Deep Neural Networks Courses and Certifications

Packt

Introduction to RNN and DNN

Master RNN and DNN techniques for AI applications. Build sophisticated models for speech recognition, machine translation, and more using PyTorch. Gain practical skills for real-world AI implementation.

• Coursera
• 6 hours 36 minutes
• On-Demand
• Free Online Course (Audit)

Yannic Kilcher

Author Interview - VOS- Learning What You Don't Know by Virtual Outlier Synthesis

Interview exploring Virtual Outlier Synthesis for out-of-distribution detection in neural networks. Discusses motivations, methodology, and key findings of novel approach to improve model robustness using synthesized outlier data.

• YouTube
• 36 minutes
• On-Demand
• Free Video

Yannic Kilcher

Learning What You Don't Know by Virtual Outlier Synthesis - Paper Explained

Explore Virtual Outlier Synthesis, a novel method for improving out-of-distribution detection in deep learning models by generating synthetic outliers during training to enhance decision boundary regularization.

• YouTube
• 36 minutes
• On-Demand
• Free Video

Simons Institute

The Information Bottleneck Theory of Deep Neural Networks

Explore the Information Bottleneck Theory's application to deep neural networks, examining statistical learning, information theory, and stochastic gradient descent in brain data analysis.

• YouTube
• 1 hour 38 minutes
• On-Demand
• Free Video

Databricks

Listening at the Cocktail Party - Deep Neural Networks for Speech Separation

Explore deep neural networks for speech separation, focusing on data preparation, network architectures, and real-time implementation on embedded devices for smart audio applications.

• YouTube
• 40 minutes
• On-Demand
• Free Video

Institute for Pure & Applied Mathematics (IPAM)

Explainable AI to Analyze Internal Decision Mechanism of Deep Neural Networks

Explore techniques for understanding and improving deep neural networks' decision-making processes, focusing on safety in critical applications and automatic correction of unreliable internal nodes.

• YouTube
• 54 minutes
• On-Demand
• Free Video

Institute for Pure & Applied Mathematics (IPAM)

From Network Dissection to Policy Dissection - Emergent Concepts in Deep Representations

Explore emergent concepts in deep neural networks, from image classification to reinforcement learning, revealing inner workings and enabling meaningful human-AI interactions for content creation and robotics control.

• YouTube
• 44 minutes
• On-Demand
• Free Video

Alan Turing Institute

High-Dimensional Learning and Deep Neural Networks

Explorez les réseaux neuronaux profonds et l'apprentissage en haute dimension avec le professeur Mallat. Découvrez les avancées récentes en science des données et leurs applications pratiques.

• YouTube
• 1 hour
• On-Demand
• Free Video

Alan Turing Institute

Data-Driven Regularisation for Solving Inverse Problems - Carola-Bibiane Schönlieb, Turing/Cambridge

Explores data-driven regularizers for inverse imaging problems, combining model-based and data-driven approaches. Covers bilevel optimization and deep neural networks for image processing.

• YouTube
• 48 minutes
• On-Demand
• Free Video

Fields Institute

Arbitrage-Free Yield Curve and Bond Price Forecasting by Deep Neural Networks

Explore deep neural networks for arbitrage-free yield curve and bond price forecasting, enhancing quantitative finance strategies and market predictions.

• YouTube
• 59 minutes
• On-Demand
• Free Video

Simons Institute

Interpreting Deep Neural Networks

Explore techniques for interpreting deep neural networks, focusing on methods to understand and explain their decision-making processes and internal representations.

• YouTube
• 47 minutes
• On-Demand
• Free Video

Simons Institute

Why Deep Learning Works: Implicit Self-Regularization in Deep Neural Networks

Explore the underlying mechanisms of deep learning's success, focusing on implicit self-regularization in neural networks and its impact on model performance.

• YouTube
• 38 minutes
• On-Demand
• Free Video

Alan Turing Institute

Nonlinear Approximation by Deep ReLU Networks - Ron DeVore, Texas A&M

Explores deep ReLU networks for nonlinear approximation, covering architecture, error analysis, and manifold approximation. Bridges mathematics, statistics, and computer science in data science applications.

• YouTube
• 47 minutes
• On-Demand
• Free Video

Alan Turing Institute

Approximation with Deep Networks - Remi Gribonval, Inria

Explore deep network approximation, focusing on expressivity, universal properties, sparse networks, and the impact of network architecture and activation functions on performance.

• YouTube
• 50 minutes
• On-Demand
• Free Video

Pascal Poupart

Deep Neural Networks

Explore deep neural networks, activation functions, and optimization techniques. Learn about gradient vanishing and solutions like rectified linear units.

• YouTube
• 47 minutes
• On-Demand
• Free Video