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所在平台: Udemy |
课程主页: https://www.udemy.com/course/deep-learning-image-classification-with-tensorflow-in-2023/
课程评论：没有评论
**课程名称：** 深度学习：Tensorflow 图像分类 (2025) **课程概述：** 本课程将带您踏上一段精彩的旅程，深入学习图像分类的概念和实践。从2010年代初高效深度学习模型的出现，图像分类技术取得了显著进步，并在农业、医疗、工业、教育和交通等各个领域得到了广泛应用。 **您将学习到：** * **Tensorflow 基础：** 掌握 Tensors、模型构建、训练和评估等核心概念。 * **深度学习算法：** 深入理解卷积神经网络 (CNN) 和 Vision Transformers。 * **分类模型评估：** 熟悉 Precision, Recall, Accuracy, F1-score, Confusion Matrix, ROC Curve 等评估指标。 * **缓解过拟及技术：** 学习数据增强等方法来提高模型泛化能力。 * **高级 Tensorflow 技术：** 探索自定义损失函数和指标、Eager/Graph 模式、自定义训练循环以及 Tensorboard 可视化工具。 * **机器学习运维 (MLOps)：** 使用 Weights & Biases 进行实验跟踪、超参数调优、数据集及模型版本管理。 * **实际应用案例：** 通过疟疾检测（二分类）和人类情感检测（多分类）等实际项目来巩固所学。 * **迁移学习：** 利用 VGGNet, ResNet, MobileNet, EfficientNet 等现代卷积神经网络模型进行迁移学习。 * **模型部署：** 学习使用 ONNX 格式、量化技术，以及 FastAPI 和 Heroku Cloud 等工具进行模型部署。 **课程特色：** * **技术栈：** 使用 Tensorflow 2 (Google 出品的深度学习库) 和 Huggingface。 * **教学方式：** 循序渐进，概念清晰。 * **实践导向：** 结合实际案例，注重部署和 MLOps。 * **互动反馈：** 课程由 Neuralearn 提供，非常重视学员的反馈和提问，并及时回复。 **适合人群：** 希望在职业生涯中迈出下一步，精通图像分类技术的学习者。
Image classification models find themselves in different places today, like farms, hospitals, industries, schools, and highways,.With the creation of much more efficient deep learning models from the early 2010s, we have seen a great improvement in the state of the art in the domain of image classification.In this course, we shall take you on an amazing journey in which you'll master different concepts with a step-by-step approach. We shall start by understanding how image classification algorithms work, and deploying them to the cloud while observing best practices. We are going to be using Tensorflow 2 (the world's most popular library for deep learning, built by Google) and HuggingfaceYou 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)Model Deployment (Onnx format, Quantization, Fastapi, Heroku Cloud)If 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!!!
