#CNN

This tool offers a Keras-like summary in PyTorch for detailed network visualization and debugging, installable via pip or GitHub. It enhances standard print functions by showing detailed architecture not visible otherwise, aiding in model understanding and optimization for various models like CNNs and VGG16.

Explore a Tensorflow-based framework for audio steganalysis using advanced deep learning techniques. This project includes CNN-based methods to analyze MP3 steganography, with findings published in IH&MMSec and ICASSP. Users can create custom networks to investigate entropy coding, high-pass filtering, and correlations in audio data. Comprehensive setup guidelines, dependency management, and execution instructions are available, along with access to datasets and research publications. Utilize Tensorboard for visual analytics and Pycharm for project development.

Delve into the MambaOut PyTorch models introducing groundbreaking efficiency in vision tasks with models like the Kobe edition. MambaOut excels with 80% ImageNet accuracy using minimal resources, integrating with pytorch-image-models for impressive performance. Understand model architecture, and compare causal and RNN-like attention. Find training insights, validation processes, and explore through Hugging Face Spaces' Gradio demo.