Applied Deep Learning with Python: Use scikit-learn, TensorFlow, and Keras to create intelligent systems and machine learning solutions

Alex Galea, Luis Capelo

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商品描述

A hands-on guide to deep learning that's filled with intuitive explanations and engaging practical examples

Key Features

  • Designed to iteratively develop the skills of Python users who don't have a data science background
  • Covers the key foundational concepts you'll need to know when building deep learning systems
  • Full of step-by-step exercises and activities to help build the skills that you need for the real-world

Book Description

Taking an approach that uses the latest developments in the Python ecosystem, you'll first be guided through the Jupyter ecosystem, key visualization libraries and powerful data sanitization techniques before we train our first predictive model. We'll explore a variety of approaches to classification like support vector networks, random decision forests and k-nearest neighbours to build out your understanding before we move into more complex territory. It's okay if these terms seem overwhelming; we'll show you how to put them to work.

We'll build upon our classification coverage by taking a quick look at ethical web scraping and interactive visualizations to help you professionally gather and present your analysis. It's after this that we start building out our keystone deep learning application, one that aims to predict the future price of Bitcoin based on historical public data.

By guiding you through a trained neural network, we'll explore common deep learning network architectures (convolutional, recurrent, generative adversarial) and branch out into deep reinforcement learning before we dive into model optimization and evaluation. We'll do all of this whilst working on a production-ready web application that combines Tensorflow and Keras to produce a meaningful user-friendly result, leaving you with all the skills you need to tackle and develop your own real-world deep learning projects confidently and effectively.

What you will learn

  • Discover how you can assemble and clean your very own datasets
  • Develop a tailored machine learning classification strategy
  • Build, train and enhance your own models to solve unique problems
  • Work with production-ready frameworks like Tensorflow and Keras
  • Explain how neural networks operate in clear and simple terms
  • Understand how to deploy your predictions to the web

Who this book is for

If you're a Python programmer stepping into the world of data science, this is the ideal way to get started.

Table of Contents

  1. Jupyter Fundamentals
  2. Data Cleaning and Advanced Machine Learning
  3. Web Scraping and Interactive Visualizations
  4. Introduction to Neural Networks and Deep Learning
  5. Model Architecture
  6. Model Evaluation
  7. Productization

商品描述(中文翻譯)

一本充滿直觀解釋和引人入勝實際例子的深度學習實踐指南。

主要特點:
- 設計給沒有數據科學背景的Python使用者,逐步發展技能。
- 講解構建深度學習系統時需要了解的基礎概念。
- 提供一系列逐步練習和活動,幫助您建立實際應用所需的技能。

書籍描述:
本書採用最新的Python生態系統發展方法,首先引導您了解Jupyter生態系統、關鍵可視化庫和強大的數據清理技術,然後訓練第一個預測模型。我們將探索各種分類方法,如支持向量網絡、隨機決策森林和k最近鄰算法,以擴展您的理解能力,然後進入更複雜的領域。如果這些術語讓您感到壓倒性,沒關係,我們將向您展示如何應用它們。

我們將通過快速了解道德網絡爬蟲和交互式可視化來擴展我們的分類範圍,以幫助您專業地收集和呈現分析結果。然後,我們開始構建我們的核心深度學習應用程序,該應用程序旨在根據歷史公共數據預測比特幣的未來價格。

通過引導您使用訓練過的神經網絡,我們將探索常見的深度學習網絡架構(卷積、循環、生成對抗)並進入深度強化學習,然後深入研究模型優化和評估。在這個過程中,我們將開發一個生產就緒的Web應用程序,結合Tensorflow和Keras,以產生有意義且用戶友好的結果,讓您具備自信和高效地應對和開發自己的實際深度學習項目所需的所有技能。

您將學到什麼:
- 發現如何組合和清理自己的數據集。
- 制定量身定制的機器學習分類策略。
- 構建、訓練和增強自己的模型以解決獨特問題。
- 使用Tensorflow和Keras等生產就緒框架。
- 以清晰簡單的方式解釋神經網絡的運作原理。
- 理解如何將預測部署到Web上。

本書適合對象:
如果您是一名Python程序員,想進入數據科學領域,這是理想的入門方式。

目錄:
1. Jupyter基礎
2. 數據清理和高級機器學習
3. 網絡爬蟲和交互式可視化
4. 神經網絡和深度學習入門
5. 模型架構
6. 模型評估
7. 產品化