Learn CNN
12 expert-rated courses covering CNN. Compared by rating, price, difficulty, and job relevance so you can pick the right one.
Top CNN Courses
Deep Learning Specialization
Foundational specialization on neural networks, CNNs, sequence models, and practical deep learning engineering.
Introduction to Deep Learning with PyTorch
Build deep learning models with PyTorch. Cover neural network fundamentals, training loops, CNNs, and sequence models.
Fundamentals of Deep Learning
Hands-on deep learning course covering CNNs, data augmentation, transfer learning, and model training.
Convolutional Neural Networks
Master convolutional neural networks for image processing and computer vision tasks. Learn CNN architecture, convolution operations, pooling, and application to real-world problems. Explore pre-trained models and transfer learning techniques.
Deep Learning for Computer Vision
Learn deep learning techniques for computer vision including autoencoders, CNNs, and GANs with hands-on implementation.
PyTorch for Deep Learning
Professional Certificate by DeepLearning.AI covering PyTorch for deep learning, CNNs, transfer learning, and model deployment.
Computer Vision Specialization
Comprehensive specialization covering image analysis, CNNs, Vision Transformers, GANs, and multimodal prompting for computer vision.
Deep Learning and Reinforcement Learning
IBM course covering deep learning architectures (CNNs, RNNs, GANs, autoencoders) and reinforcement learning fundamentals.
Deep Learning for Object Detection
Learn deep learning techniques for object detection using MATLAB including CNNs, transfer learning, and model evaluation.
Deep Learning: Convolutional Neural Networks in Python
TensorFlow 2 CNNs for Computer Vision, Natural Language Processing and more. Deep Learning for Data Science and Machine Learning.
AI Engineer Professional
Advanced specialization covering MLOps, CNNs, RNNs, generative AI agents, LangGraph, Keras, and production-ready AI systems.
Deep Learning & Modern AI Architectures
Master modern deep learning architectures including RNNs, CNNs, transfer learning, and Vision Transformers for practical applications.