Deep Neural Networks Courses and Certifications

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

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

Stanford Seminar - Persistent and Unforgeable Watermarks for Deep Neural Networks

Explore persistent and unforgeable watermarking techniques for deep neural networks, focusing on IP protection, embedding methods, and resistance to attacks.

• YouTube
• 1 hour 2 minutes
• On-Demand
• Free Online Course

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

Databricks

Generalized Pipeline Parallelism for DNN Training - PipeDream System Overview

Efficient multi-accelerator parallelization for DNN training using PipeDream, combining intra-batch parallelism with inter-batch pipelining to improve throughput and overcome challenges in bi-directional training.

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

Yacine Mahdid

Adam Optimizer from Scratch in Python

Discover how to implement the Adam optimizer from scratch using Python and numpy, covering theory, formulas, and practical coding for deep neural network training.

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

Deep Learning: Convolutional Neural Networks in Python

6531 ratings at Udemy

Tensorflow 2 CNNs for Computer Vision, Natural Language Processing (NLP) +More! For Data Science & Machine Learning

• Udemy
• 13 hours 54 minutes
• On-Demand
• Paid Course

Deep Learning: Recurrent Neural Networks in Python

5935 ratings at Udemy

GRU, LSTM, Time Series Forecasting, Stock Predictions, Natural Language Processing (NLP) using Artificial Intelligence

• Udemy
• 13 hours 24 minutes
• On-Demand
• Paid Course

Artem Kirsanov

Dendrites: Why Biological Neurons Are Deep Neural Networks

Explore how individual neurons function like deep neural networks, examining their complex information processing capabilities and the physiological mechanisms behind their computational complexity.

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

Introduction to Deep Learning & Neural Networks with Keras

2088 ratings at Coursera

. This course will introduce you to the field of deep learning and help you answer many questions that people are asking nowadays, like what is deep learning, and how do deep learning models compare to artificial neural networks?

• Coursera
• 10 hours 10 minutes
• On-Demand
• Paid Course

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

Hausdorff Center for Mathematics

Tropical Solutions to Hard Problems in Auction Theory and Neural Networks - Lecture II

Explore tropical mathematics and its applications in neural networks, auction theory, and more, focusing on key ideas of tropical objects as limits and their polyhedral geometry.

• YouTube
• 1 hour 3 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

Optimizing for Interpretability in Deep Neural Networks - Mike Wu

Explore techniques for making deep neural networks more interpretable through regularization, focusing on medical applications in critical care and HIV treatment. Insights on balancing model performance with human-understandable decision processes.

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

ACM SIGPLAN

Taming Reachability Analysis of DNN-Controlled Systems via Abstraction-Based Training

Explore a novel abstraction-based approach for efficient reachability analysis of DNN-controlled systems, improving tightness and efficiency over existing methods.

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