计算神经科学课程
计算神经科学课程可以帮助您学习神经建模、数据分析技术和大脑连接原理。您可以培养使用 Python 编程、统计分析以及使用机器学习算法来分析神经数据的技能。许多课程都会介绍 MATLAB 和 TensorFlow 等工具，这些工具支持模拟神经网络和分析大型数据集，使您能够在研究和实际应用中应用所学知识。
热门计算神经科学课程和认证
状态：免费试用DeepLearning.AI
您将获得的技能: 卷积神经网络, 模型部署, 机器学习, Javascript, 计算机视觉, JSON, 迁移学习, 应用机器学习, 数据转换, 实时数据, 数据处理, 网络应用, 张力流, 图像分析, 人工神经网络
状态：免费试用您将获得的技能: Recurrent Neural Networks (RNNs), Embeddings, Time Series Analysis and Forecasting, Tensorflow, Natural Language Processing, Forecasting, Image Analysis, Deep Learning, Artificial Neural Networks, Predictive Analytics, Predictive Modeling, Technical Analysis, Data Preprocessing, Supervised Learning, Data Science, Machine Learning
状态：免费试用Columbia University
您将获得的技能: 计算机视觉, 无监督学习, 降维, 图论, 深度学习, 图像分析, 机器学习算法, Machine Learning 方法, Algorithm, 人工神经网络
状态：预览Northeastern University
您将获得的技能: Model Evaluation, Linear Algebra, Statistical Machine Learning, Probability, Bayesian Statistics, Probability Distribution, Mathematical Modeling, Machine Learning, Applied Mathematics, Statistical Inference, Dimensionality Reduction, Algebra
状态：新状态：免费试用University of Colorado Boulder
您将获得的技能: Vision Transformer (ViT), Recurrent Neural Networks (RNNs), Multimodal Prompts, Artificial Intelligence and Machine Learning (AI/ML), Embeddings, Digital Signal Processing, Transfer Learning
您将获得的技能: Tensorflow, Convolutional Neural Networks, Keras (Neural Network Library), Matplotlib, Artificial Neural Networks, Image Analysis, Deep Learning, Applied Machine Learning, Python Programming, Model Evaluation, Adaptability, Problem Solving
状态：新状态：免费试用您将获得的技能: Unity Engine, Algorithms, Game Design, Video Game Development, Programming Principles, Virtual Environment, 3D Assets, Debugging, Data Structures
状态：免费试用DeepLearning.AI
您将获得的技能: Keras（神经网络库）, 深度学习, 分布式计算, 性能调整, 张力流
状态：免费试用您将获得的技能: Recurrent Neural Networks (RNNs), PyTorch (Machine Learning Library), Convolutional Neural Networks, Deep Learning, Classification Algorithms, Transfer Learning, Applied Machine Learning, Image Analysis, Model Evaluation, Artificial Neural Networks, Computer Vision, Data Preprocessing
状态：免费试用Imperial College London
您将获得的技能: 概率分布, 卷积神经网络, 模型评估, 计算机视觉, 模型部署, 数据预处理, 自然语言处理, 监督学习, 张力流, 自动编码器, 深度学习, 迁移学习, 贝叶斯统计, Keras（神经网络库）, 递归神经网络 (RNN), 数据管道, 应用机器学习, 生成模型架构, 图像分析, 人工神经网络
状态：免费试用您将获得的技能: 机器学习, 卷积神经网络, 模型评估, 分类与回归树 (CART), 自然语言处理, 回归分析, 网络架构, 深度学习, 迁移学习, Keras（神经网络库）, 递归神经网络 (RNN), 图像分析, 人工神经网络
状态：免费试用DeepLearning.AI
您将获得的技能: 模型评估, 验证和确认, 数据预处理, 深度学习, Machine Learning 方法, 张力流, 性能调整, 人工神经网络
总之，以下是 10 最受欢迎的 computational neuroscience 课程
- 基于浏览器的 TensorFlow.js 模型: DeepLearning.AI
- Deep Learning - Recurrent Neural Networks with TensorFlow: Packt
- 视觉感知: Columbia University
- Foundations of Statistical Learning & Algorithms: Northeastern University
- Modern AI Models for Vision and Multimodal Understanding: University of Colorado Boulder
- CNNs with TensorFlow: Basics of Machine Learning: Coursera
- Introduction to Procedural Maze Building and Maze Algorithms: Packt
- 使用 TensorFlow 进行自定义和分布式培训: DeepLearning.AI
- Building and Training Neural Networks with PyTorch: Packt
- 用于深度学习的 TensorFlow 2: Imperial College London
