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DeepLearning.AI
您将获得的技能: 卷积神经网络, 计算机视觉, Keras（神经网络库）, 数据预处理, 人工神经网络, 张力流, Transfer Learning, PyTorch（机器学习库）, 图像分析, 深度学习
状态：免费试用Johns Hopkins University
您将获得的技能: Responsible AI, Autoencoders, Convolutional Neural Networks, Recurrent Neural Networks (RNNs), Data Ethics, Deep Learning, Artificial Neural Networks, Reinforcement Learning, Generative AI, Generative Adversarial Networks (GANs), Machine Learning Algorithms, Model Deployment, Debugging, Artificial Intelligence, Image Analysis, Unsupervised Learning, Machine Learning Methods, Artificial Intelligence and Machine Learning (AI/ML), Machine Learning, Computer Vision
状态：免费试用DeepLearning.AI
您将获得的技能: 计算机视觉, 卷积神经网络, MLOps（机器学习 Operator）, 嵌入, 监督学习, 调试, 应用机器学习, Keras（神经网络库）, 递归神经网络 (RNN), 机器学习, Transfer Learning, 人工神经网络, 数据预处理, 性能调整, 拥抱的脸, 自然语言处理, 张力流, PyTorch（机器学习库）, 图像分析, 深度学习
状态：免费试用您将获得的技能: 卷积神经网络, 监督学习, 深度学习, Model Evaluation, 人工神经网络, 应用机器学习, PyTorch（机器学习库）, 分类算法
状态：免费试用您将获得的技能: 卷积神经网络, 自动编码器, Keras（神经网络库）, 递归神经网络 (RNN), 回归分析, Model Evaluation, 分类与回归树 (CART), Transfer Learning, 人工神经网络, 自然语言处理, 机器学习, 应用机器学习, 网络架构, 图像分析, 深度学习
Google Cloud
您将获得的技能: 卷积神经网络, 计算机视觉, Google 云端平台, 云计算, 数据收集, 深度学习, Model Evaluation, 人工神经网络, 张力流, 图像分析, 科学可视化
是什么让您今天来到 Coursera？
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
您将获得的技能: Tensorflow, Convolutional Neural Networks, Image Analysis, Python Programming, Jupyter, Artificial Neural Networks, Deep Learning, Software Visualization, Machine Learning
状态：新状态：免费试用您将获得的技能: Model Deployment, Transfer Learning, Keras (Neural Network Library), Deep Learning, Applied Machine Learning, Tensorflow, PyTorch (Machine Learning Library), Convolutional Neural Networks, Artificial Neural Networks, Machine Learning Methods, Application Frameworks, Model Evaluation, Performance Tuning
状态：新状态：免费试用您将获得的技能: Convolutional Neural Networks, Model Evaluation, Keras (Neural Network Library), Image Analysis, Deep Learning, Applied Machine Learning, Computer Vision, Dimensionality Reduction, PyTorch (Machine Learning Library), Tensorflow, Machine Learning Methods, Data Preprocessing, Performance Tuning, Artificial Intelligence
状态：新状态：预览Whizlabs
您将获得的技能: Deep Learning, Convolutional Neural Networks, Image Analysis, Transfer Learning, Artificial Neural Networks, Model Evaluation, Computer Vision, Artificial Intelligence and Machine Learning (AI/ML), Machine Learning Methods
状态：新状态：免费试用您将获得的技能: Recurrent Neural Networks (RNNs), Convolutional Neural Networks, Transfer Learning, Natural Language Processing, Deep Learning, PyTorch (Machine Learning Library), Large Language Modeling, Keras (Neural Network Library), Artificial Neural Networks, Image Analysis, Tensorflow, Artificial Intelligence, Machine Learning Methods, Computer Vision, Embeddings, Data Preprocessing, Model Evaluation
总之，以下是 10 最受欢迎的 convolutional neural network 课程
- 卷积神经网络: DeepLearning.AI
- Foundations of Neural Networks: Johns Hopkins University
- 深度学习: DeepLearning.AI
- 使用 PyTorch 进行深度学习: IBM
- 使用 Keras 的深度学习和神经网络简介: IBM
- 用 TensorFlow 卷积神经网络对图像进行分类: Google Cloud
- Introduction to Neural Networks: Johns Hopkins University
- Visualizing Filters of a CNN using TensorFlow: Coursera
- Deep Learning and Advanced Techniques: Packt
- Foundations of Model Optimization and Deep Learning: Packt
