A subset of machine learning that uses deep artificial neural networks to model and analyze complex data. This technique is particularly used in fields such as computer vision, natural language processing, and voice recognition.
Main Branches
1 . Convolutional Neural Networks (CNNs)
Primarily used for processing image data and computer vision.
CNNs can effectively detect spatial features from images and are used in tasks like face recognition, object detection, and image classification.
2 . Recurrent Neural Networks (RNNs)
These networks are designed for processing sequential data, such as text and time signals.
RNNs can take into account previous information in subsequent processing due to their memory feature.
3 . Long Short-Term Memory (LSTM)
A specific type of RNN designed to address the “vanishing gradient” problem.
LSTMs are particularly effective for analyzing sequential data and time series predictions.
4 . Deep Neural Networks (DNNs)
These networks consist of multiple hidden layers and can identify more complex patterns.
DNNs are commonly used for a wide range of deep learning problems.
5 . Generative Adversarial Networks (GANs)
This type of network involves two neural networks that work competitively; one generates data, while the other attempts to identify the differences between real and generated data.
GANs are used in image generation, digital art, and data simulation.
6 . Transformers
These models are especially famous in natural language processing and are very efficient due to their structure that facilitates parallel processing of data.
Transformers are used in machine translation, chatbots, and large language models like GPT and BERT.
7 . Deep Reinforcement Learning
A combination of deep learning and reinforcement learning that allows agents to learn and make decisions in complex environments.
This technique is used in video games, robotics, and autonomous vehicles.
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