Transformers: Self-Attention and MLP

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Offers insights into the statistical physics of learning, exploring the relationship between neural network structure and disordered systems.

Mathematics of Data: From Theory to Computation

Covers key concepts in data mathematics, including automatic differentiation, linear layers, and attention layers.

Chemical Reactions: Transformer Architecture

Explores atom mapping in chemical reactions and the transition to reaction grammar using the transformer architecture.

Deep Learning Fundamentals

Introduces deep learning, from logistic regression to neural networks, emphasizing the need for handling non-linearly separable data.

Reinforcement Learning Concepts

Covers key concepts in reinforcement learning, neural networks, clustering, and unsupervised learning, emphasizing their applications and challenges.

Recurrent Neural Networks: Training and Challenges

Discusses recurrent neural networks, their training challenges, and solutions like LSTMs and GRUs.

Non conceptual knowledge systems

Explores the impact of Deep learning on Digital Humanities, focusing on non conceptual knowledge systems and recent advancements in AI.

Dimensionality Reduction: PCA and Autoencoders

Introduces artificial neural networks, CNNs, and dimensionality reduction using PCA and autoencoders.

Sequence to Sequence Models: Overview and Attention Mechanisms

Explores sequence to sequence models, attention mechanisms, and their role in addressing model limitations and improving interpretability.

Deep Splines: Unifying Framework for Deep Neural Networks

Introduces a functional framework for deep neural networks with adaptive piecewise-linear splines, focusing on biomedical image reconstruction and the challenges of deep splines.