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所在平台: Udemy |
课程主页: https://www.udemy.com/course/neural-networks-with-tensorflow-and-pytorch/
课程评论：没有评论
课程名称：TensorFlow和PyTorch的神经网络 课程概述：本课程旨在教授深度学习和机器学习的基础知识，重点在于利用TensorFlow和PyTorch这两种主流框架来构建和训练神经网络。课程采用循序渐进的方式，通过真实世界的例子解释每个主题。您将学习TensorFlow的一些深度学习算法，如卷积神经网络和深度强化学习算法，包括深度Q网络和异步优势演员-评论家等。随后，您将深入探讨强化学习算法，并使用真实数据集掌握神经网络编程和自编码器的应用。 此外，课程中还将教授自然语言处理（NLP）的相关知识，您将学习如何编写程序让机器识别面孔、预测股市价格，并处理文本。接下来，您将探索PyTorch的动态神经网络编程。课程的最后，您将完成两个迷你项目，第一个项目专注于将动态神经网络应用于图像识别，第二个项目则解决NLP相关问题，如语法解析。 完成本课程后，您将全面理解TensorFlow和PyTorch这两个主要的机器学习库，能够无障碍地开发和训练各种复杂度的神经网络。 讲授专家简介： - Roland Meertens：目前正在开发自动驾驶汽车的计算机视觉算法，曾在翻译部门工作，参与了神经机器翻译实现等项目。 - Harveen Singh Chadha：深度学习研究员，专注于创建先进驾驶辅助系统，致力于帮助人们进入数据科学领域。 - Anastasia Yanina：高级数据科学家，拥有约5年经验，专攻深度学习和自然语言处理，致力于人机交互的促进。 总之，本课程为希望深入理解深度学习和神经网络的学习者提供了全面的知识和实用的项目经验。
TensorFlow is quickly becoming the technology of choice for deep learning and machine learning, because of its ease to develop powerful neural networks and intelligent machine learning applications. Like TensorFlow, PyTorch has a clean and simple API, which makes building neural networks faster and easier. It's also modular, and that makes debugging your code a breeze. If you're someone who wants to get hands-on with Deep Learning by building and training Neural Networks, then go for this course.This course takes a step-by-step approach where every topic is explicated with the help of a real-world examples. You will begin with learning some of the Deep Learning algorithms with TensorFlow such as Convolutional Neural Networks and Deep Reinforcement Learning algorithms such as Deep Q Networks and Asynchronous Advantage Actor-Critic. You will then explore Deep Reinforcement Learning algorithms in-depth with real-world datasets to get a hands-on understanding of neural network programming and Autoencoder applications. You will also predict business decisions with NLP wherein you will learn how to program a machine to identify a human face, predict stock market prices, and process text as part of Natural Language Processing (NLP). Next, you will explore the imperative side of PyTorch for dynamic neural network programming. Finally, you will build two mini-projects, first focusing on applying dynamic neural networks to image recognition and second NLP-oriented problems (grammar parsing).By the end of this course, you will have a complete understanding of the essential ML libraries TensorFlow and PyTorch for developing and training neural networks of varying complexities, without any hassle.Meet Your Expert(s):We have the best work of the following esteemed author(s) to ensure that your learning journey is smooth:Roland Meertens is currently developing computer vision algorithms for self-driving cars. Previously he has worked as a research engineer at a translation department. Examples of things he has made are a Neural Machine Translation implementation, a post-editor, and a tool that estimates the quality of a translated sentence. Last year, he worked at the Micro Aerial Vehicle Laboratory at the university of Delft, on indoor localization (SLAM) and obstacle avoidance behaviors for a drone that delivers food inside a restaurant. Another thing he worked on was detecting and following people using onboard computer vision algorithms on a stereo camera. For his Master's thesis, he did an internship at a company called SpirOps, where he worked on the development of a dialogue manager for project Romeo. In his Artificial Intelligence study, he specialized in cognitive artificial intelligence and brain-computer interfacing.Harveen Singh Chadha is an experienced researcher in Deep Learning and is currently working as a Self Driving Car Engineer. He is currently focused on creating an ADAS (Advanced Driver Assistance Systems) platform. His passion is to help people who currently want to enter into the Data Science Universe.Anastasia Yanina is a Senior Data Scientist with around 5 years of experience. She is an expert in Deep Learning and Natural Language processing and constantly develops her skills as far as possible. She is passionate about human-to-machine interactions. She believes that bridging the gap may become possible with deep neural network architectures.
