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所在平台: Udemy |
课程主页: https://www.udemy.com/course/practical-deep-learning-with-pytorch/
课程评论：没有评论
课程名称：PyTorch实用深度学习 课程概述： 深度学习在当今有着越来越重要的应用，如人脸识别、自动驾驶汽车和医学诊断等。本课程旨在为任何人提供深度学习的坚实基础，尽管许多课程要么过于数学化，要么过于实用，但本课程在两者之间找到了恰当的平衡，非常适合没有强大数学背景的学习者，同时也能加速有数学基础的学习者对不同深度学习模型的理解。 学习方式： 本课程采用Python Notebook的形式进行授课，学习者可以在观看视频的同时实践和调整模型，从而深刻理解每一行代码。建议在观看视频时亲自输入每一行代码，以便熟悉语法。 逐步学习： 与许多缺乏基础过渡的指南不同，本课程将每个主题紧密连接。从逻辑回归开始，课程会逐步解释每个模型是如何从前一个模型演变而来的，使学习者能够顺利掌握深度学习的结构。 图示驱动代码： 本课程使用了100多个自制图示，帮助学习者清晰地看到模型之间的过渡，深入理解模型的本质。这些图示帮助学习者将理论与代码紧密结合。 导师支持： 课程提供免费的提问机会，学习者可以向我提问，无论问题多么基础。我将尽力回应，帮助学习者穿越从基础到高级理论的壁垒。 数学基础问答： 本课程不重视深度学习背后的数学，而是注重如何理解事物的运作机制，为未来更深入的课程打下基础。虽然涉及一定的数学内容，但旨在增强理解，并提供温和的学习曲线。 最新Python Notebook兼容PyTorch 0.4和1.0： 本课程与PyTorch 0.3相比，变化极小，容易过渡到这个深度学习系列之中。 通过本课程，您可以在深度学习的世界中迈出坚实的一步，逐步积累知识，获取实际操作经验。
Growing Importance of Deep LearningDeep learning underpins a lot of important and increasingly important applications today ranging from facial recognition, to self-driving cars, to medical diagnostics and more. Made for Anyone Although many courses are very mathematical or too practical in nature, this course strikes a careful balance between the two to provide a solid foundation in deep learning for you to explore further if you are interested in research in the field of deep learning and/or applied deep learning. It is purposefully made for anyone without a strong background in mathematics. And for those with a strong background, it would accelerate your learning in understanding the different models in deep learning. Code As You Learn This entire course is delivered in a Python Notebook such that you can follow along the videos and replicate the results. You can practice and tweak the models until you truly understand every line of code as we go along. I highly recommend you to type every line of code when you are listening to the videos as this will help a lot in getting used to the syntax. Gradual Learning Style The thing about many guides out there is that they lack the transition from the very basics and people often get lost or miss out vital links that are critical in understanding certain models. Because of this, you can see how every single topic is closely linked with one another. In fact, at the beginning of every topic from logistic regression, I take the time to carefully explain how one model is simply a modification from the previous. That is the marvel of deep learning, we can trace back some part of it to linear regression where we will start. Diagram-Driven Code This course uses more than 100 custom-made diagrams where I took hundreds of hours to carefully create such that you can clearly see the transition from one model to another and understand the models comprehensively. Also, the diagrams are created so you can clearly see the link between the theory that I would teach and the code you would learn. Mentor Availability When I first started learning, I wished I had a mentor to guide me through the basics till the advanced theories where you can publish research papers and/or implement very complicated projects. And this course provides you with free access to ask any question, no matter how basic. I will be there and try my very best to answer your question. Even if the material is covered here, I will take the effort to point you to where you can learn here and more resources beyond this course.Math Prerequisite FAQThis is not a course that emphasizes heavily on the mathematics behind deep learning. It focuses on getting you to understand how everything works first which is very important for you to easily catch up on the mathematics later on. There are mathematics involved but they are limited with the sole aim to enhance your understanding and provide a gentle learning curve for future courses that would dive much deeper into it. Latest Python Notebooks Compatible with PyTorch 0.4 and 1.0There are very small changes from PyTorch 0.3 for this deep learning series where you will find it is extremely easy to transit over!
