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所在平台: Udemy |
课程主页: https://www.udemy.com/course/learning-path-tensorflow-computer-vision-with-tensorflow/
课程评论：没有评论
课程名称：学习路径：使用TensorFlow进行计算机视觉 课程概述：近年来，TensorFlow因其强大和易用性而备受关注。如果你是一位有兴趣学习如何使用TensorFlow创建应用程序并进行图像处理的Python开发者，这个学习路径将十分适合你。Packt的在线视频学习路径将一系列单独的视频产品以逻辑且逐步的方式组合在一起，以便每个视频都在之前学习的技能基础上进行扩展。 这一学习路径的亮点包括： - 学习如何使用免费工具和库创建图像处理应用程序 - 使用TensorFlow API进行高级图像处理 - 通过建立深度学习的先进模型了解和优化TensorFlow的各种特性 学习旅程简述： 该学习路径从图像处理的介绍开始，接着介绍用于图像分类的图图张量。你将从基础的2D图像逐步学习更复杂的图像、颜色、形状等内容。你还将学习使用Python API来分类并训练模型以识别图像中的对象。接下来，课程将深入讲解卷积神经网络（CNN）的结构，以及为何其在图像处理任务中表现优异。你将探索TensorFlow中可用的不同层，构建神经网络特征提取器，将图像嵌入到密集而丰富的向量空间中。 继续深入，你将学习构建高效的CNN体系结构，包括CNN压缩层和延迟下采样，了解残差学习及跳跃连接的深度残差模块，并看到如何实现深度残差神经网络进行图像识别。此外，你将了解到Google的Inception模块和深度可分卷积，并理解如何使用TF-Keras构建极端的Inception架构。最后，你将被引入到对抗性神经网络的刺激新世界，这些网络在合成图像生成方面取得了近期突破，并实现辅助条件生成对抗网络（GAN）。 通过这个学习路径的学习，你将能够高效地创建应用程序并进行图像处理。 专家介绍： 我们邀请了以下著名作者提供最佳课程以确保你的学习过程顺利：Marvin Bertin是在线深度学习课程的作者，深度学习书籍的技术编辑及会议讲者。他拥有机械工程学士学位和数据科学硕士学位，在一家深度学习初创公司工作，开发神经网络架构。现他在生物技术行业，构建用于精确医疗的NLP机器学习解决方案，走在下一代DNA测序的前沿，致力于利用机器学习和深度学习构建智能应用。
TensorFlow has been gaining immense popularity over the past few months, due to its power and simplicity to use. So, if you're a Python developer who is interested in learning how to create applications and perform image processing using TensorFlow, then you should surely go for this Learning Path. Packt's Video Learning Path is 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: Learn how to create image processing applications using free tools and librariesPerform advanced image processing with TensorFlowAPIsUnderstand and optimize various features of TensorFlow by building deep learning state-of-the-art models Let's take a quick look at your learning journey. This Learning Path starts off with an introduction to image processing. You will then walk through graph tensor which is used for image classification. Starting with the basic 2D images, you will gradually be taken through more complex images, colors, shapes, and so on. You will also learn to make use of Python API to classify and train your model to identify objects in an image. Next, you will learn about convolutional neural networks (CNNs), its architecture, and why they perform well in the image take. You will then dive into the different layers available in TensorFlow. You will also learn to construct the neural network feature extractor to embed images into a dense and rich vector space. Moving ahead, you will learn to construct efficient CNN architectures with CNN Squeeze layers and delayed downsampling. You will learn about residual learning with skip connections and deep residual blocks, and see how to implement a deep residual neural network for image recognition. Next, you will find out about Google's Inception module and depth-wise separable convolutions and understand how to construct an extreme Inception architecture with TF-Keras. Finally, you will be introduced to the exciting new world of adversarial neural networks, which are responsible for recent breakthroughs in synthetic image generation and implement an auxiliary conditional generative adversarial networks (GAN). By the end of this Learning Path, you will be able to create applications and perform image processing efficiently. Meet Your Expert: We have the best work of the following esteemed author to ensure that your learning journey is smooth: Marvin Bertin has authored online deep learning courses. He is the technical editor of a deep learning book and a conference speaker. He has a bachelor's degree in mechanical engineering and master's in data science. He has worked at a deep learning startup developing neural network architectures. He is currently working in the biotech industry building NLP machine learning solutions. At the forefront of next generation DNA sequencing, he builds intelligent applications with machine learning and deep learning for precision medicine.
