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：免费试用DeepLearning.AI
您将获得的技能: 卷积神经网络, 监督学习, 深度学习, 人工神经网络, 应用机器学习, Python 程序设计, 微积分, 递归神经网络 (RNN), 线性代数
状态：免费试用您将获得的技能: 机器学习, 网络架构, 卷积神经网络, 回归分析, 深度学习, Model Evaluation, 分类与回归树 (CART), 人工神经网络, Transfer Learning, 自然语言处理, 自动编码器, Keras（神经网络库）, 递归神经网络 (RNN), 应用机器学习, 图像分析
状态：新状态：预览您将获得的技能: Exploratory Data Analysis, Feature Engineering, Data Analysis, Data Preprocessing, Model Evaluation, Data Cleansing, Data Transformation, Predictive Modeling, Pandas (Python Package), Applied Machine Learning, Statistical Methods, Regression Analysis, Scikit Learn (Machine Learning Library), Artificial Neural Networks
状态：免费试用DeepLearning.AI
您将获得的技能: 机器学习, 卷积神经网络, 拥抱的脸, PyTorch（机器学习库）, 数据预处理, 调试, 监督学习, 张力流, 深度学习, 人工神经网络, Transfer Learning, MLOps（机器学习 Operator）, 计算机视觉, 性能调整, 自然语言处理, 应用机器学习, 嵌入, Keras（神经网络库）, 递归神经网络 (RNN), 图像分析
状态：免费试用您将获得的技能: Model Evaluation, PyTorch（机器学习库）, 张力流, 数据预处理, 逻辑回归, 回归分析, 概率与统计, 人工神经网络, 统计方法
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DeepLearning.AI
您将获得的技能: 卷积神经网络, 数据预处理, 张力流, PyTorch（机器学习库）, 深度学习, 计算机视觉, 人工神经网络, Transfer Learning, Keras（神经网络库）, 图像分析
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：新状态：免费试用您将获得的技能: Applied Machine Learning, Analysis, Performance Analysis
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: 验证和确认, 数据预处理, 张力流, Machine Learning 方法, Model Evaluation, 深度学习, 人工神经网络, 性能调整
总之，以下是 10 最受欢迎的 neural networks 课程
- 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
- Apply Neural Networks for Car Price Prediction: EDUCBA
- 深度学习: DeepLearning.AI
- 神经网络和 PyTorch 简介: IBM
- Foundations of Neural Networks: Johns Hopkins University
- 卷积神经网络: DeepLearning.AI
- Practical Machine Learning: Foundations to Neural Networks: Dartmouth College
- Debug Neural Networks: Analyze Training Dynamics: Coursera
- Introduction to Neural Networks: Johns Hopkins University
