Advanced Deep Learning with Python (Paperback)
Ivan Vasilev
- 出版商: Packt Publishing
- 出版日期: 2019-12-12
- 售價: $1,650
- 貴賓價: 9.5 折 $1,568
- 語言: 英文
- 頁數: 468
- 裝訂: Quality Paper - also called trade paper
- ISBN: 178995617X
- ISBN-13: 9781789956177
-
相關分類:
Python、程式語言、DeepLearning
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相關主題
商品描述
Key Features
- Get to grips with building faster and more robust deep learning architectures
- Investigate and train convolutional neural network (CNN) models with GPU-accelerated libraries such as TensorFlow and PyTorch
- Apply deep neural networks (DNNs) to computer vision problems, NLP, and GANs
Book Description
In order to build robust deep learning systems, you’ll need to understand everything from how neural networks work to training CNN models. In this book, you’ll discover newly developed deep learning models, methodologies used in the domain, and their implementation based on areas of application.
You’ll start by understanding the building blocks and the math behind neural networks, and then move on to CNNs and their advanced applications in computer vision. You'll also learn to apply the most popular CNN architectures in object detection and image segmentation. Further on, you’ll focus on variational autoencoders and GANs. You’ll then use neural networks to extract sophisticated vector representations of words, before going on to cover various types of recurrent networks, such as LSTM and GRU. You’ll even explore the attention mechanism to process sequential data without the help of recurrent neural networks (RNNs). Later, you’ll use graph neural networks for processing structured data, along with covering meta-learning, which allows you to train neural networks with fewer training samples. Finally, you’ll understand how to apply deep learning to autonomous vehicles.
By the end of this book, you’ll have mastered key deep learning concepts and the different applications of deep learning models in the real world.
What you will learn
- Cover advanced and state-of-the-art neural network architectures
- Understand the theory and math behind neural networks
- Train DNNs and apply them to modern deep learning problems
- Use CNNs for object detection and image segmentation
- Implement generative adversarial networks (GANs) and variational autoencoders to generate new images
- Solve natural language processing (NLP) tasks, such as machine translation, using sequence-to-sequence models
- Understand DL techniques, such as meta-learning and graph neural networks
Who this book is for
This book is for data scientists, deep learning engineers and researchers, and AI developers who want to further their knowledge of deep learning and build innovative and unique deep learning projects. Anyone looking to get to grips with advanced use cases and methodologies adopted in the deep learning domain using real-world examples will also find this book useful. Basic understanding of deep learning concepts and working knowledge of the Python programming language is assumed.
商品描述(中文翻譯)
主要特點
- 掌握構建更快速和更可靠的深度學習架構
- 使用GPU加速庫(如TensorFlow和PyTorch)研究和訓練卷積神經網絡(CNN)模型
- 將深度神經網絡(DNN)應用於計算機視覺問題、自然語言處理(NLP)和生成對抗網絡(GANs)
書籍描述
為了構建可靠的深度學習系統,您需要從神經網絡的工作原理到訓練CNN模型的所有內容都有所了解。在本書中,您將了解新開發的深度學習模型、領域中使用的方法以及基於應用領域的實現。
您將首先了解神經網絡的基本組件和數學原理,然後深入研究CNN及其在計算機視覺中的高級應用。您還將學習在目標檢測和圖像分割中應用最流行的CNN架構。此外,您還將專注於變分自編碼器和GANs。然後,您將使用神經網絡提取單詞的複雜向量表示,然後涵蓋各種類型的循環網絡,如LSTM和GRU。您甚至將探索注意機制,以處理序列數據,而無需循環神經網絡(RNNs)的幫助。隨後,您將使用圖神經網絡處理結構化數據,並涵蓋元學習,使您能夠使用更少的訓練樣本來訓練神經網絡。最後,您將了解如何將深度學習應用於自動駕駛車輛。
通過閱讀本書,您將掌握關鍵的深度學習概念以及深度學習模型在現實世界中的不同應用。
您將學到什麼
- 涵蓋先進和最新的神經網絡架構
- 了解神經網絡的理論和數學原理
- 訓練DNN並將其應用於現代深度學習問題
- 使用CNN進行目標檢測和圖像分割
- 實現生成對抗網絡(GANs)和變分自編碼器以生成新圖像
- 使用序列到序列模型解決自然語言處理(NLP)任務,如機器翻譯
- 了解元學習和圖神經網絡等深度學習技術
本書適合對象
本書適合數據科學家、深度學習工程師和研究人員以及希望進一步了解深度學習並構建創新獨特的深度學習項目的AI開發人員。任何希望通過實際示例瞭解深度學習領域中採用的高級用例和方法的人也會發現本書很有用。假設您對深度學習概念有基本的理解並具備Python編程語言的工作知識。
作者簡介
Ivan Vasilev started working on the first open source Java deep learning library with GPU support in 2013. The library was acquired by a German company, where he continued to develop it. He has also worked as a machine learning engineer and researcher in the area of medical image classification and segmentation with deep neural networks. Since 2017, he has been focusing on financial machine learning. He is working on a Python-based platform that provides the infrastructure to rapidly experiment with different machine learning algorithms for algorithmic trading. Ivan holds an MSc degree in artificial intelligence from the University of Sofia, St. Kliment Ohridski.
作者簡介(中文翻譯)
Ivan Vasilev在2013年開始參與第一個支援GPU的開源Java深度學習庫的開發工作。該庫被一家德國公司收購後,他繼續在該公司進行開發工作。他還曾在醫學影像分類和深度神經網絡分割領域擔任機器學習工程師和研究員。自2017年以來,他一直專注於金融機器學習。他正在開發一個基於Python的平台,該平台提供了基礎設施,可以快速實驗不同的機器學習算法,用於算法交易。Ivan擁有索菲亞大學聖克里門特奧赫里德斯基分校的人工智能碩士學位。
目錄大綱
- The Nuts and Bolts of Neural Networks
- Understanding Convolutional Networks
- Advanced Convolutional Networks
- Object Detection and Image Segmentation
- Generative Models
- Language Modelling
- Understanding Recurrent Networks
- Sequence-to-Sequence Models and Attention
- Emerging Neural Network Designs
- Meta Learning
- Deep Learning for Autonomous Vehicles
目錄大綱(中文翻譯)
- The Nuts and Bolts of Neural Networks
- Understanding Convolutional Networks
- Advanced Convolutional Networks
- Object Detection and Image Segmentation
- Generative Models
- Language Modelling
- Understanding Recurrent Networks
- Sequence-to-Sequence Models and Attention
- Emerging Neural Network Designs
- Meta Learning
- Deep Learning for Autonomous Vehicles