神经网络
神经元网络知识库可以帮助您了解如何建立、训练和优化模型。您可以在建筑设计、数据挖掘、培训技术和模型评估等方面有所建树。许多课程都有 KI-Praxis 框架和要点。
令人信服的神经网络工具和证书
您将获得的技能: Computer Vision, Image Analysis, Model Evaluation, Convolutional Neural Networks, Model Optimization, Artificial Neural Networks, Model Training, Machine Learning Methods, PyTorch (Machine Learning Library), Artificial Intelligence and Machine Learning (AI/ML), Deep Learning, Transfer Learning, Machine Learning, Recurrent Neural Networks (RNNs), Artificial Intelligence, NumPy, Python Programming, Matplotlib, Data Visualization, Data Science
您将获得的技能: 卷积神经网络, 自动编码器, 深度学习, 自然语言处理, Keras（神经网络库）, 递归神经网络 (RNN), 模型培训, 人工神经网络, 应用机器学习, 网络架构, 模型优化, Transfer Learning, 机器学习, 图像分析, 回归分析, 机器学习方法, 迁移学习
DeepLearning.AI
您将获得的技能: 卷积神经网络, 深度学习, 模型培训, 应用机器学习, Python 编程, 人工神经网络, 递归神经网络 (RNN), 模型优化, 机器学习方法, 人工智能, 监督学习
您将获得的技能: 人工神经网络, 统计方法, 应用机器学习, 预测建模, 模型培训, 模型优化, Model Evaluation, 概率与统计, 数据处理, 逻辑回归, 回归分析, PyTorch（机器学习库）, 模型评估, 张力流
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, Convolutional Neural Networks, 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, Statistical Inference
Johns Hopkins University
您将获得的技能: Responsible AI, Autoencoders, Model Training, Convolutional Neural Networks, Recurrent Neural Networks (RNNs), Data Ethics, Model Optimization, Deep Learning, Artificial Neural Networks, Reinforcement Learning, Generative AI, Generative Adversarial Networks (GANs), Machine Learning Algorithms, Model Deployment, Generative Model Architectures, Debugging, Machine Learning Methods, Artificial Intelligence, Image Analysis, Unsupervised Learning
是什么让您今天来到 Coursera？
DeepLearning.AI
您将获得的技能: 卷积神经网络, 深度学习, 自然语言处理, 计算机视觉, 生成式人工智能, 人工智能和机器学习（AI/ML）, 拥抱的脸, 嵌入, 应用机器学习, 人工神经网络, 模型培训, 递归神经网络 (RNN), 大型语言建模, 模型优化, 微调, Transfer Learning, 监督学习, 图像分析, 迁移学习, 人工智能, 张力流
您将获得的技能: Model Evaluation, Convolutional Neural Networks, Model Training, Data Preprocessing, Image Analysis, Predictive Modeling, Deep Learning, Keras (Neural Network Library), Tensorflow, Data Processing, Model Optimization, Computer Vision, Artificial Neural Networks, Recurrent Neural Networks (RNNs), Data Transformation, Financial Forecasting, Applied Machine Learning, Feature Engineering, Statistical Visualization, Python Programming
您将获得的技能: 卷积神经网络, 深度学习, 模型培训, 人工神经网络, 应用机器学习, 人工智能和机器学习（AI/ML）, 分类算法, 模型优化, Model Evaluation, 图像分析, PyTorch（机器学习库）, 模型评估
Johns Hopkins University
您将获得的技能: Convolutional Neural Networks, Model Optimization, Artificial Neural Networks, Deep Learning, Machine Learning Algorithms, Machine Learning Methods, Model Training, Image Analysis, Machine Learning, Computer Vision, Model Evaluation, Algorithms
您将获得的技能: Data Preprocessing, Deep Learning, Data Processing, Artificial Neural Networks, Logistic Regression, Applied Machine Learning, Program Development, Python Programming, Network Architecture, Model Training, Model Optimization, Machine Learning Algorithms, NumPy, Pandas (Python Package), Linear Algebra
DeepLearning.AI
您将获得的技能: 卷积神经网络, 深度学习, 计算机视觉, 人工神经网络, 模型培训, 嵌入, 应用机器学习, 数据预处理, 微调, 网络架构, Transfer Learning, 图像分析, 张力流, 迁移学习
总之，以下是 10 最受欢迎的 neural network 课程
- Neural Networks and Computer Vision Foundations: Edureka
- 使用 Keras 的深度学习和神经网络简介: IBM
- 神经网络与深度学习: DeepLearning.AI
- 神经网络和 PyTorch 简介: IBM
- Practical Machine Learning: Foundations to Neural Networks: Dartmouth College
- Foundations of Neural Networks: Johns Hopkins University
- 深度学习: DeepLearning.AI
- Deep Learning with Python: CNN, ANN & RNN: EDUCBA
- 使用 PyTorch 进行深度学习: IBM
- Introduction to Neural Networks: Johns Hopkins University
