This comprehensive course offers a hands-on deep dive into artificial neural networks and their real-world applications using PyTorch, one of the most widely adopted deep learning frameworks used by top AI labs like OpenAI, Meta, and Apple.
Designed for learners ranging from beginner to advanced, the course covers foundational concepts in Artificial Neural Networks (ANNs) and Deep Neural Networks (DNNs) while exploring powerful applications in:
Computer Vision (with CNNs for image recognition)
Natural Language Processing (NLP with RNNs and deep learning)
Time Series Forecasting (including stock return prediction)
Reinforcement Learning (build a stock trading bot)
Generative Adversarial Networks (GANs)
Recommender Systems
Transfer Learning for image classification
Foundations of technologies like ChatGPT, GPT-4, DALL·E, Midjourney, and Stable Diffusion
 
Learners will apply core concepts using tools like Python, NumPy, and PyTorch while mastering project-based workflows that are immediately applicable in business, research, or engineering settings.
 
By the end of this course, you'll be able to:
Build and deploy neural network models using PyTorch
Implement real-world AI projects from computer vision to finance
Understand and apply CNNs, RNNs, GANs, and reinforcement learning models
Use transfer learning and explainability techniques to enhance model performance
Grasp the principles behind cutting-edge AI applications shaping the future
 
Requirements:
Basic Python and NumPy programming
Optional: Familiarity with calculus and probability for theoretical sections
Who This Course is For:
IT professionals, developers, data analysts, and students seeking to gain practical, production-level skills in modern AI techniques—especially those interested in rapidly prototyping and deploying neural network models across diverse applications.
 
If you're ready to build cool projects, understand how real AI works, and take your skills beyond basic theory—this is your next step.