Deep Learning with PyTorch

This course uses the PyTorch framework to provide students a hands-on introduction to deep learning. Students gain a solid foundation in model training, neural networks, backpropagation, and optimization methods. With practical projects in computer vision and natural language processing, the course covers advanced architectures like Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), LSTMs, and Transformers. Additionally, it examines contemporary methods for practical AI applications, including as regularization, batch normalization, transfer learning, mixed precision training, and model optimization.
Using Deep Learning

Provider/Creator: Meta AI
Platform: Coursera
Category: PyTorch Deep Learning
Level: Intermediate
Duration: ~2 Months
Certificate: Paid Certificate (Audit Free)
Rating: ⭐ 4.7/5
Direct Course Link: Deep Learning with PyTorch
Recommended For: Intermediate Python developers, machine learning practitioners, AI engineers, and data scientists who want hands-on experience building deep learning applications with PyTorch.