且来自多个行业的实际问题。你将进一步探索文本和图像分析，并在TensorFlow 1.x中学习CNN建模和设置。接下来，实现一个完整的真实生产系统，从训练到运行一个深度学习模型。逐步深入学习Amazon Web Services（AWS）并创建一个深度神经网络以解决视频活动识别问题。很后，把caffe模型转换到TensorFlow，并学习不错TensorFlow库：TensorFlow—Slim。学完《TensorFlow 1.x机器学习（影印版·英文版）》，你会被武装成可以应对机器学习环境中任何TensorFlow 1.x相关挑战的绝地武士。
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