ML & Deep Learning
Master the building blocks of intelligent systems—from neural networks to LLMs
Explore the essential concepts and architectures that underpin modern machine learning and deep learning. From training neural networks to fine-tuning transformers, this track guides you through practical techniques and state-of-the-art tools to build models that scale, adapt, and perform.
ML & Deep Learning
Learn from Industry Leaders about:
- Designing and training deep neural networks with modern loss functions and architectures for high-performance models.
- Supervised learning techniques with best practices for evaluation, generalization, and robust training.
- Make black-box models interpretable through explainable AI (XAI) methods.
- Using transfer learning to adapt pre-trained models to new tasks efficiently with minimal data.
- Explore Transformer architectures and building foundation models.
Frequently Asked Questions
What will I learn in the ML & Deep Learning track?
You’ll learn to design, train, evaluate, and deploy deep learning models using proven architectural patterns. The track balances foundational machine learning principles with cutting-edge deep learning advances like transformers and transfer learning.
How are deep neural networks designed and trained?
You’ll explore how to select architectures, choose loss functions, and tune hyperparameters. Topics include optimization strategies like Adam, batch normalization, and regularization for effective model convergence.
What is supervised learning and why does generalization matter?
Supervised learning uses labeled datasets to train models. You’ll learn how to prevent overfitting, evaluate performance, and ensure your models generalize to unseen data using methods like cross-validation and dropout.
How does explainable AI (XAI) support model trust?
XAI techniques—like SHAP, LIME, and attention visualization—help developers interpret model decisions, improve transparency, and meet compliance requirements.
What role do transformers and LLMs play in deep learning?
You’ll examine the architecture behind transformer-based models, understand how pre-training works, and apply transfer learning to build domain-adapted models with fewer resources.
Track Speakers London 2025
Track Speakers San Diego 2025
San Diego's program will go live soon! Until then, please take a look atNew York's 2024 Program!
TRACK SPEAKERS MUNICH 2025
TRACK SPEAKERS NEW YORK 2025
Track Speakers Berlin 2025
Track Program London 2025
Track Highlights 2025
Track Program Munich 2025
Track Program New York 2025
Track Program Berlin 2025
Join ML Conference and stay tuned!