Full Stack AI Engineer 2026 – Deep Learning – II

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

What you’ll learn
Build deep learning models from scratch using PyTorch with a strong engineering foundation
Build deep learning models from scratch using PyTorch with a strong engineering foundation
Understand and apply neural networks, backpropagation, and optimization effectively
Train, evaluate, and improve models using regularization and generalization techniques

Requirements
Build CNNs and sequence models for real-world vision and time-series tasks.
Build CNNs and sequence models for real-world vision and time-series tasks.
Apply CNNs and sequence models to solve real image and time-series problems end-to-end.
Create computer vision and time-series solutions using CNNs and sequence networks.

Description
“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 scratchEnd-to-end training pipelinesFully connected networks using real datasetsImage classification models with CNNsSequence prediction models using RNNs, LSTMs, and GRUsYou’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.

Machine learning engineers who want to deepen their understanding of deep neural networks,Software engineers transitioning into AI and deep learning roles,Data scientists looking to build production-ready deep learning models,Students and graduates preparing for AI, ML, or deep learning interviews