Course
Deep Learning Training
Learn how modern neural networks work and build deep learning models for computer vision, natural language processing, and generative AI using PyTorch.
Who this is for
Designed for learners with a solid foundation in Python and Machine Learning who want to master Deep Learning and modern neural network architectures. Suitable for data scientists, AI engineers, software developers, researchers, and professionals interested in computer vision, natural language processing, and generative AI.
Deep Learning has become the foundation of today's most advanced artificial intelligence systems, powering applications such as image recognition, language understanding, recommendation systems, autonomous vehicles, and generative AI. This course provides a practical introduction to modern neural networks, focusing on both the underlying theory and hands-on implementation. You will learn how neural networks are designed, trained, and optimized using PyTorch, while exploring convolutional neural networks (CNNs), recurrent neural networks (RNNs), attention mechanisms, Transformers, and modern generative models. Throughout the course, you will build models on real-world datasets, evaluate their performance, apply regularization techniques, and learn best practices for developing reliable deep learning applications. The program concludes with a comprehensive capstone project that integrates the concepts learned throughout the course.
What you'll learn
Deep Learning foundations, neural networks, and optimization.
PyTorch fundamentals and neural network implementation.
CNNs for computer vision and image classification.
RNNs, sequence models, attention, and Transformers.
Autoencoders, VAEs, GANs, and modern generative models.
Model training, evaluation, regularization, and best practices.
Deep Learning capstone project on real-world datasets.
14 weeks
