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Applied Deep Learning with Python
Applied Deep Learning with Python

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

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Applied Deep Learning with Python

Data Cleaning and Advanced Machine Learning

The goal of data analytics, in general, is to uncover actionable insights that result in positive business outcomes. In the case of predictive analytics, the aim is to do this by determining the most likely future outcome of a target, based on previous trends and patterns.

The benefits of predictive analytics are not restricted to big technology companies. Any business can find ways to benefit from machine learning, given the right data.

Companies all around the world are collecting massive amounts of data and using predictive analytics to cut costs and increase profits. Some of the most prevalent examples of this are from the technology giants Google, Facebook, and Amazon, who utilize big data on a huge scale. For example, Google and Facebook serve you personalized ads based on predictive algorithms that guess what you are most likely...

Preparing to Train a Predictive Model

Here, we will cover the preparation required to train a predictive model. Although not as technically glamorous as training the models themselves, this step should not be taken lightly. It's very important to ensure you have a good plan before proceeding with the details of building and training a reliable model. Furthermore, once you've decided on the right plan, there are technical steps in preparing the data for modeling that should not be overlooked.

We must be careful not to go so deep into the weeds of technical tasks that we lose sight of the goal. Technical tasks include things that require programming skills, for example, constructing visualizations, querying databases, and validating predictive models. It's easy to spend hours trying to implement a specific feature or get the plots looking just right. Doing this sort...

Training Classification Models

As we've already seen in the previous chapter, using libraries such as scikit-learn and platforms such as Jupyter, predictive models can be trained in just a few lines of code. This is possible by abstracting away the difficult computations involved with optimizing model parameters. In other words, we deal with a black box where the internal operations are hidden instead. With this simplicity also comes the danger of misusing algorithms, for example, by overfitting during training or failing to properly test on unseen data. We'll show how to avoid these pitfalls while training classification models and produce trustworthy results with the use of k-fold cross-validation and validation curves.

Introduction to Classification Algorithms

...

Summary

In this chapter, we have seen how predictive models can be trained in Jupyter Notebooks.

To begin with, we talked about how to plan a machine learning strategy. We thought about how to design a plan that can lead to actionable business insights and stressed the importance of using the data to help set realistic business goals. We also explained machine learning terminologies such as supervised learning, unsupervised learning, classification, and regression.

Next, we discussed methods for preprocessing data using scikit-learn and pandas. This included lengthy discussions and examples of a surprisingly time-consuming part of machine learning: dealing with missing data.

In the latter half of the chapter, we trained predictive classification models for our binary problem, comparing how decision boundaries are drawn for various models such as the SVM, k-Nearest Neighbors, and...

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Key benefits

  • 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
  • Complete with step-by-step exercises and activities to help you build the skills you need for the real world

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 you train your first predictive model. You’ll then explore a variety of approaches to classification such as support vector networks, random decision forests and k-nearest neighbors to build on your knowledge before moving on to advanced topics. After covering classification, you’ll go on to discover ethical web scraping and interactive visualizations, which will help you professionally gather and present your analysis. Next, you’ll start building your keystone deep learning application, one that aims to predict the future price of Bitcoin based on historical public data. You’ll then be guided through a trained neural network, which will help you explore common deep learning network architectures (convolutional, recurrent, and generative adversarial networks) and deep reinforcement learning. Later, you’ll delve into model optimization and evaluation. You’ll do all this while working on a production-ready web application that combines TensorFlow and Keras to produce meaningful user-friendly results. By the end of this book, you’ll be equipped with the skills you need to tackle and develop your own real-world deep learning projects confidently and effectively.

Who is this book for?

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

What you will learn

  • Discover how you can assemble and clean your very own datasets
  • Develop a customized machine learning classification strategy
  • Build, train and enhance your own models to solve unique problems
  • Work with production-ready frameworks such as TensorFlow and Keras
  • Understand how neural networks operate in clear and simple terms
  • Deploy your predictions to the web
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Publication date, Length, Edition, Language, ISBN-13
Publication date : Aug 31, 2018
Length: 334 pages
Edition : 1st
Language : English
ISBN-13 : 9781789804744
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Product Details

Publication date : Aug 31, 2018
Length: 334 pages
Edition : 1st
Language : English
ISBN-13 : 9781789804744
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Concepts :
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Table of Contents

8 Chapters
Jupyter Fundamentals Chevron down icon Chevron up icon
Data Cleaning and Advanced Machine Learning Chevron down icon Chevron up icon
Web Scraping and Interactive Visualizations Chevron down icon Chevron up icon
Introduction to Neural Networks and Deep Learning Chevron down icon Chevron up icon
Model Architecture Chevron down icon Chevron up icon
Model Evaluation and Optimization Chevron down icon Chevron up icon
Productization Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.5
(4 Ratings)
5 star 50%
4 star 0%
3 star 25%
2 star 0%
1 star 25%
Sean K R Basler Nov 16, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
I got a lot out of reading this book, especially from the first few chapters. Jupyter notebooks are the best! Lessons are full of clear step-by-step directions and good examples—highly recommended!!
Amazon Verified review Amazon
Andrew Sep 25, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Lots of projects to dive into, and not a dry as some of the other titles in the area. Really liked the Bitcoin price prediction application near the end.
Amazon Verified review Amazon
Renaldo A Williams Oct 31, 2018
Full star icon Full star icon Full star icon Empty star icon Empty star icon 3
Learning a lot but typos make it hard to read without wondering how did they miss these typos.
Amazon Verified review Amazon
Bernard De Terwangne Oct 29, 2018
Full star icon Empty star icon Empty star icon Empty star icon Empty star icon 1
Ce livre est très confus. Aucun chapitre introductif pour expliquer où on va avec le livre ou un chapitre particulier du coup on ne s'y retrouve pas et il faut rentrer dans la technique pour comprendre ce que ça fait. Aucun prérequis donc on ne sait pas avant de commencer à installer si le PC sera suffisant, si l'OS convient etc. Pas même une explication claire de comment installer les modules donc c'est parti pour chercher sur Internet et perdre du temps. Résultat : difficile de dépasser le chapitre 1.
Amazon Verified review Amazon
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