Practical Convolutional Neural Network Models: Enhance deep learning skills by building intelligent ConvNet models using Keras

Mohit Sewak, Md. Rezaul Karim, Pradeep Pujari

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商品描述

One stop guide to practice ConvNets models from most common to recent advances in artificial intelligence field

Key Features

  • Fast-paced guide with use cases and real-world examples to get well versed with CNN techniques
  • Learn best practices to get the most out of the book to implement CNN models on image recognition, object classification, transfer learning, GAN and more
  • Develop your skills by in-depth understanding of advanced CNN architectures such as AlexNet, VGG, GoogLeNet and more and apply them to real-world research field

Book Description

Convolutional Neural Network (CNN) is revolutionizing several application domains such as visual recognition systems, self-driving cars, speech recognition and more. These advances create unprecedented opportunities and challenges to build and deploy large-scale ConvNet applications. This book aims to take you through the building blocks of CNN's, best practices for implementing CNN models and how they can be applied to solve complex machine learning problems.

This book starts with an overview of deep neural networks with the example of image classification and walks you through building your first CNN for human face detector. As you progress further you'll come across practical illustrations of CNN internals with interesting examples to understand different optimization and visualization techniques to build a robust model. While explaining CNN architecture, this book covers case studies of most common and award-winning CNN architectures.

Furthermore, this book examines how knowledge transfer can be achieved to train a CNN model that does need a lot of data. You will be introduced to the concept of transfer learning that helps to improve the performance of a CNN model besides training data needs. Towards the end, this book also touches upon the subject on the attention-based CNN with an example of visual question answering application. Finally, this book covers details of generative models and a novel application - getting started with generating your own hand-written MNIST digits.

By the end of this book, you will be all ready to implement CNN models in your work or projects by working with extreme datasets.

What you will learn

  • From CNN basics building blocks to advanced concepts understand the practical areas they can be applied to.
  • Build a simple image classifier CNN model to understand how different components interact with each other.
  • Learn CNN Model Optimization and Visualization techniques
  • Implement award-winning CNN Architectures like AlexNet, VGG, GoogLeNet, ResNet etc
  • Practical approach to use pre-trained models and examples to understand transfer learning methodology.
  • Understand the difference between GAN generators and discriminators.
  • Step into the world of Artificial intelligence with adversarial training and applications of GANs

Who This Book Is For

This book is for data scientists, machine learning practitioners, deep learning and AI enthusiasts who want to move one step further in building convolutional neural network models. Get your hands on extreme datasets and different CNN architectures to build efficient and smart ConvNet models. Basic knowledge of machine learning and CNN is expected.

商品描述(中文翻譯)

《一站式指南:從常見到最新的人工智慧領域中實踐 ConvNets 模型》

主要特點


  • 快節奏的指南,通過使用案例和實際示例,熟悉 CNN 技術

  • 學習最佳實踐,以充分利用本書在圖像識別、物體分類、遷移學習、GAN 等方面實施 CNN 模型的方法

  • 通過深入理解 AlexNet、VGG、GoogLeNet 等高級 CNN 架構,並將其應用於實際研究領域,提升您的技能

書籍描述

卷積神經網絡(CNN)正在改變視覺識別系統、自駕車、語音識別等多個應用領域。這些進展為構建和部署大規模 ConvNet 應用程序創造了前所未有的機會和挑戰。本書旨在帶領您了解 CNN 的構建模塊、實施 CNN 模型的最佳實踐以及如何應用它們來解決複雜的機器學習問題。

本書以深度神經網絡的概述開始,以圖像分類為例,並引導您構建第一個用於人臉檢測的 CNN。隨著進一步的學習,您將遇到有趣的示例,深入了解不同優化和可視化技術,以構建強大的模型。在解釋 CNN 架構的同時,本書還涵蓋了最常見和獲獎的 CNN 架構的案例研究。

此外,本書還探討了如何實現知識轉移,以訓練一個不需要大量數據的 CNN 模型。您將介紹轉移學習的概念,有助於提高 CNN 模型的性能,除了訓練數據需求。最後,本書還涉及基於注意力的 CNN,並以視覺問答應用為例。最後,本書詳細介紹了生成模型和一個新的應用 - 開始生成您自己的手寫 MNIST 數字。

通過閱讀本書,您將準備好在工作或項目中實施 CNN 模型,並使用極端數據集進行工作。

您將學到什麼


  • 從 CNN 基礎構建模塊到高級概念,了解它們可以應用於的實際領域。

  • 構建一個簡單的圖像分類器 CNN 模型,了解不同組件之間的交互作用。

  • 學習 CNN 模型優化和可視化技術

  • 實現獲獎的 CNN 架構,如 AlexNet、VGG、GoogLeNet、ResNet 等

  • 實際應用預訓練模型和示例,了解轉移學習方法。

  • 了解 GAN 生成器和鑑別器之間的區別。

  • 進入對抗訓練的人工智慧世界,以及 GAN 的應用

適合閱讀對象

本書適合數據科學家、機器學習從業者、深度學習和人工智慧愛好者,他們希望在構建卷積神經網絡模型方面更進一步。通過使用極端數據集和不同的 CNN 架構,建立高效智能的 ConvNet 模型。預期讀者具備機器學習和 CNN 的基礎知識。