Deep Neural Networks Courses and Certifications

Georgia Tech Research

Mathematics for Deep Neural Networks: Energy Landscape and Open Problems - Lecture 5

Explore the energy landscape of deep neural networks and future challenges in statistical theory, focusing on gradient descent methods and important developments.

• YouTube
• 1 hour 3 minutes
• Self-Paced
• Free Video

Institut des Hautes Etudes Scientifiques (IHES)

Geometry, Topology and Discrete Symmetries Revealed by Deep Neural Networks

Explore deep neural networks' ability to reveal geometry, topology, and discrete symmetries in manifolds. Learn about universal approximators and their applications in inverse problems and group actions.

• YouTube
• 36 minutes
• Self-Paced
• Free Video

Institut des Hautes Etudes Scientifiques (IHES)

Interpreting Deep Neural Networks Towards Trustworthiness

Explore methods for interpreting neural networks, including contextual decomposition and adaptive wavelet distillation, to enhance model trustworthiness and performance.

• YouTube
• 33 minutes
• Self-Paced
• Free Video

VinAI

Implementation of Backtracking Line Search in Deep Neural Networks - Theory and Practice

Explore backtracking line search implementation in deep neural networks, covering theoretical foundations and practical applications for optimizing model performance.

• YouTube
• 1 hour 18 minutes
• Self-Paced
• Free Video

HOTI - Hot Interconnects Symposium

Principles and Practice of Scalable and Distributed Deep Neural Networks

Discover principles and practices for building scalable distributed deep neural networks in this comprehensive 3.5-hour tutorial from Ohio State University experts.

• YouTube
• 3 hours 30 minutes
• Self-Paced
• Free Video

Computational Genomics Summer Institute CGSI

Deep Neural Network Models for Inference of Cellular Dynamics and Underlying Regulatory Networks - MIOflow (Manifold Interpolating Flows)

Discover deep neural network models for cellular dynamics inference and regulatory networks using MIOflow manifold interpolating flows in computational genomics.

• YouTube
• 33 minutes
• Self-Paced
• Free Video

Computational Genomics Summer Institute CGSI

Deep Neural Network Models for Inference of Cellular Dynamics and Underlying Regulatory Networks - Granger Causality and RITINI

Discover deep neural network models for inferring cellular dynamics and regulatory networks using Granger causality and RITINI methods in computational genomics.

• YouTube
• 27 minutes
• Self-Paced
• Free Video

USC Information Sciences Institute

Deep Neural Networks for Modeling Fluid Flow in Underground Rocks

Discover how deep learning models like FNO and U-Net accelerate predictions of viscous fingering, CO₂ leakage, and ground deformation in subsurface fluid flow applications.

• YouTube
• 53 minutes
• Self-Paced
• Free Video

USENIX

Tossing in the Dark: Practical Bit-Flipping on Gray-box Deep Neural Networks for Runtime Trojan Injection

Explore practical bit-flipping techniques for runtime Trojan injection in deep neural networks, focusing on gray-box scenarios and innovative approaches to bypass memory protection systems.

• YouTube
• 12 minutes
• Self-Paced
• Free Video

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
• Self-Paced
• Paid Course

Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

63536 ratings at Coursera

Enhance deep learning skills: master hyperparameter tuning, regularization, optimization, and TensorFlow implementation for improved neural network performance and systematic results generation.

• Coursera
• 23 hours 35 minutes
• Self-Paced
• Paid Course

Deep Learning A-Z 2026: Neural Networks, AI & ChatGPT Prize

49373 ratings at Udemy

Learn to create Deep Learning models in Python from two Machine Learning, Data Science experts. Code templates included.

• Udemy
• 22 hours 33 minutes
• Self-Paced
• Paid Course

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
• Self-Paced
• Free Video

Data Science: Deep Learning and Neural Networks in Python

10276 ratings at Udemy

The MOST in-depth look at neural network theory for machine learning, with both pure Python and Tensorflow code

• Udemy
• 12 hours 9 minutes
• Self-Paced
• Paid Course

Yannic Kilcher

Evolving Normalization-Activation Layers

Explore evolutionary search for optimal normalization-activation layers in neural networks, uncovering novel designs that outperform traditional methods across various image-related tasks.

• YouTube
• 31 minutes
• Self-Paced
• Free Video