DL-Workshop-Series

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DL-workshop-series is an Open Source research & education tool that provides materials for deep learning workshops. It includes code and notebooks for various CNN architectures and learning in deep networks.

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At a glance

Pricing

Open Source

Free tier

Yes

API

Skill level

Technical

About

What is DL-workshop-series?

DL-workshop-series is an open-source GitHub repository maintained by Machine Learning Tokyo (MLT), offering comprehensive materials for deep learning workshops. It features practical implementations and theoretical insights into convolution operations and learning processes within deep neural networks. The repository includes Colab notebooks with examples of kernel applications and functions for constructing Keras models, covering architectures like AlexNet, VGG, Inception, MobileNet, ResNet, and YOLO. It serves as a valuable resource for individuals and groups looking to learn and practice deep learning techniques, providing both code and presentation slides for a structured learning experience.

Best used for

Ideal for professors and teachers who need to access deep learning workshop materials, demonstrate various CNN architectures, and provide practical coding examples using Keras. Especially valuable for educational institutions and study groups focusing on machine learning.

Common actions

learn deep learning

teach deep learning

explore CNN architectures

implement Keras models

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Capabilities

Key features

Convolution operation implementations
Keras model functions
AlexNet, VGG, Inception models
MobileNet, ResNet, YOLO models
Presentation slides included

Target Audience

professorteacher

Integrations

Not yet documented

Pricing & Plans

Open Source

Free

FAQs

What deep learning models are covered in the DL-workshop-series?

The DL-workshop-series covers a range of deep learning models including AlexNet, VGG, Inception, MobileNet, ShuffleNet, ResNet, DenseNet, Xception, Unet, SqueezeNet, YOLO, and RefineNet, providing implementations and examples for each.

Does the repository include theoretical explanations or just code?

The repository includes both practical code implementations, particularly in Colab notebooks for convolution operations and Keras models, and links to presentation slides that likely cover the theoretical aspects of the workshops.

Is the DL-workshop-series suitable for beginners in deep learning?

While it provides comprehensive materials, the content covers advanced topics like various CNN architectures and deep network learning. Users should ideally have some foundational knowledge in machine learning and Python programming.

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Academic Research Study Assistants Knowledge Management Scientific Computing Summarization Language Learning

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Coding & Development › Open Source & Models

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