Kirill Eremenko – Deep Learning A-Z 2026: Neural Networks, AI & ChatGPT Prize (Download)
Artificial intelligence isn’t future speculation—it’s reshaping industries today. Self-driving cars, medical diagnostics, and recommendation engines all depend on deep learning. Yet most educational resources drown learners in mathematical theory without explaining the practical “why” behind each technique.
This deep learning course takes a different path. You’ll build six real-world AI models while developing genuine intuition for how neural networks actually think and learn, moving far beyond memorizing formulas.
Course Framework and Instructors
Created by Kirill Eremenko, Hadelin de Ponteves, and the SuperDataScience Team, this program divides deep learning into two fundamental branches: Supervised and Unsupervised Learning. Each section focuses on specific neural network architectures with hands-on implementation.
You’ll work with both TensorFlow (Google’s framework) and PyTorch (developed by researchers at Nvidia and leading universities), learning when to apply each tool for optimal results.
What You’ll Master
Artificial Neural Networks (ANNs)
Understand how neurons, activation functions, gradient descent, and backpropagation work together. Build an ANN to solve a customer churn problem for a bank, predicting which customers will leave based on demographic and transactional data.
Convolutional Neural Networks (CNNs)
Learn CNN architecture from convolution filters to fully connected layers. Create an image recognition system that classifies objects in photos—starting with cats versus dogs, then adaptable to medical imaging or custom applications.
Recurrent Neural Networks (RNNs)
Master Long Short-Term Memory (LSTM) networks that possess memory capabilities. Apply RNNs to predict Google stock prices using historical trends, tackling the same challenge faced by Stanford researchers.
Self-Organizing Maps (SOMs)
Explore unsupervised learning through SOMs for fraud detection. Analyze credit card applications to identify potential fraudulent patterns, creating an explicit list of suspicious cases for investigation.
Boltzmann Machines
Build a movie recommendation system using Restricted Boltzmann Machines. Work with Netflix-style datasets where users rate films from 1-5, predicting which unwatched movies each person would enjoy most.
AutoEncoders
Implement stacked autoencoders—a cutting-edge technique—for an advanced recommender system. Compare performance against the Boltzmann Machine approach while understanding regularization techniques like denoising and sparse autoencoders.
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Learning Approach
Every practical tutorial begins with blank code that you write together with instructors, understanding each line’s purpose. The course provides structured code templates you can adapt for personal projects by swapping datasets and adjusting parameters.
Intuition tutorials precede technical implementation, ensuring you grasp the conceptual “why” before diving into mechanics. This foundation makes coding exercises meaningful rather than rote copying.
Technical Coverage
Beyond core architectures, you’ll learn essential skills: feature scaling and normalization, handling missing data, train-test splitting, confusion matrices, and model evaluation. The curriculum includes regression fundamentals, logistic regression, and preprocessing techniques critical for production-ready models.
Libraries covered include Keras (for rapid prototyping), Scikit-learn (for evaluation and preprocessing), NumPy (for computations), Matplotlib (for visualization), and Pandas (for data manipulation).
Who This Course Serves
Perfect for beginners with high school math looking to enter deep learning, intermediate practitioners wanting broader exposure to different architectures, and professionals seeking to add measurable AI value to their business operations.
The course avoids unnecessary mathematical complexity while maintaining technical rigor, making advanced concepts accessible without oversimplification.
Ready to build intelligent systems that learn from data? Start with practical projects that demonstrate AI’s real-world impact.
