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#CIFAR-10

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Diffusion Models - PyTorch Implementation

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Learn to implement and train diffusion models in PyTorch, covering unconditional and conditional versions, classifier-free guidance, and exponential moving average. Gain hands-on experience with state-of-the-art generative AI techniques.
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10 Lesons
22 minutes
On-Demand
Free-Video
Data Science Conference

Google Colaboratory and Python: Deep Learning Basics for Computer Vision Tasks

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Master Google Colab for image processing while learning Python fundamentals, CNN architecture, and hands-on model training using CIFAR-10 dataset for computer vision tasks.
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1 Lesons
1 hour 56 minutes
On-Demand
Free-Video
Fields Institute

Premonitions of Public Data for Private ML

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Explore how public data pre-training enhances private machine learning, examining its impact on accuracy, zero-shot learning, and the future of privacy-preserving AI techniques.
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21 Lesons
29 minutes
On-Demand
Free-Video
Valence Labs

Reflected Diffusion Models: Principled Data Constraints in Generative Modeling

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Explore reflected diffusion models for improved image generation. Learn about reversing stochastic differential equations, addressing numerical errors, and incorporating data constraints in principled ways.
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6 Lesons
55 minutes
On-Demand
Free-Video
ACM SIGPLAN

One Pixel Adversarial Attacks via Sketched Programs

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Explore a novel approach for one-pixel adversarial attacks using program synthesis, achieving state-of-the-art success rates with fewer queries and transferable programs for neural network classifiers.
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1 Lesons
19 minutes
On-Demand
Free-Video
Code Sync

Comparing Elixir and Python for Simple Neural Networks

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Compare Elixir's Nx library with Python's machine learning ecosystem for training neural networks. Explore development experiences and performance differences using MNIST and CIFAR-10 datasets.
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1 Lesons
38 minutes
On-Demand
Free-Video
Google TechTalks

Differentially Private Diffusion Models Generate Useful Synthetic Images

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Explore privacy-preserving synthetic image generation using diffusion models. Learn techniques for fine-tuning pre-trained models to achieve state-of-the-art results on CIFAR-10 and Camelyon17 datasets.
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1 Lesons
41 minutes
On-Demand
Free-Video
Finnish Center for Artificial Intelligence FCAI

Elucidating the Design Space of Diffusion-Based Generative Models

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Explore advancements in diffusion-based generative models, including improved sampling, training, and score network preconditioning. Learn about state-of-the-art results and modular design changes for enhanced efficiency and quality.
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1 Lesons
53 minutes
On-Demand
Free-Video
USENIX

Splitting the Difference on Adversarial Training - A Method for Robust Neural Networks

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Discover a novel approach to adversarial training that enhances neural network robustness while maintaining optimal accuracy by splitting data classes into clean and adversarial categories.
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1 Lesons
13 minutes
On-Demand
Free-Video