Learning Path - Python: Advanced Guide to Artificial Intelligence: Expert techniques to train advanced neural networks and self-learning agents using Python

Giuseppe Bonaccorso, Armando Fandango, Rajalingappaa Shanmugamani

  • 出版商: Packt Publishing
  • 出版日期: 2018-12-12
  • 售價: $2,010
  • 貴賓價: 9.5$1,910
  • 語言: 英文
  • 頁數: 805
  • 裝訂: Paperback
  • ISBN: 1789957214
  • ISBN-13: 9781789957211
  • 相關分類: Python程式語言人工智慧
  • 海外代購書籍(需單獨結帳)

商品描述

Demystify the complexity of machine learning techniques and create evolving, clever solutions to solve your problems

Key Features

  • Master supervised, unsupervised, and semi-supervised ML algorithms and their implementation
  • Build deep learning models for object detection, image classification, similarity learning, and more
  • Build, deploy, and scale end-to-end deep neural network models in a production environment

Book Description

This Learning Path is your complete guide to quickly getting to grips with popular machine learning algorithms. You'll be introduced to the most widely used algorithms in supervised, unsupervised, and semi-supervised machine learning, and learn how to use them in the best possible manner. Ranging from Bayesian models to the MCMC algorithm to Hidden Markov models, this Learning Path will teach you how to extract features from your dataset and perform dimensionality reduction by making use of Python-based libraries.

You'll bring the use of TensorFlow and Keras to build deep learning models, using concepts such as transfer learning, generative adversarial networks, and deep reinforcement learning. Next, you'll learn the advanced features of TensorFlow1.x, such as distributed TensorFlow with TF clusters, deploy production models with TensorFlow Serving, and build and deploy TensorFlow models for mobile and embedded devices on Android and iOS platforms. You'll implement different techniques related to object classification, object detection, image segmentation, captioning, image generation, face analysis, and more.

By the end of this Learning Path, you'll have obtained in-depth knowledge of TensorFlow, making you the go-to person for solving artificial intelligence problems

This Learning Path includes content from the following Packt products:

  • Mastering Machine Learning Algorithms by Giuseppe Bonaccorso
  • Mastering TensorFlow 1.x by Armando Fandango
  • Deep Learning for Computer Vision by Rajalingappaa Shanmugamani

What you will learn

  • Explore how an ML model can be trained, optimized, and evaluated
  • Work with Autoencoders and Generative Adversarial Networks
  • Explore the most important Reinforcement Learning techniques
  • Build end-to-end deep learning (CNN, RNN, and Autoencoders) models
  • Define and train a model for image and video classification
  • Deploy your deep learning models and optimize them for high performance

Who This Book Is For

This Learning Path is for data scientists, machine learning engineers, artificial intelligence engineers who want to delve into complex machine learning algorithms, calibrate models, and improve the predictions of the trained model.

You will encounter the advanced intricacies and complex use cases of deep learning and AI. A basic knowledge of programming in Python and some understanding of machine learning concepts are required to get the best out of this Learning Path.

商品描述(中文翻譯)

解密機器學習技術的複雜性,並創造出不斷演進的聰明解決方案來解決您的問題。

主要特點:
- 掌握監督式、非監督式和半監督式機器學習算法及其實現
- 構建用於物體檢測、圖像分類、相似性學習等的深度學習模型
- 在生產環境中構建、部署和擴展端到端的深度神經網絡模型

書籍描述:
本學習路徑是您快速掌握流行機器學習算法的完整指南。您將介紹監督式、非監督式和半監督式機器學習中最常用的算法,並學習如何以最佳方式使用它們。從貝葉斯模型到MCMC算法再到隱馬爾可夫模型,本學習路徑將教您如何通過使用基於Python的庫從數據集中提取特徵並進行降維。

您將使用TensorFlow和Keras構建深度學習模型,並使用轉移學習、生成對抗網絡和深度強化學習等概念。接下來,您將學習TensorFlow1.x的高級功能,例如使用TF集群進行分佈式TensorFlow、使用TensorFlow Serving部署生產模型,以及在Android和iOS平台上構建和部署針對移動和嵌入式設備的TensorFlow模型。您將實現與物體分類、物體檢測、圖像分割、字幕生成、圖像生成、人臉分析等相關的不同技術。

通過完成本學習路徑,您將深入了解TensorFlow,成為解決人工智能問題的專家。

本學習路徑包含以下Packt出版的內容:
- 《精通機器學習算法》(作者:Giuseppe Bonaccorso)
- 《精通TensorFlow 1.x》(作者:Armando Fandango)
- 《計算機視覺的深度學習》(作者:Rajalingappaa Shanmugamani)

您將學到什麼:
- 探索如何訓練、優化和評估機器學習模型
- 使用自編碼器和生成對抗網絡
- 探索最重要的強化學習技術
- 構建端到端的深度學習(CNN、RNN和自編碼器)模型
- 定義並訓練圖像和視頻分類模型
- 部署深度學習模型並優化其性能

本書適合對象:
本學習路徑適合數據科學家、機器學習工程師、人工智能工程師,他們希望深入研究複雜的機器學習算法,校準模型並改進訓練模型的預測能力。

您將遇到深度學習和人工智能的高級細節和複雜用例。為了充分利用本學習路徑,需要基本的Python編程知識和對機器學習概念的一些理解。