# BirdNET model version 2.4 > artificial neural network **Wikidata**: [Q136281925](https://www.wikidata.org/wiki/Q136281925) **Source**: https://4ort.xyz/entity/birdnet-model-version-2-4 ## Summary BirdNET model version 2.4 is an artificial neural network designed as a computational model based on connected, hierarchical functions. It is a specific iteration of the BirdNET system, classified as a machine learning tool utilized for pattern recognition and processing through interconnected nodes. The model is documented in English and is part of the broader BirdNET project. ## Key Facts * **Entity Type:** Artificial Neural Network (instance of) * **Parent Project:** BirdNET (part of) * **Core Function:** Computational model used in machine learning based on connected, hierarchical functions * **Documentation:** Described at `https://zenodo.org/records/15050749` * **Language:** English * **Source Reference:** BirdNET-Analyzer GitHub repository * **Architectural Basis:** Inspired by biological neural networks; processes information via interconnected nodes (neurons) organized into layers (input, hidden, output) * **Learning Mechanism:** Capable of supervised, unsupervised, or reinforcement learning; utilizes optimization algorithms like gradient descent to adjust connection weights ## FAQs **What is BirdNET model version 2.4?** BirdNET model version 2.4 is an artificial neural network and a specific version of the BirdNET system, functioning as a computational model for machine learning tasks. **Where can the documentation for this model be found?** The model is described in English at the Zenodo repository (record 15050749), with additional references available via the BirdNET-Analyzer GitHub page. **What technical architecture does the model use?** As an artificial neural network, the model utilizes a computational architecture based on connected, hierarchical functions, processing information through layers of interconnected nodes that adjust based on experience. ## Why It Matters BirdNET model version 2.4 represents a specific implementation of deep learning technology within the BirdNET ecosystem. As an instance of an artificial neural network, it embodies a technology that has revolutionized machine learning by enabling computers to learn from observational data without explicit programming. By leveraging the architecture of interconnected nodes and weighted connections, this model contributes to the broader trend of using neural networks to solve complex problems and recognize patterns. Its documentation on Zenodo also highlights the growing practice of citable, open scientific archiving for AI models. ## Notable For * Being a distinct versioned release (2.4) within the BirdNET project. * Classification as an artificial neural network, a technology recognized for transforming industries from healthcare to finance. * Utilizing a computational model inspired by the biological neural networks of the human brain. * Integration into the BirdNET-Analyzer framework, supported by GitHub references. ## Body ### Identity and Classification BirdNET model version 2.4 is explicitly classified as an **artificial neural network** (ANN). It functions as a computational model used in machine learning, based on connected, hierarchical functions. This model exists as a specific component or version within the larger **BirdNET** system. It is described by a Wikidata entry as an "artificial neural network" and is documented in English. ### Technical Architecture and Components As an artificial neural network, BirdNET model version 2.4 is built upon a foundation of interconnected nodes, or neurons, organized into primary layers: * **Input Layer:** Receives the initial data. * **Hidden Layers:** Process information through weighted connections. * **Output Layer:** Produces the final results. The model relies on **weights** to represent the strength of connections between neurons. These weights are adjusted during training through optimization algorithms like gradient descent. Each neuron applies an **activation function** to its weighted inputs to introduce non-linearity, which is essential for learning complex patterns. While specific architectural details (e.g., whether it is a CNN or RNN) are not defined in the source, ANNs generally power tasks involving pattern recognition, classification, and prediction. ### Learning Mechanisms The model operates within the paradigm of neural networks that learn through supervised, unsupervised, or reinforcement learning. In supervised learning, networks train on labeled datasets to minimize the error between predicted and actual outputs. The training process for such models involves: 1. **Forward Propagation:** Passing inputs through the network. 2. **Error Calculation:** Determining the difference between the output and the target. 3. **Backward Propagation:** Adjusting weights/gradients to improve accuracy. ### Context of Artificial Neural Networks BirdNET model version 2.4 is an instance of a technology that emerged from concepts developed in the 1940s by Warren McCulloch and Walter Pitts. The field experienced a resurgence in the 1980s with the backpropagation algorithm and a transformative revolution in the 2010s due to increased computational power and big data. The broader technology of ANNs is characterized by: * **Market Growth:** The global ANN market is projected to reach $305.53 billion by 2032. * **Industry Applications:** ANNs are used in healthcare (imaging, drug discovery), finance (fraud detection, trading), and autonomous vehicles. * **Challenges:** Common limitations include the "black box" nature of deep networks, vulnerability to adversarial attacks, and the need for large training datasets. ### Documentation and Resources The model is formally described at the URL `https://zenodo.org/records/15050749`. This record is in English and is referenced by the BirdNET-Analyzer GitHub repository (`https://github.com/birdnet-team/BirdNET-Analyzer`), establishing its presence in the open-source development community. ### Competitive Landscape While specific competitors for version 2.4 are not listed, the ecosystem for artificial neural networks involves major tech giants like Google, Microsoft, and Amazon, which provide frameworks, and NVIDIA, which dominates hardware. Open-source frameworks like TensorFlow and PyTorch are standard in this domain, suggesting BirdNET models likely interface with or rely upon these broader industry tools. ## References 1. [Source](https://zenodo.org/records/15050749) 2. [Source](https://github.com/birdnet-team/BirdNET-Analyzer)