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所在平台: Udemy |
课程主页: https://www.udemy.com/course/learning-path-keras-deep-learning-with-keras/
课程评论：没有评论
课程名称：学习路径：Keras：使用Keras进行深度学习 概述：Keras是一个用Python编写的深度学习库，旨在快速有效地训练深度学习模型，并可与TensorFlow和Theano配合使用。由于其轻量级和易用性，Keras在短时间内受到了广泛的欢迎。该学习路径适合有一定机器学习经验并接触过神经网络的数据科学家。Packt的视频学习路径是一系列逻辑性强且循序渐进的视频产品，每个视频都在之前学到的技能基础上进行扩展。 学习重点包括： - 理解机器学习和深度学习的主要概念 - 处理涉及图像、文本、时间序列、声音和视频的各种数据 - 学习构建自编码器和生成对抗网络 学习旅程概述： - 从Keras基础开始，采用高度实用的方式 - 深入深度学习，包括卷积神经网络（CNN）和递归神经网络（RNN），这两者是深度学习的基石 - 学习推荐系统及其类型 - 探索流行的Keras框架进行风格迁移，学习高级技术及风格迁移机制的深入解析 - 构建、训练和运行生成对抗网络（GAN），了解其流行架构，并学习如何优化其性能 - 实践训练CNN、RNN、LSTM、自编码器和生成对抗网络，使用真实的训练数据集 - 学习生成对抗网络的概念与应用，通过Keras实现，并使用批量归一化技术提高性能 完成本学习路径后，您将熟练掌握深度学习及其在Keras中的实现，并能够解决不同类型的问题。 专家介绍： 课程专家包括Philippe Remy和Tsvetoslav Tsekov。Philippe Remy是一名研究工程师和创业者，专注于深度学习，居住在日本东京。他参与了与手写字符识别、时间序列分析和自然语言处理相关的人工智能算法研究。Tsvetoslav Tsekov则在多种软件开发项目中拥有五年的经验，并在深度学习领域产生了浓厚的兴趣。 通过这门课程，您将踏上深入了解深度学习及其实际应用的旅程。
Keras is a deep learning library written in Python for quick, efficient training of deep learning models, and can also work with Tensorflow and Theano. Because of its lightweight and very easy to use nature, Keras has become popularity in a very short span of time. So, if you are a data scientist with experience in machine learning with some exposure to neural networks, then go for this Learning Path. Packt's Video Learning Paths are a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it.The highlights of this Learning Path are: Understand the main concepts of machine learning and deep learning Work with any kind of data involving images, text, time series, sound and videos Learn to build auto encoders and generative adversarial networks Let's take a quick look at your learning journey. You will start with the basics of Keras, in a highly practical manner. You will then dive into deep learning with convolutional and recurrent neural networks, which are the cornerstones of deep learning. You will then take to look at recommender system and some of its types. You will move ahead with a popular Keras framework for style transfer, some advanced techniques and in-depth explanations of the style transfer mechanism. You will also learn to build, train and run generative adversarial networks, go through some of its most popular architectures, and learn techniques to make them work better. Next, you will get an hands-on training of CNNs, RNNs, LSTMs, autoencoders and generative adversarial networks using real-world training datasets. Finally, you will learn the concepts and applications of generative adversarial networks, implementation with Keras, using Batch Normalization to improve performance. By the end of this Learning Path, you will be well-versed with deep learning and its implementation with Keras and will be able to solve different kinds of problems. Meet Your Expert: We have the best works of the following esteemed author to ensure that your learning journey is smooth: Philippe Remy is a research engineer and entrepreneur working on deep learning and living in Tokyo, Japan. As a research engineer, Philippe reads scientific papers and implements artificial intelligence algorithms related to handwriting character recognition, time series analysis, and natural language processing. As an entrepreneur, his vision is to bring a meaningful and transformative impact to society with the ultimate goal of enhancing overall quality of life and pushing the limits of what is considered possible today. Philippe contributes to different open source projects related to deep learning and fintech (github. com/philipperemy). You can visit Philippe Remy's blog on philipperemy. github.io. TsvetoslavTsekov has worked for 5 years on various software development projects - desktop applications, backend applications, WinCE embedded software, RESTful APIs. He then became exceedingly interested in Artificial Intelligence and particularly Deep Learning. After receiving his Deep Learning Nanodegree, he has worked on numerous projects - Image Classification, Sport Results Prediction, Fraud Detection, and Machine Translation. He is also very interested in General AI research and is always trying to stay up to date with the cutting-edge developments in the field.
