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：新状态：预览您将获得的技能: Plot (Graphics), Data Visualization Software, R Programming, Scatter Plots, Regression Analysis, Statistical Modeling, Predictive Modeling, Artificial Neural Networks, Data Science, Deep Learning, Model Evaluation, Descriptive Statistics, Predictive Analytics, Statistical Methods, Data Manipulation, Data Preprocessing, Performance Testing
状态：免费试用您将获得的技能: Recurrent Neural Networks (RNNs), PyTorch (Machine Learning Library), Text Mining, Artificial Neural Networks, Natural Language Processing, Classification Algorithms, Deep Learning, Applied Machine Learning, Network Architecture, Machine Learning
状态：免费试用Universidades Anáhuac
您将获得的技能: Artificial Neural Networks, Keras (Neural Network Library), Deep Learning, Applied Machine Learning, Tensorflow, Convolutional Neural Networks, Recurrent Neural Networks (RNNs), Image Analysis, Computer Vision, Model Evaluation, Python Programming
状态：预览Universidad de los Andes
您将获得的技能: Recurrent Neural Networks (RNNs), Deep Learning, Convolutional Neural Networks, Generative Model Architectures, Generative Adversarial Networks (GANs), Transfer Learning, Vision Transformer (ViT), Image Analysis, Artificial Neural Networks, Machine Learning Methods, Computer Vision, Network Architecture, Natural Language Processing
您将获得的技能: PyTorch (Machine Learning Library), Computer Vision, NumPy, Matplotlib, Convolutional Neural Networks, Deep Learning, Pandas (Python Package), Image Analysis, Python Programming, Artificial Neural Networks, Data Manipulation
状态：免费试用您将获得的技能: Model Deployment, PyTorch (Machine Learning Library), Recurrent Neural Networks (RNNs), Artificial Intelligence, Applied Machine Learning, Artificial Neural Networks, Deep Learning, Artificial Intelligence and Machine Learning (AI/ML), Machine Learning Methods, Machine Learning, Natural Language Processing, Supervised Learning, Network Architecture, Data Science
状态：免费试用您将获得的技能: Recurrent Neural Networks (RNNs), Tensorflow, Deep Learning, Predictive Modeling, Time Series Analysis and Forecasting, Natural Language Processing, Model Evaluation, Data Preprocessing
状态：免费试用您将获得的技能: Recurrent Neural Networks (RNNs), Convolutional Neural Networks, Artificial Intelligence, Applied Machine Learning, Python Programming, Model Evaluation
状态：新状态：免费试用您将获得的技能: Recurrent Neural Networks (RNNs), PyTorch (Machine Learning Library), Convolutional Neural Networks, Tensorflow, Transfer Learning, Natural Language Processing, Embeddings, Image Analysis, Deep Learning, Computer Vision, Time Series Analysis and Forecasting
状态：预览Yonsei University
您将获得的技能: 卷积神经网络, 机器学习, 深度学习, 企业战略, 人工神经网络, 技术战略, 无监督学习, 强化学习, 自然语言处理, 张力流, 人工智能和机器学习（AI/ML）, 人工智能, 递归神经网络 (RNN)
状态：免费试用Illinois Tech
您将获得的技能: Recurrent Neural Networks (RNNs), Deep Learning, Generative AI, Convolutional Neural Networks, Transfer Learning, Image Analysis, Artificial Neural Networks, Keras (Neural Network Library), Generative Adversarial Networks (GANs), Artificial Intelligence and Machine Learning (AI/ML), PyTorch (Machine Learning Library), Network Architecture, Computer Vision, Natural Language Processing, Model Deployment, Model Evaluation
状态：免费试用Johns Hopkins University
您将获得的技能: Autoencoders, Recurrent Neural Networks (RNNs), Deep Learning, Artificial Neural Networks, Reinforcement Learning, Generative AI, Generative Adversarial Networks (GANs), Unsupervised Learning, Machine Learning Methods, Artificial Intelligence and Machine Learning (AI/ML), Data Ethics, Markov Model
总之，以下是 10 最受欢迎的 convolutional neural network 课程
- Deep Learning with R: Build & Predict Neural Networks: EDUCBA
- RNN Architecture and Sentiment Classification: Packt
- Deep Learning: Del Concepto a la Práctica: Universidades Anáhuac
- Introducción al deep learning contemporáneo: Universidad de los Andes
- Deep Learning - Computer Vision for Beginners Using PyTorch: Packt
- Introduction to RNN and DNN: Packt
- Advanced RNN Concepts and Projects: Packt
- Practical Deep Learning with Python: Edureka
- Learning Deep Learning: Unit 2: Pearson
- 商业深度学习: Yonsei University
