https://github.com/anythinglord/ai-journey
Approach to construction of hybrid AI models optimized by Deep Neuroevolution, with ML, AI and DL
https://github.com/anythinglord/ai-journey
ai deeplearning machine-learning neural-networks python tensorflow
Last synced: about 2 months ago
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Approach to construction of hybrid AI models optimized by Deep Neuroevolution, with ML, AI and DL
- Host: GitHub
- URL: https://github.com/anythinglord/ai-journey
- Owner: anythinglord
- Created: 2024-08-09T02:06:44.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2024-10-21T15:28:34.000Z (over 1 year ago)
- Last Synced: 2025-01-21T22:34:58.711Z (over 1 year ago)
- Topics: ai, deeplearning, machine-learning, neural-networks, python, tensorflow
- Language: Python
- Homepage:
- Size: 5.86 KB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# AI Journey
Approach to construction of hybrid AI models optimized by Deep Neuroevolution, with ML, AI and DL
# CNN-VAE (Convolutional Neural Network Variational Autoencoder)
## Overview
The CNN-VAE is an advanced autoencoder architecture that combines Convolutional Neural Networks (CNNs) with Variational Autoencoders (VAEs). It is particularly effective for processing and generating high-dimensional data such as images.
## Components
### Convolutional Neural Network (CNN)
- **Purpose:** The CNN component is used in the encoder to extract features from input images.
- **Advantage:** Convolutional layers excel at capturing spatial hierarchies and patterns in images, making them well-suited for visual data.
### Variational Autoencoder (VAE)
- **Purpose:** The VAE component focuses on learning a probabilistic mapping from the input space to a latent space.
- **Structure:**
- **Encoder:** Maps the input data to a distribution in the latent space, typically producing parameters for a Gaussian distribution (mean and variance).
- **Decoder:** Samples from this latent distribution and reconstructs the original input data from these samples.
### Latent Space
- **Purpose:** Captures the underlying structure of the data.
- **Benefit:** Encourages learned latent representations to be continuous and normally distributed, which aids in generating new, similar data samples.
## Loss Function
The CNN-VAE employs a combined loss function consisting of:
- **Reconstruction Loss:** Measures how well the decoder reconstructs the original input from the latent space. Common metrics include binary cross-entropy or mean squared error.
- **KL Divergence:** Ensures that the distribution learned by the encoder is close to a standard normal distribution. This term regularizes the model and helps prevent overfitting.
## Summary
The CNN-VAE architecture leverages CNNs to effectively process and encode image data and VAEs to learn a meaningful latent space and generate new images. This combination enables powerful generative models capable of creating new, high-quality images similar to the training data.