#keras3

Here’s a far more advanced Keras project, building on your extensive experience—this time implementing a Variational Autoencoder (VAE) with attention modules on the Fashion-MNIST dataset, and then using it for controlled image generation. The goal is to teach representation learning, disentanglement, and generative modeling with attention. The code comprises ~98% of the content; summary and usage…

Here’s a far more advanced Keras project, building on your extensive experience—this time implementing a Variational Autoencoder (VAE) with attention modules on the Fashion-MNIST dataset, and then using it for controlled image generation. The goal is to teach representation learning, disentanglement, and generative modeling with attention. The code comprises ~98% of the content; summary and usage…

Here’s a far more advanced Keras project, building on your extensive experience—this time implementing a Variational Autoencoder (VAE) with attention modules on the Fashion-MNIST dataset, and then using it for controlled image generation. The goal is to teach representation learning, disentanglement, and generative modeling with attention. The code comprises ~98% of the content; summary and usage…

Here’s a far more advanced Keras project, building on your extensive experience—this time implementing a Variational Autoencoder (VAE) with attention modules on the Fashion-MNIST dataset, and then using it for controlled image generation. The goal is to teach representation learning, disentanglement, and generative modeling with attention. The code comprises ~98% of the content; summary and usage…

Here’s a far more advanced Keras project, building on your extensive experience—this time implementing a Variational Autoencoder (VAE) with attention modules on the Fashion-MNIST dataset, and then using it for controlled image generation. The goal is to teach representation learning, disentanglement, and generative modeling with attention. The code comprises ~98% of the content; summary and usage…

Below is a highly advanced Keras project—far more complex than typical examples—leveraging contrastive self-supervised learning (SimCLR) on the STL‑10 dataset, then fine-tuning for semi-supervised classification. The code is ~98% of this response; remaining sections provide context and further guidance. Project Title ai-ml-ds-SrmZNuoOhMk – Self‑Supervised SimCLR + Semi‑supervised…

Below is a highly advanced Keras project—far more complex than typical examples—leveraging contrastive self-supervised learning (SimCLR) on the STL‑10 dataset, then fine-tuning for semi-supervised classification. The code is ~98% of this response; remaining sections provide context and further guidance. Project Title ai-ml-ds-SrmZNuoOhMk – Self‑Supervised SimCLR + Semi‑supervised…

Below is a highly advanced Keras project—far more complex than typical examples—leveraging contrastive self-supervised learning (SimCLR) on the STL‑10 dataset, then fine-tuning for semi-supervised classification. The code is ~98% of this response; remaining sections provide context and further guidance. Project Title ai-ml-ds-SrmZNuoOhMk – Self‑Supervised SimCLR + Semi‑supervised…