Lectures related to Principles of Self-Organization

Spatial Continuum: Field Equations MOOC: Neuronal Dynamics 2- Computational Neuroscience: Neuronal Dynamics of Cognition

Explores spatial continuum models, field equations, and coupling mechanisms in computational neuroscience.

Cortical Connectivity: Neuronal Dynamics MOOC: Neuronal Dynamics 2- Computational Neuroscience: Neuronal Dynamics of Cognition

Explores neuronal dynamics in computational neuroscience, emphasizing population activity, cortical connectivity, and connectivity schemes in models.

Neural Networks: Multilayer Perceptrons

Covers Multilayer Perceptrons, artificial neurons, activation functions, matrix notation, flexibility, regularization, regression, and classification tasks.

The Hidden Convex Optimization Landscape of Deep Neural Networks

Explores the hidden convex optimization landscape of deep neural networks, showcasing the transition from non-convex to convex models.

Nonlinear Supervised Learning

Explores the inductive bias of different nonlinear supervised learning methods and the challenges of hyper-parameter tuning.

Statistical Physics in Machine Learning: Understanding Deep Learning

Explores the application of statistical physics in understanding deep learning with a focus on neural networks and machine learning challenges.

Neural Modeling: From Sensory Systems to Behavior

Explores neural modeling of sensory systems and behavior using deep neural networks and task-driven networks.

Neural Networks: Deep Neural Networks

Explores the basics of neural networks, with a focus on deep neural networks and their architecture and training.

Multi-layer Neural Networks Log in to Mediaspace to watch this video

Covers the fundamentals of multi-layer neural networks and the training process of fully connected networks with hidden layers.

Neural Networks: Training and Optimization Log in to Mediaspace to watch this video

Explores the training and optimization of neural networks, addressing challenges like non-convex loss functions and local minima.

Neural Networks: Hierarchical Models and Odor Taxis Log in to Mediaspace to watch this video

Covers neural function, hierarchical models, odor taxis behaviors, and disparate circuit parameters in 18 slides.

Deep Learning Fundamentals Log in to Mediaspace to watch this video

Introduces deep learning fundamentals, covering data representations, neural networks, and convolutional neural networks.

Understanding Visual Cortex with Deep Learning Models Log in to Mediaspace to watch this video

Explores using deep learning models to understand the visual cortex and sensory portraits.

Neural Networks: Regression and Classification Log in to Mediaspace to watch this video

Explores neural networks for regression and classification tasks, covering training, regularization, and practical examples.

Deep Learning Fundamentals Log in to Mediaspace to watch this video

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

Deep Learning: Data Representations and Neural Networks Log in to Mediaspace to watch this video

Explores data representations, histograms, neural networks, and deep learning concepts.

Data-Driven Modeling in Neuroscience: Meenakshi Khosla Log in to Mediaspace to watch this video

By Meenakshi Khosla explores data-driven modeling in large-scale naturalistic neuroscience, focusing on brain activity representation and computational models.

Feed-forward Networks Log in to Mediaspace to watch this video

Introduces feed-forward networks, covering neural network structure, training, activation functions, and optimization, with applications in forecasting and finance.

Neural Networks for NLP Log in to Mediaspace to watch this video

Covers modern Neural Network approaches to NLP, focusing on word embeddings, Neural Networks for NLP tasks, and future Transfer Learning techniques.

Deep Neural Networks Log in to Mediaspace to watch this video

Covers the back-propagation algorithm for deep neural networks and the importance of locality in CNN.