{"id":24501364,"url":"https://github.com/anythinglord/ai-journey","last_synced_at":"2026-05-21T07:46:39.883Z","repository":{"id":252332114,"uuid":"840112033","full_name":"anythinglord/ai-journey","owner":"anythinglord","description":"Approach to construction of hybrid AI models optimized by Deep Neuroevolution, with ML, AI and DL","archived":false,"fork":false,"pushed_at":"2024-10-21T15:28:34.000Z","size":6,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"main","last_synced_at":"2025-01-21T22:34:58.711Z","etag":null,"topics":["ai","deeplearning","machine-learning","neural-networks","python","tensorflow"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/anythinglord.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2024-08-09T02:06:44.000Z","updated_at":"2024-10-21T15:28:38.000Z","dependencies_parsed_at":"2024-10-21T20:17:14.523Z","dependency_job_id":null,"html_url":"https://github.com/anythinglord/ai-journey","commit_stats":null,"previous_names":["anythinglord/ai-journey"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/anythinglord%2Fai-journey","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/anythinglord%2Fai-journey/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/anythinglord%2Fai-journey/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/anythinglord%2Fai-journey/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/anythinglord","download_url":"https://codeload.github.com/anythinglord/ai-journey/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":243698107,"owners_count":20333054,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["ai","deeplearning","machine-learning","neural-networks","python","tensorflow"],"created_at":"2025-01-21T22:29:14.394Z","updated_at":"2026-05-21T07:46:34.842Z","avatar_url":"https://github.com/anythinglord.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"# AI Journey\n\nApproach to construction of hybrid AI models optimized by Deep Neuroevolution, with ML, AI and DL\n\n# CNN-VAE (Convolutional Neural Network Variational Autoencoder)\n\n## Overview\n\nThe 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.\n\n## Components\n\n### Convolutional Neural Network (CNN)\n\n- **Purpose:** The CNN component is used in the encoder to extract features from input images.\n- **Advantage:** Convolutional layers excel at capturing spatial hierarchies and patterns in images, making them well-suited for visual data.\n\n### Variational Autoencoder (VAE)\n\n- **Purpose:** The VAE component focuses on learning a probabilistic mapping from the input space to a latent space.\n- **Structure:**\n  - **Encoder:** Maps the input data to a distribution in the latent space, typically producing parameters for a Gaussian distribution (mean and variance).\n  - **Decoder:** Samples from this latent distribution and reconstructs the original input data from these samples.\n\n### Latent Space\n\n- **Purpose:** Captures the underlying structure of the data.\n- **Benefit:** Encourages learned latent representations to be continuous and normally distributed, which aids in generating new, similar data samples.\n\n## Loss Function\n\nThe CNN-VAE employs a combined loss function consisting of:\n\n- **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.\n- **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.\n\n## Summary\n\nThe 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.\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fanythinglord%2Fai-journey","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fanythinglord%2Fai-journey","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fanythinglord%2Fai-journey/lists"}