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所在平台: Udemy |
课程主页: https://www.udemy.com/course/machine-learning-for-android-developer-using-tensorflow-lite/
课程评论:没有评论
课程名称:使用 TensorFlow Lite 的 Java/Kotlin 安卓机器学习 课程概述:厌倦了传统的安卓应用开发课程吗?现在是时候学习一些新兴且热门的技术了。机器学习正处于高峰期,安卓应用开发也在持续需求中,那么学习二者结合的知识是最好的选择!本课程旨在帮助安卓开发者了解机器学习,并在安卓应用中使用 TensorFlow Lite 部署机器学习模型。如果您对安卓开发有基本了解,并想学习如何在安卓应用中应用机器学习,这门课非常适合您。课程将带您开始构建您的第一个深度学习模型和安卓应用,使用 Java 和 Kotlin 以及 Android Studio。我们首先学习机器学习和深度学习的基础知识,然后训练您的第一个模型,并在安卓应用中部署。 本课程的所有材料均免费。课程允许使用 Java 和 Kotlin 两种语言学习,且为这两种编程语言提供了独立讲座。您无需具备先前的机器学习知识。课程内容涵盖: 1. Python 编程语言基础 2. 数据科学库 3. 机器学习与深度学习的基础 4. TensorFlow 和 TensorFlow Lite 5. 训练第一个机器学习模型,开发安卓应用 我们将通过简单到高级的实例进行学习,包括: - 预测汽车燃油效率(回归例子) - 识别手写数字(分类例子) - 猫狗分类 - 剪刀石头布问题 - 识别花卉、石头、水果 - 预测个人健康状况等实践活动 每个实例都将首先训练机器学习模型,然后构建相应的安卓应用程序。我们也将学习神经网络的基本结构,以及如何使用 TensorFlow 2.0 库训练机器学习模型,并将其转换为 tflite 格式,以供安卓应用使用。课程中将详细介绍监督学习的分类和回归,并通过实例进行解释。 如果您是一名希望在安卓应用中集成机器学习(深度学习)的初学者,或希望使用 TensorFlow Lite 和 Android Studio 部署机器学习模型的安卓开发者,这门课程将非常适合您。我们将通过许多实践实例,帮助您学习如何训练和部署机器学习模型。 面向对象: - 初学者安卓开发者,希望让其安卓应用更智能 - 有兴趣在安卓应用中使用机器学习的安卓开发者 - 对机器学习和计算机视觉的实际应用感兴趣的开发者 - 有意在安卓中使用机器学习模型的专业人士 - 希望将机器学习模型部署到安卓的机器学习专家 欢迎参加此课程,开始您的机器学习与安卓开发之旅!
Tired of traditional Android App Development courses? Now it's time to learn something new and trending for Android. Machine Learning is at its peak and Android App Development is also in demand so what is better than learning both?This course is designed for Android developers who want to learn Machine Learning and deploy machine learning models in their Android apps using TensorFlow Lite. If you have very basic knowledge of Android App development and want to learn Machine Learning use in Android Applications this course is for you. This course will get you started in building your FIRST deep learning model and Android Application using both Java and Kotlin Tensorflow Lite, and Android Studio. We will learn about machine learning and deep learning and then train your first model and deploy it in an Android application using Android Studio. All the materials for this course are FREE.You can follow this course using both Java and Kotlin. Separate Lectures are provided for both of these languages.You don't need any prior knowledge of Machine Learning to start this course. We will start by learningPython Programming LanguageData Science LibrariesBasics of Machine Learning and Deep LearningTensorflow and Tensorflow LiteThen we will train our first Machine Learning model and Develop an Android Application using Android Studio.The course includes examples from basic to advancedA very simple Machine Learning examplePredicting fuel efficiency of automobiles (Regression Example)Recognizing handwritten digits (Classification example)Cats and Dogs classificationRock Paper and Scissors ProblemFlowers Recognition ExampleStones Recognition ExampleFruits Recognition ExamplePredicting the Fitness of a Person Practice ActivityHuman and Horse Practice ActivityFor each of these examples, we will first train the machine-learning model and then build an Android ApplicationWe will start by learning about the basics of the Python programming language. Then we will learn about some famous Machine Learning libraries like Numpy, Matplotlib, and Pandas. After that, we will learn about Machine learning and its types. Then we look at Supervised learning in detail. We will try to understand classification and regression through examples. After we will start Deep learning. We start by looking and the basic structure of neural networks. Then we will understand the working of neural networks through an example. Then we will learn about the Tensorflow 2.0 library and how we can use it to train Machine Learning models. After that, we will look at Tensorflow lite and how we can convert our Machine Learning models to tflite format which will be used inside Android Applications. There are three ways through which you can get a tflite file From Keras ModelFrom Concrete FunctionFrom Saved ModelWe will cover all these three methods in this course.We will learn about Feed Forwarding, Back Propagation, and activation functions through a practical example. We also look at cost function, optimizer, learning rate, Overfitting, and Dropout. We will also learn about data preprocessing techniques like One hot encoding and Data normalization.Next, we implement a neural network using Google's new TensorFlow library.You should take this course If you are an Android Developer and want to learn the basics of machine learning(Deep Learning) and deploy ML models in your Android applications using Tensorflow lite and Android Studio.This course provides you with many practical examples so that you can learn how you can train and deploy machine learning models in Android. We will use Android Studio to develop Android Applications for the models we trained.Another section at the end of the course shows you how you can use datasets available in different formats for a number of practical purposes.After getting your feet wet with the fundamentals, I provide a brief overview of how you can add your machine-learning model in Google's existing Android machine-learning project templates.Who this course is for:Beginner Android Developers want to make their Android applications smartAndroid Developers want to use Machine Learning in their Android ApplicationsDevelopers interested in the practical implementation of Machine Learning and computer visionStudents interested in machine learning - you'll get all the tidbits you need to add machine learning models in Android using Android studioProfessionals who want to use machine learning models in Android Applications.Machine Learning experts want to deploy their models in Android using Android Studio and Tensorflow Lite