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所在平台: Udemy |
课程主页: https://www.udemy.com/course/a-practical-guide-to-deep-learning-with-keras/
课程评论：没有评论
课程名称：使用Keras进行深度学习的实用指南 课程概述：Keras是一个用Python编写的开源神经网络库，是一个快速高效的深度学习模型训练库。它是一个轻量级且模块化的框架，可以在CPU和GPU上运行，使得用户可以在最短的时间内将创意付诸实践。由于其易于使用，Keras在短时间内获得了广泛的欢迎。本课程通过实践的步骤，涵盖了图像处理和强化学习中的深度学习模型的快速高效实现。 课程结构： 本课程分为两个完整的部分，旨在提供全面的培训。第一个部分是《使用Keras进行深度学习》，它介绍了如何用Python实现深度学习神经网络，帮助学员通过实践方式掌握Keras的基础知识。第二个部分是《使用Keras和Python进行实践人工智能》，教授如何利用Keras构建复杂的深度学习网络，通过较少的代码实现更高效的开发。学员将学习解决图像处理领域相关的实际问题，开发自动化和人工智能技术，最终能够使用Keras和Python构建现实世界的人工智能应用。 课程目标： - 学习反向传播并安装配置Keras，理解回调函数和自定义流程。 - 从零开始开发一个深度学习网络，解决交通标志分类的实际问题。 - 掌握Keras，轻松实现快速高效的深度学习模型。 - 使用Keras构建复杂的深度学习网络，减少Python代码行数。 作者介绍： - Antonio Gulli是软件执行官和商业领袖，擅长技术人才的管理和创新。他在欧洲多个国家有丰富的专业经验，曾担任多个技术职位。 - Sujit Pal是Elsevier Labs的技术研究总监，主要研究自然语言处理、机器学习等领域，目前专注于深度学习模型的图像分类。 - Sandipan Das是一名高级软件工程师，专注于自动驾驶技术领域，拥有超过8年的软件开发经验，对神经网络架构有深入理解。 该课程适合希望深入学习和实践深度学习技术的学员。
Keras is an Open source Neural Network library written in Python. It is a Deep Learning library for fast, efficient training of Deep Learning models. It is a minimal, highly modular framework that runs on both CPUs and GPUs and allows you to put your ideas into action in the shortest possible time. Because it is lightweight and very easy to use, Keras has gained quite a lot of popularity in a very short time.This comprehensive 3-in-1 course takes a step-by-step practical approach to implement fast and efficient Deep Learning models: Projects on Image Processing and Reinforcement Learning. Initially, you'll learn backpropagation, install and configure Keras to understand callbacks and customize the process. You'll develop a deep learning network from scratch with Keras using Python to solve a practical problem of classifying the traffic signs on the road. Finally, you'll get to grips with Keras to implement fast and efficient deep-learning models with ease.Towards the end of this course, you'll use AI with Keras for building complex Deep Learning networks with fewer lines of coding in Python.Contents and OverviewThis training program includes 2 complete courses, carefully chosen to give you the most comprehensive training possible.The first course, Deep Learning with Keras, covers implementing deep learning neural networks with Python. Keras is a high-level neural network library written in Python and runs on top of either Theano or TensorFlow. It is a minimal, highly modular framework that runs on both CPUs and GPUs and allows you to put your ideas into action in the shortest possible time. This course will help you get started with the basics of Keras, in a highly practical manner.The second course, Hands-On Artificial Intelligence with Keras and Python, covers how to use AI with Keras for building complex Deep Learning networks with fewer lines of coding in Python. This course will help you learn by doing an industry relevant problem in image processing domain, develop and understand automation and AI techniques. You will learn how to harness the power of algorithms by creating apps which intelligently interact with the world around you, addressing common challenges faced in AI ecosystem. By the end of the course, you will be able to build real-world artificial intelligence applications using Keras and Python.Towards the end of this course, you'll use AI with Keras for building complex Deep Learning networks with fewer lines of coding in Python.About the AuthorsAntonio Gulli is a software executive and business leader with a passion for establishing and managing global technological talent, innovation, and execution. He is an expert in search engines, online services, machine learning, information retrieval, analytics, and cloud computing. So far, he has been lucky enough to gain professional experience in four different countries in Europe and has managed people in six different countries in Europe and America. Antonio served as CEO, GM, CTO, VP, director, and site lead in multiple fields ranging from publishing (Elsevier) to consumer internet (Ask.com and Tiscali) and high-tech R & D (Microsoft and Google).Sujit Pal is a technology research director at Elsevier Labs, working on building intelligent systems around research content and metadata. His primary interests are information retrieval, ontologies, natural language processing, machine learning, and distributed processing. He is currently working on image classification and similarity using deep learning models. Prior to this, he worked in the consumer healthcare industry, where he helped build ontology-backed semantic search, contextual advertising, and EMR data processing platforms. He writes about technology on his blog at Salmon Run.Sandipan Das is working as a senior software engineer in the field of perception within the Autonomous vehicles industry in Sweden. He has more than 8 years of experience in developing and architecting various software components. He understands the industry needs and the gaps in between a traditional university degree and the job requirements in the industry. He has worked extensively on various neural network architectures and deployed them in real vehicles for various perception tasks in real-time.
