个结果，搜索词为 "recurrent neural networks (rnns)"
状态：新状态：免费试用您将获得的技能: Model Evaluation, Convolutional Neural Networks, Data Preprocessing, Keras (Neural Network Library), Image Analysis, Deep Learning, Tensorflow, Computer Vision, Artificial Neural Networks, Recurrent Neural Networks (RNNs), Data Manipulation, Data Transformation, Financial Forecasting, Data Visualization, Time Series Analysis and Forecasting, Exploratory Data Analysis, Python Programming, Customer Analysis, Applied Machine Learning, Cloud Computing
状态：免费试用您将获得的技能: 分类与回归树 (CART), 自然语言处理, Model Evaluation, 自动编码器, 机器学习, 卷积神经网络, Keras（神经网络库）, 递归神经网络 (RNN), 人工神经网络, 应用机器学习, 图像分析, 回归分析, 深度学习, Transfer Learning, 网络架构
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：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: 生成式人工智能, 信息隐私, 机器学习, Model Evaluation, 生成对抗网络 (GAN), 数据综合, 数据伦理, 卷积神经网络, 负责任的人工智能, 人工神经网络, 生成模型架构, 无监督学习, 图像质量, 深度学习, 图像分析, PyTorch（机器学习库）
状态：免费试用DeepLearning.AI
您将获得的技能: Algorithm, 数据预处理, Machine Learning 方法, 嵌入, 文本挖掘, 自然语言处理, 马尔可夫模型, Keras（神经网络库）, 递归神经网络 (RNN), 降维, 统计机器学习, 人工神经网络, PyTorch（机器学习库）, 张力流, 功能工程, 应用机器学习, 监督学习, 分类算法, 深度学习, Transfer Learning
是什么让您今天来到 Coursera？
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：新状态：免费试用您将获得的技能: Network Architecture, Artificial Neural Networks, Network Planning And Design, Performance Testing, Deep Learning
状态：免费试用您将获得的技能: 数据预处理, Model Evaluation, 统计方法, 逻辑回归, 人工神经网络, 回归分析, 概率与统计, 张力流, PyTorch（机器学习库）
状态：预览University of Washington
您将获得的技能: 生物学, 线性代数, 统计方法, 信息架构, 网络模型, 递归神经网络 (RNN), 机器学习算法, 生理学, 计算思维, 数学建模, 人工神经网络, 计算机科学, 微分方程, 神经学, 强化学习, Matlab, 监督学习, 概率分布, 生物信息学
状态：免费试用DeepLearning.AI
您将获得的技能: 线性代数, Python 程序设计, 卷积神经网络, 应用机器学习, 递归神经网络 (RNN), 人工神经网络, 微积分, 监督学习, 深度学习
状态：新状态：免费试用您将获得的技能: Recurrent Neural Networks (RNNs), Transfer Learning, Tensorflow, Artificial Neural Networks, Embeddings, Keras (Neural Network Library), Deep Learning, Time Series Analysis and Forecasting, Image Analysis, Classification Algorithms, Convolutional Neural Networks, Natural Language Processing, Computer Vision, Forecasting, Supervised Learning, Machine Learning Algorithms, Machine Learning, Predictive Analytics, Model Evaluation, Predictive Modeling
是什么让您今天来到 Coursera？
总之，以下是 10 最受欢迎的 recurrent neural networks (rnns) 课程
- Deep Learning with Python: CNN, ANN & RNN: EDUCBA
- 使用 Keras 的深度学习和神经网络简介: IBM
- Fundamentals of Deep Learning: Whizlabs
- Foundations of Neural Networks: Johns Hopkins University
- 生成对抗网络 (GAN): DeepLearning.AI
- 自然语言处理: DeepLearning.AI
- Deep Learning: Recurrent Neural Networks with Python: Packt
- Design and Build Custom Neural Networks: Coursera
- 神经网络和 PyTorch 简介: IBM
- 计算神经科学: University of Washington
