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所在平台: Udemy |
课程主页: https://www.udemy.com/course/deep-learning-with-caffe-2-hands-on/
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课程名称：《深度学习与Caffe 2 - 实战演练！》 课程概述： Caffe 2 是一个开源的深度学习框架，经过重构以提供更大的计算灵活性。它是一个轻量级和模块化的框架，为云和移动应用进行了优化。该框架在移动设备和低功耗设备上提升了深度学习的能力，能够构建、训练和评估模型，并支持Android、iOS设备和Raspberry Pi板的编程。如果您希望开发自己的定制神经网络和深度学习模型并能高效地进行部署，那么本课程非常适合您。 本课程将教您如何使用Caffe 2创建、训练和部署您的神经网络和深度学习模型。课程开始时介绍Caffe 2的基本概念，如blobs、工作空间、操作符和网络，接着构建神经网络，深入理解卷积神经网络、循环神经网络、Adam优化器、Dropout、BatchNorm等。最后，学习如何在移动设备上部署模型。 内容概览： 该培训项目包含两个完整的课程，旨在为您提供最全面的训练。第一个课程《Caffe 2实战深度学习》将从Caffe 2的基础知识入手，如blobs、工作空间、操作符和网络。随后，您将学习如何利用Caffe 2的新API brew构建模型，以及创建可以识别手写文字和时尚物品的卷积神经网络（CNN）。接下来，您将学习迁移学习，使用在大规模数据集上预训练的模型进行图像识别的微调。最后，您将学习如何在任何平台上部署您的模型。 第二个课程《Caffe 2深度学习导论》将帮助您学习深度学习的基础，理解如何构建神经网络，并深入理解卷积网络、RNN、Adam、Dropout、BatchNorm等。整个课程中您将完成多个项目，着重于如何有效地训练和操作深度神经网络。 通过本课程的学习，您将能够有效地创建和训练深度学习模型，提供对大规模分布式训练、移动部署、新硬件支持和灵活性的优质支持。 专家介绍： 本课程由以下知名作者团队授课，确保您在学习过程中享受优质的体验： - 郑帅（Kyle Shuai）：牛津大学计算机视觉与机器学习博士，参与多个顶级会议，研究深度学习在计算机视觉中的应用。 - Abhishek Kumar Annamraju：Tessellate Imaging的CTO和联合创始人，研究领域涵盖计算机视觉、机器学习和自然语言处理等。 - Akash Deep Singh：Tessellate Imaging的COO和联合创始人，对人工智能和机器视觉充满热情，拥有丰富的实时目标检测和跟踪系统开发经验。 课程将为您提供深入的技术了解和实用的技能，激发您在深度学习领域的探索与应用。
Caffe 2 is an open-sourced Deep Learning framework, refactored to provide further flexibility in computation. It is a light-weighted and modular framework, and is being optimized for cloud and mobile applications. It boosts Deep Learning on mobile and low-power devices by building, training, and evaluating the models and enables programming for Android and iOS devices, and Raspberry Pi boards.If you want to develop your own customised neural networks and deep learning models which can also be deployed efficiently, then take up this course.This course teaches you to create, train, and deploy your neural networks and deep learning models using Caffe 2. You will begin with an introduction to Caffe 2 and learn the basic concepts of Caffe 2 such as blobs, workspaces, operators, and nets. You will then build neural networks and develop an understanding of convolutional neural networks, RNNs, Adam, Dropout, BatchNorm, and more. You will also learn how train and manipulate deep neural networks effectively. Finally, you will learn how to deploy your models on mobile devices.Contents and OverviewThis training program includes 2 complete courses, carefully chosen to give you the most comprehensive training possible.The first course, Hands-On Deep Learning with Caffe2, starts off with the basics of Caffe2 such as blobs, workspaces, operators, and nets. You will then learn how to build a model using Caffe 2's new API brew. You will also learn how to create Convolutional Neural Networks (CNNs) that can identify not only handwriting but also fashion items from an image. Next, you will work on transferring learning to allow you to work with CNN's for image recognition by fine-tuning models that are already pre-trained on a large-scale dataset. Finally, you will learn how to deploy your models on any platform.In the second course, Introduction to Deep Learning with Caffe2, you will learn the foundations of deep learning, understand how to build neural networks and develop an understanding of convolutional networks, RNNs, Adam, Dropout, BatchNorm and more. You will work on various projects throughout this MOOC with a focus on how to train and manipulate a deep neural network effectively.By the end of this course, you will be able to effectively create and train deep learning models with Caffe2, providing you with high-performance and first-class support for large-scale distributed training, mobile deployment, new hardware support, and flexibility.Meet Your Expert(s):We have the best work of the following esteemed author(s) to ensure that your learning journey is smooth:Shuai Zheng, also known as Kyle, did his Ph.D. degree in Machine Learning and Computer Vision at the University of Oxford. He has published in top-tier machine learning and computer vision conferences such as CVPR, ECCV, and ICCV. His research interests are in deep learning and its applications in computer vision such as semantic segmentation. He is currently a research scientist at eBay Inc, where he works on both fundamental and practical problems in Augmented Reality, Computer Vision, and Deep Learning.Abhishek Kumar Annamraju, is the CTO and co-founder at Tessellate Imaging. His research areas include computer vision, machine learning, NLP and photogrammetry. As a part of his undergraduate thesis and then continued employment at Tata Elxsi, India, he built and later lead the machine learning and sensor analytics team. He has research papers on cascade classifiers and shape based object analysis, and a research on traffic sign classifier with accuracies reaching upto 99% as per GTSRB stats is one of the state of art solutions available. He participated in the Google Summer of Code (GSoC), 2016, program, working with Open-Detection, to develop a deep learning oriented vision based classifier and an end-to-end GUI based classifier training module. His past projects include image based monitoring solution to curb illegal sand mining, on-road real-time vehicle detection, 3D facial model generation and classification, deep learning based face recognition, and camera auto-calibration for fisheye images (Tesseract Imaging, India). He was also a part of Mahindra rise challenge, 2014, to develop real-time stationary-cam object detection modules. His research work includes projects involving forensic sketch to image matching and biomedical image processing.Akash Deep Singh, is the COO and co-founder at Tessellate Imaging and is passionate about combining Artificial Intelligence and Machine Vision. Prior to Tessellate Imaging, he worked on building solutions ranging from novel systems to detect and classify glioma cancer to a real-time stat generation camera solution for basketball players. He was also part of the team which built India's first panoramic camera where he acted as the Machine Learning lead. He has a vast experience in building real-time object detection and tracking systems. His past projects include autopilot firmware for Search and Rescue drones, building Disguised and Imposter face recognition software, an all-terrain navigation vehicle and sketch to face image matching for forensics. A national cyber olympiad gold medalist, he loves reading books.
