Machine Learning by Tutorials Book Details
Title: Machine Learning by Tutorials
Author: By Alexis Gallagher, Matthijs Hollemans, Audrey Tam & Chris LaPollo
Publisher: Ray Wenderlich
Subject: Swift / Computers & Technology / Programming / Apple Programming
No. of pages: 586
Format: PDF, EPUB, Source code
Recently I bought a set of 10 IOS books – Advance IOS and Swift Bundle from Ray Wenderlich. As you can see in the image above, which includes Machine Learning by Tutorials. And now I want to transfer it to you for $ 60 (10 books) Payment Via Paypal or Bitcoin, All books are the latest version and have full source code, I will share it for you for $ 60 Includes EPUB file and full source code, you can download on Google Drive. When any book have new version i will get it free for you.
List bundle 10 books: Advance IOS and Swift:
2, Server Side Swift with Vapor
3, Push Notifications by Tutorials
5, Data Structures and Algorithms in Swift
6, Realm Building Modern Swift Apps with Realm
7, RxSwift Reactive Programming with Swift
9, Machine Learning by Tutorials
10, Advanced iOS App Architecture
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About the Cover Machine Learning by Tutorials Book
The orca, or more commonly known as the killer whale, is one of the most intelligent — and lethal — predators in the sea. Orcas are incredibly smart and have often been seen using problem-solving techniques in the wild as they learn to hunt and even steal fish straight out of the nets of fishing boats. With the second-heaviest brains among marine mammals, orcas have a broad capacity for learning and general intelligence.
Most people know orcas through their playful choreographed performances at Sea World. In the wild, however, orcas are more than just playful mammals; they form highly complex social and familiar relationships that parallel the types of group bonding found in elephants and humans.
Although orcas are found in large numbers in most oceans around the world, tracking their migration patterns has proved difficult despite decades of research, since entire groups of orca are known to simply disappear at times, only to reappear months later.
In fact, machine learning is starting to play a part in tracking the migration patterns of large whales, with up to 98% accuracy. Read more about how machine learning is helping measure the impact of human activities on whales here:
Section I: Machine Learning by Tutorials with Images
This section introduces you to the world of machine learning. You’ll get a high level view of what it is, and how it can be used on mobile. You’ll also get a quick primer on using Python for machine learning. You’ll learn how to set up an environment to use tools such as CreateML, Turi Create, and Keras for machine learning. Finally, you’ll learn how to use machine learning techniques to solve problems using images. The topics you’ll explore include image classification, object detection with bounding boxes, and object segmentation.
• Chapter 1, Machine Learning, iOS & You: In this introduction chapter, you’ll learn what machine learning is all about. You’ll touch on everything from, the difference between supervised and unsupervised learning, to what transfer learning is. You’ll even go over the ethics of machine learning, and how bias can affect models.
• Chapter 2, Getting Started with Image Classification: In this chapter, you’ll build your first iOS app by adding a CoreML model to detect whether a snack is healthy or unhealthy. You’ll focus on how machine learning can be used to solve classification problems such as trying to identify what an object might be.
• Chapter 3, Training the Image Classifier: In this chapter, you’ll start to build your first machine learning model using Create ML. You’ll be introduced to the dataset used to create the model, along with how Create ML uses transfer learning to get amazing classification results. Moreover, you’ll learn what it means to evaluate the performance of your model.
- Chapter 4, Getting Started with Python & Turi Create: In this chapter, you’ll get a quick primer on Python. You’ll learn how to setup your Python environment using Conda, and how to install external libraries. You’ll learn how to run and use Jupyter Machine Learning by Tutorials to iterate quickly with Python.
- Chapter 5, Digging Deeper Into Turi Create: In this chapter, you’ll learn to use Turi Create to build a classification model. You’ll use the snacks dataset to create your model. You’ll learn how to analyze your model’s performance, and how to go under the hood with Turi Create in order to improve your model.
- Chapter 6, Taking Control of Training with Keras: In this chapter, you’ll learn to how to take control of your model’s training with Keras. You’ll design your first neural network, and how to pass your dataset into Keras for training.
- Chapter 7, Going Convolutional: In this chapter, you’ll learn why a simple neural network might not be enough when it comes to solving problems with images using machine learning. You’ll learn about how using a convolutional neural network provides a better approach to solving classification problems.
- Chapter 8, Advanced Convolutional Neural Networks: In this chapter, you’ll learn about advanced model architectures used for solving image classification. You’ll learn how you can use Keras to do transfer learning, and how applying advanced techniques such as dropout and regularization can improve your model’s performance.
- Chapter 9, Beyond Classification: In this chapter, you’ll learn how to identify an object’s location in an image. You’ll learn how to build a simple localization model that predicts a single bounding box
- Chapter 10, YOLO & Semantic Segmentation: In this final chapter, you’ll learn about some advanced localization models. You’ll learn about one-shot detectors like YOLO and SSD and how they can be used to identify multiple objects in an image. You’ll also learn about how machine learning can be used for segmentation to separate an object from its background.
Section II: Machine Learning with Sequences
In this section, you’ll learn how to apply machine learning to sequential data. You’ll work on a new iOS app which attempts to identify a user’s activity using data from their iPhone’s motion sensors. In the process, you’ll learn how to build a good training dataset, how to create an activity classification model using Turi Create, and how to incorporate your model into an iOS app to support responsive classifications with real-time data.
• Chapter 11, Data Collection for Sequence Classification: In this chapter, you’ll learn how working with sequences differs from working with discrete data like individual images. You’ll learn how to collect iPhone sensor data, as well as what it takes to build a good training dataset.
• Chapter 12, Training a Model for Sequence Classification: In this chapter, you’ll learn about neural networks designed to work with sequences. You’ll also learn how to use Turi Create to train an activity classification model using data from the previous chapter.
• Chapter 13, Sequence Classification: In this chapter, you’ll learn how to pass real-time sequential data captured from a device’s motion sensors into your Core ML model. You’ll learn some tricks to help keep your apps responsive and accurate while processing sequences of streaming data.
Section III: Natural Language Processing
In this section, you’ll focus on a specific type of sequential data — natural language text. You’ll learn how to use Apple-provided APIs to perform common language processing tasks. You’ll also learn how to use text with neural networks, and you’ll create a model with Keras that translates text from Spanish to English Machine Learning by Tutorials. Finally, you’ll read about advanced techniques that you can experiment with to improve your model.
• Chapter 14, Natural Language Classification: In this chapter, you’ll learn how to use Apple’s Natural Language framework to handle several useful text-related tasks. You’ll explore this API in the context of a movie review app that supports multiple languages.
• Chapter 15, Natural Language Transformation, Part 1: In this chapter, you’ll learn about sequence-to-sequence models and how you can use them to do things like language translation. You’ll build a model with Keras that attempts to translate Spanish-language movie reviews into English.
• Chapter 16, Natural Language Transformation, Part 2: This chapter introduces additional techniques you can use to improve the performance of your sequence- to-sequence models.