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所在平台: Udemy |
课程主页: https://www.udemy.com/course/building-a-deep-learning-model-and-neural-network-with-keras/
课程评论：没有评论
课程名称：使用Keras进行深度学习 课程概述：欢迎参加Uplatz提供的“使用Keras进行深度学习”课程。Keras是一个用Python编写的开源神经网络组件库，能够运行在TensorFlow、Theano、PlaidML等多个平台之上。该库旨在模块化和用户友好的方式进行开发，支持快速实验，具有高水平、用户友好的模块化和可扩展API。Keras支持在CPU和GPU上运行，是TensorFlow的高级API之一。 Keras包含了常用的机器学习组件库，包括目标函数、激活函数和优化器，并支持递归神经网络和卷积神经网络。它还提供移动平台开发，便于用户在iOS和Android智能手机上实施深度学习模型。Keras旨在降低认知负担，提供一致且简单的API，最大限度减少用户在常见用例中的操作，并提供清晰有效的错误信息及丰富的文档和开发者指南。 课程亮点： - 深入理解深度学习及Keras的基本概念 - 学习Keras的模型、层和模块 - 掌握模型的编译、评估和预测 - 理解神经网络模型在Keras中的生命周期 - 实践构建多层感知器（MLP）神经网络和图像分类模型 - 学习卷积神经网络及其层的构建步骤 Keras的主要特点包括用户友好的体验、高度灵活性、跨平台支持、快速原型开发以及简单易学的特性，使其成为深度学习和机器学习工程师的理想选择。通过本课程，您将掌握如何在Python中实现深度学习，进行数据预处理，模型构建、评估和优化等技能。 请加入我们，开启深度学习的旅程，利用Keras使您的模型生产变得更加高效与创新！
A warm welcome to the Deep Learning with Keras course by Uplatz.Keras is an open-source library of neural network components written in Python. Keras is capable of running atop TensorFlow, Theano, PlaidML and others. The library was developed to be modular and user-friendly. Keras enables fast experimentation through a high level, user-friendly, modular and extensible API. Keras can also be run on both CPU and GPU. Keras was developed and is maintained by Francois Chollet and is part of the TensorFlow core, which makes it TensorFlow preferred high-level API.Comprised of a library of commonly used machine learning components including objectives, activation functions, and optimizers, Keras' open-source platform also offers support for recurrent and convolutional neural networks. Additionally, Keras offers mobile platform development for users intending to implement deep learning models on smartphones, both iOS and Android.Keras is essentially an API designed for machine learning and deep learning engineers and follows best practices for reducing cognitive load. Keras offers consistent & simple APIs, minimizes the number of user actions required for common use cases, and provides clear & actionable error messages. It also supports extensive documentation and developer guides. It is made user-friendly, extensible, and modular for facilitating faster experimentation with deep neural networks. It not only supports Convolutional Networks and Recurrent Networks individually but also their combinationWhy do we need Machine Learning libraries such as Keras?Machine learning uses a variety of math models and calculations to answer specific questions about data. Examples of machine learning in action include detecting spam emails, determining certain objects using computer vision, recognizing speech, recommending products, and even predicting commodities values years in the future.The calculations implicit in machine learning and deep learning are very complicated to set up to ensure correct output (answers). A variety of machine learning libraries have emerged to help navigate these complexities. With these options, new folks can start getting into data science easily. Some of the most popular machine learning libraries include:TensorFlowKerassciKit learnTheanoMicrosoft Cognitive Toolkit (CNTK)Uplatz provides this comprehensive course on Deep Learning with Keras. This Keras course will help you implement deep learning in Python, preprocess your data, model, build, evaluate and optimize neural networks. The Keras training will teach you how to use Keras, a neural network API written in Python. This Keras course will show how the full implementation is done in code using Keras and Python. You will learn how to organize data for training, build and train an artificial neural network from scratch, build and fine-tune convolutional neural networks (CNNs), implement fine-tuning and transfer learning, deploy models using both front-end and back-end deployment techniques.Deep Learning with Keras - Course Syllabus1. Introduction to Deep Learning & KerasWhat is deep learning?What is ANN?Introduction to Kerasa) Overview of Kerasb) Features of Kerasc) Benefits of KerasKeras Installation2. Keras - Models, Layers and ModulesKeras Modelsa) Sequential Modelb) Functional APIKeras Layersa) Dense Layersb) Dropout Layersc) Convolution Layersd) Pooling LayersKeras Modules3. Keras - Model Compilation, Evaluation and PredictionLossOptimizerMetricsCompile the modelModel TrainingModel EvaluationModel Prediction4. Life-Cycle for Neural Network Models in KerasDefine NetworkCompile NetworkFit NetworkEvaluate NetworkMake Predictions5. Building our first Neural Network with Keras(Building a Multilayer Perceptron neural network)Load DataDefine Keras ModelCompile Keras ModelFit Keras ModelEvaluate Keras ModelMake Predictions6. Building Image Classification Model with KerasWhat is Image Recognition (Classification)Convolutional Neural Network (CNN) & its layersBuilding Image Classification Model (step by step)Key Features of KerasKeras is an API designed for humansFocus on user experience has always been a major part of KerasLarge adoption in the industryHighly FlexibleIt is a multi backend and supports multi-platform, which helps all the encoders come together for codingResearch community present for Keras works amazingly with the production communityEasy to grasp all conceptsIt supports fast prototypingIt seamlessly runs on CPU as well as GPUIt provides the freedom to design any architecture, which then later is utilized as an API for the projectIt is really very simple to get started withEasy production of models actually makes Keras specialEasy to learn and use
