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所在平台: Udemy |
课程主页: https://www.udemy.com/course/master-deep-learning-for-computer-vision-with-tensorflow-2/
课程评论：没有评论
课程名称：深度学习与计算机视觉在TensorFlow中的精通[2025] 概述：深度学习是当今的热门话题，因其在多个行业中的重大影响，尤其是在计算机视觉领域。深度学习为图像检测、图像分割、图像分类、图像生成和人员计数等应用提供了强大的支持。随着计算机通过摄像头理解周围环境的能力不断提升，深度学习在医疗诊断、农业、自动驾驶汽车、智能监控系统、艺术创作与体育数据分析等多个领域的应用改变了我们的生活。 计算机视觉工程师的需求急剧上升，专家在该领域的薪资也非常高，然而，对于初学者来说，入门并不容易。本课程采取循序渐进和项目驱动的方法，带领学员掌握各种概念。课程内容将基于TensorFlow 2（谷歌开发的深度学习库）和Huggingface，开始学习简单的模型构建，如线性回归和二分类器，然后扩展到更高级的模型，例如YOLO进行物体检测和GANs进行图像生成。 学习内容包括： - TensorFlow基础（张量、模型构建、训练和评价） - 深度学习算法（卷积神经网络和视觉变压器） - 分类模型评价（精确率、召回率、准确率、F1分数、混淆矩阵、ROC曲线） - 过拟合的缓解技巧（数据增强） - 高级TensorFlow概念（自定义损失和指标、执行模式与图模式、自定义训练循环、Tensorboard） - 机器学习操作（MLOps）与Weights and Biases（实验跟踪、超参数调优、数据集版本控制、模型版本控制） - 恶性疟疾检测的二分类 - 人类情感识别的多分类 - 使用现代卷积网络（如Vggnet、Resnet、Mobilenet、Efficientnet）和视觉变压器（VIT）进行迁移学习 - 使用YOLO进行物体检测 - 使用UNet进行图像分割 - 使用Csrnet模型进行人员计数 - 模型部署（蒸馏、ONNX格式、量化、Fastapi、Heroku云） - 使用变分自编码器生成数字 - 使用生成对抗网络生成面孔 本课程旨在帮助有意在职业生涯中更进一步的人士，我们非常期待能够帮助您实现目标！课程由Neuralearn提供，强调反馈，欢迎在论坛中提出问题与建议，我们将尽快回复。尽情享受学习吧！
Deep Learning is a hot topic today! This is because of the impact it's having in several industries. One of fields in which deep learning has the most influence today is Computer Vision.Object detection, Image Segmentation, Image Classification, Image Generation & People Counting To understand why Deep Learning based Computer Vision is so popular; it suffices to take a look at the different domains where giving a computer the power to understand its surroundings via a camera has changed our lives.Some applications of Computer Vision are:Helping doctors more efficiently carry out medical diagnosticsenabling farmers to harvest their products with robots, with the need for very little human intervention,Enable self-driving carsHelping quick response surveillance with smart CCTV systems, as the cameras now have an eye and a brainCreation of art with GANs, VAEs, and Diffusion ModelsData analytics in sports, where players' movements are monitored automatically using sophisticated computer vision algorithms.The demand for Computer Vision engineers is skyrocketing and experts in this field are highly paid, because of their value. However, getting started in this field isn't easy. There's so much information out there, much of which is outdated and many times don't take the beginners into consideration:(In this course, we shall take you on an amazing journey in which you'll master different concepts with a step-by-step and project-based approach. You shall be using Tensorflow 2 (the world's most popular library for deep learning, built by Google) and Huggingface. We shall start by understanding how to build very simple models (like Linear regression model for car price prediction and binary classifier for malaria prediction) using Tensorflow to much more advanced models (like object detection model with YOLO and Image generation with GANs). After going through this course and carrying out the different projects, you will develop the skill sets needed to develop modern deep learning for computer vision solutions that big tech companies encounter.You will learn: The Basics of TensorFlow (Tensors, Model building, training, and evaluation)Deep Learning algorithms like Convolutional neural networks and Vision TransformersEvaluation of Classification Models (Precision, Recall, Accuracy, F1-score, Confusion Matrix, ROC Curve)Mitigating overfitting with Data augmentationAdvanced Tensorflow concepts like Custom Losses and Metrics, Eager and Graph Modes and Custom Training Loops, TensorboardMachine Learning Operations (MLOps) with Weights and Biases (Experiment Tracking, Hyperparameter Tuning, Dataset Versioning, Model Versioning)Binary Classification with Malaria detection Multi-class Classification with Human Emotions DetectionTransfer learning with modern Convnets (Vggnet, Resnet, Mobilenet, Efficientnet) and Vision Transformers (VITs)Object Detection with YOLO (You Only Look Once)Image Segmentation with UNetPeople Counting with Csrnet Model Deployment (Distillation, Onnx format, Quantization, Fastapi, Heroku Cloud)Digit generation with Variational AutoencodersFace generation with Generative Adversarial Neural NetworksIf you are willing to move a step further in your career, this course is destined for you and we are super excited to help achieve your goals!This course is offered to you by Neuralearn. And just like every other course by Neuralearn, we lay much emphasis on feedback. Your reviews and questions in the forum will help us better this course. Feel free to ask as many questions as possible on the forum. We do our very best to reply in the shortest possible time.Enjoy!!!
