AI Algorithms Based on Neural Networks
AI algorithms based on neural networks form the backbone of modern machine learning and artificial intelligence systems. These algorithms mimic the structure and function of the human brain, allowing machines to process complex data and learn from it. These algorithms encompass a diverse range of architectures and techniques, including feedforward and recurrent neural networks, convolutional neural networks for image processing, autoencoder-based architectures for unsupervised learning, attention mechanisms and transformers for sequence modeling, as well as generative adversarial networks for creative tasks. With innovations like attention mechanisms and specialized architectures, neural network-based algorithms continue to drive advancements in AI across various domains.
- Feedforward Neural Networks
- Convolutional Neural Networks (CNNs)
- LeNet
- AlexNet
- VGG-16
- GoogLeNet (Inception)
- ResNet (Residual Network)
- DenseNet
- MobileNet
- EfficientNet
- Recurrent Neural Networks (RNNs):
- Autoencoder-based Architectures
- Vanilla Autoencoder
- Variational Autoencoder (VAE)
- Denoising Autoencoder
- Sparse Autoencoder
- Contractive Autoencoder (CAE)
- Adversarial Autoencoders
- Sparse Coding Models
- Attentions based Model
- Generative Adversarial Networks (GANs)
- Other Specialized Architectures
Artificial Intelligence (AI) Algorithms
Artificial Intelligence (AI) is revolutionizing industries, transforming the way we interact with technology. With a growing interest in mastering AI, we’ve crafted a tutorial on AI algorithms, based on extensive research in the field. This tutorial covers core algorithms that serve as the backbone of artificially intelligent systems.