卷积神经网络课程
卷积神经网络课程可以帮助您学习图像分类、Object Detection 和 Feature Extraction 技术。您可以掌握优化神经网络 Architecture、实施数据 Augmentation 策略以及针对特定任务对模型进行微调的技能。许多课程介绍了 TensorFlow 和 Keras 等工具，这些工具支持构建和训练 CNN，以及评估模型性能和部署计算机视觉和 AI 驱动解决方案等领域应用的方法。
热门卷积神经网络课程和认证
状态：免费试用您将获得的技能: Model Evaluation, Computer Vision, Convolutional Neural Networks, Image Analysis, Transfer Learning, Matlab, Artificial Neural Networks, Deep Learning, Applied Machine Learning, Classification Algorithms, Performance Tuning
状态：免费试用您将获得的技能: Generative Adversarial Networks (GANs), Exploratory Data Analysis, Model Deployment, Keras (Neural Network Library), NumPy, Transfer Learning, PyTorch (Machine Learning Library), Predictive Modeling, Matplotlib, Data Analysis, Artificial Intelligence, Data Preprocessing, Development Environment, Pandas (Python Package), Deep Learning, Classification And Regression Tree (CART), Artificial Neural Networks, Image Analysis, Machine Learning, Data Science
状态：免费试用您将获得的技能: Python 程序设计, 卷积神经网络, PyTorch（机器学习库）, 模型评估, 模型部署, 视觉转换器（ViT）, 迁移学习, 数据管道, 计算机视觉, Keras（神经网络库）, 数据预处理, 深度学习, 地理空间信息与技术, 机器学习
状态：免费试用您将获得的技能: Transfer Learning, Keras (Neural Network Library), Embeddings, Deep Learning, Image Analysis, Computer Vision, Natural Language Processing, Data Preprocessing
状态：免费试用您将获得的技能: Model Deployment, PyTorch (Machine Learning Library), Recurrent Neural Networks (RNNs), Tensorflow, Artificial Intelligence, Applied Machine Learning, Artificial Neural Networks, Artificial Intelligence and Machine Learning (AI/ML), Machine Learning Methods, Text Mining, Machine Learning, Natural Language Processing, Deep Learning, Predictive Modeling, Classification Algorithms, Supervised Learning, Time Series Analysis and Forecasting, Network Architecture, Data Science, Model Evaluation
状态：预览Edge Impulse
您将获得的技能: 卷积神经网络, Python 程序设计, 模型部署, 数据伦理, 人工智能和机器学习（AI/ML）, 嵌入式系统, 模型评估, 计算机视觉, 迁移学习, 应用机器学习, 计算机编程, 图像分析, 深度学习, 数据预处理, 人工神经网络, 机器学习
状态：新状态：免费试用Pearson
您将获得的技能: Large Language Modeling, Deep Learning, Prompt Engineering, Image Analysis, Model Deployment, Recurrent Neural Networks (RNNs), PyTorch (Machine Learning Library), Convolutional Neural Networks, Tensorflow, Vision Transformer (ViT), LLM Application, Transfer Learning, Computer Vision, Responsible AI, Natural Language Processing, Embeddings, Keras (Neural Network Library), Generative AI, Artificial Neural Networks, Multimodal Prompts
状态：免费试用DeepLearning.AI
您将获得的技能: 卷积神经网络, 模型评估, 分类算法, Keras（神经网络库）, 迁移学习, 计算机视觉, 网络架构, 深度学习, 图像分析, 可视化（计算机制图）, 张力流
状态：免费试用DeepLearning.AI
您将获得的技能: Python 程序设计, 卷积神经网络, 监督学习, 应用机器学习, 线性代数, 深度学习, 人工神经网络, 微积分, 递归神经网络 (RNN)
状态：免费试用您将获得的技能: Recurrent Neural Networks (RNNs), Model Deployment, Keras (Neural Network Library), Deep Learning, Tensorflow, Artificial Neural Networks, Convolutional Neural Networks, Application Frameworks, Artificial Intelligence, Machine Learning Methods, Image Analysis, Data Preprocessing
状态：免费试用DeepLearning.AI
您将获得的技能: 卷积神经网络, 模型评估, 分类算法, 计算机视觉, Keras（神经网络库）, 深度学习, 应用机器学习, 迁移学习, 张力流, 数据预处理, 图像分析, 机器学习
状态：免费试用Johns Hopkins University
您将获得的技能: Convolutional Neural Networks, Artificial Neural Networks, Deep Learning, Machine Learning Algorithms, Image Analysis, Machine Learning, Computer Vision, Model Evaluation, Supervised Learning, Algorithms, Statistical Methods, Linear Algebra, Probability
总之，以下是 10 最受欢迎的 convolutional neural network 课程
- Introduction to Deep Learning for Computer Vision: MathWorks
- Keras Deep Learning & Generative Adversarial Networks (GAN): Packt
- 使用深度学习的人工智能顶点项目: IBM
- Deep Learning: Convolutional Neural Networks with TensorFlow: Packt
- Deep Learning: Recurrent Neural Networks with Python: Packt
- 计算机视觉与嵌入式机器学习: Edge Impulse
- Learning Deep Learning: Pearson
- 利用 TensorFlow 实现高级计算机视觉: DeepLearning.AI
- 神经网络与深度学习: DeepLearning.AI
- Deep Learning Frameworks and Neural Networks Simplified: Simplilearn
