神经网络课程
神经网络课程可以帮助您学习架构设计、反向传播、激活函数和优化技术的基础知识。您可以掌握训练模型、调整超参数和评估性能指标的技能。许多课程介绍了 TensorFlow 和 PyTorch 等工具，这些工具支持在图像识别、自然语言处理和预测分析等实际应用中实现神经网络。
热门神经网络课程与认证
状态：新状态：免费试用Dartmouth College
您将获得的技能: Supervised Learning, Bayesian Network, Logistic Regression, Artificial Neural Networks, Machine Learning Methods, Statistical Modeling, Predictive Modeling, Model Evaluation, Statistical Machine Learning, Probability & Statistics, Bayesian Statistics, Deep Learning, Artificial Intelligence and Machine Learning (AI/ML), Machine Learning, Machine Learning Algorithms, Statistical Methods, Artificial Intelligence, Regression Analysis, Classification Algorithms, Statistical Inference
状态：免费试用DeepLearning.AI
您将获得的技能: 微积分, 卷积神经网络, 监督学习, 人工神经网络, 递归神经网络 (RNN), Python 程序设计, 线性代数, 深度学习, 应用机器学习
状态：免费试用您将获得的技能: 回归分析, 模型评估, 分类与回归树 (CART), 自然语言处理, 卷积神经网络, Keras（神经网络库）, 迁移学习, 机器学习, 人工神经网络, 自动编码器, 深度学习, 图像分析, 递归神经网络 (RNN), 应用机器学习, 网络架构
状态：免费试用您将获得的技能: 概率与统计, 回归分析, 模型评估, 预测建模, PyTorch（机器学习库）, 逻辑回归, Machine Learning 方法, 人工神经网络, 分类算法, 张力流, 数据预处理, 深度学习
状态：免费试用DeepLearning.AI
您将获得的技能: 迁移学习, PyTorch（机器学习库）, 机器学习, 调试, 卷积神经网络, 自然语言处理, 监督学习, 拥抱的脸, MLOps（机器学习 Operator）, Keras（神经网络库）, 递归神经网络 (RNN), 计算机视觉, 深度学习, 数据预处理, 性能调整, 嵌入, 图像分析, 人工神经网络, 应用机器学习, 张力流
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
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
状态：免费试用DeepLearning.AI
您将获得的技能: PyTorch（机器学习库）, 卷积神经网络, 迁移学习, Keras（神经网络库）, 人工神经网络, 张力流, 计算机视觉, 深度学习, 数据预处理, 图像分析
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：免费试用DeepLearning.AI
您将获得的技能: 模型评估, Machine Learning 方法, 人工神经网络, 验证和确认, 张力流, 性能调整, 深度学习, 数据预处理
状态：免费试用您将获得的技能: 卷积神经网络, PyTorch（机器学习库）, 模型评估, Machine Learning 方法, 逻辑回归, 监督学习, 分类算法, 人工神经网络, 深度学习, 应用机器学习, 人工智能和机器学习（AI/ML）
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
是什么让您今天来到 Coursera？
总之，以下是 10 最受欢迎的 neural networks 课程
- Practical Machine Learning: Foundations to Neural Networks: Dartmouth College
- 神经网络与深度学习: DeepLearning.AI
- 使用 Keras 的深度学习和神经网络简介: IBM
- 神经网络和 PyTorch 简介: IBM
- 深度学习: DeepLearning.AI
- Foundations of Neural Networks: Johns Hopkins University
- Introduction to Neural Networks: Johns Hopkins University
- 卷积神经网络: DeepLearning.AI
- Deep Learning: Recurrent Neural Networks with Python: Packt
- 改进深度神经网络：超参数调整、正则化和优化: DeepLearning.AI
