Free Coupon Code Full Stack AI Engineer 2026 - Deep Learning - II 100% OFF

Udemy Free coupon code for Full Stack AI Engineer 2026 - Deep Learning - II course taught by Data Science Academy , School of AI, which has 1,113 students and is rated 0.0 out of 0 votes. This course is about 6 hour(s) 6 minute(s) in English and was updated on January 15, 2026. You can use this Udemy course with a free certificate and find the coupon at the bottom of this page.

Build production-ready deep learning models using PyTorch, with strong foundations, hands-on labs, and real-world engine

“This course contains the use of artificial intelligence”

Deep learning is no longer just a research skill — it is a core engineering competency. This course, Deep Learning Foundations for AI Engineers, is designed to take you beyond theory and help you build, train, debug, and manage deep learning systems the way real AI engineers do.

You’ll start by developing a strong conceptual foundation in neural networks, understanding how artificial neurons, forward propagation, activation functions, and loss functions work together to enable learning. Rather than memorizing formulas, you’ll build intuition through visual explanations and code-driven demonstrations.

From there, you’ll move into training deep neural networks using PyTorch, learning critical skills such as gradient descent, backpropagation, optimizer selection, and learning rate tuning. You’ll understand why models fail, how overfitting happens, and how to apply regularization techniques like L1/L2 penalties, dropout, and batch normalization to improve generalization.

This course is highly hands-on. You’ll implement:

• A neural network from scratch

• End-to-end training pipelines

• Fully connected networks using real datasets

• Image classification models with CNNs

• Sequence prediction models using RNNs, LSTMs, and GRUs

You’ll also develop a strong engineering mindset by learning model saving, loading, and versioning, experiment reproducibility, debugging deep learning models, and monitoring training and validation curves — skills that are essential in production environments, not just notebooks.

By the end of the course, you won’t just “know deep learning” — you’ll think and work like a deep learning engineer, capable of building scalable, reproducible, and production-ready AI systems.