SamTech

Knowledge

We share various IT and technology-related knowledge. We explain fundamental concepts, principles, and operating mechanisms of computer science, networking, artificial intelligence, big data, and more. Through practical applications and relevant information, we aim to broaden your knowledge and deepen your understanding of technology.

Understanding LSTM (Long Short-Term Memory): A Deep Learning Technology for Time Series Analysis, Natural Language Processing, and More
Understanding LSTM (Long Short-Term Memory): A Deep Learning Technology for Time Series Analysis, Natural Language Processing, and More Long Short-Term Memory (LSTM) networks are a type of recurrent neural network used in complex tasks such as time series analysis, natural language processing (NLP), and speech recognition. One of the standout features of LSTM is its ability to learn long-term patterns, positioning it as a vital tool in various application domains.
2023/08/03 ·
Generative Adversarial Networks, GANs
Generative Adversarial Networks (GANs) Generative Adversarial Networks (GANs) operate by having two neural networks compete against each other. This structure has enabled the generation of complex patterns in deep learning and plays an essential role in various application areas. Characteristics GAN consists of two neural networks, the generator and discriminator, competing with each other in a learning process, allowing the creation of complex data. However, the learning process requires a delicate balance and can be challenging.
2023/08/01 ·
Recurrent Neural Networks (RNN)
Recurrent Neural Networks (RNN) Recurrent Neural Networks (RNN) are a type of artificial neural network designed to recognize patterns in sequences of data, such as text, genomes, handwriting, or spoken words. Unlike traditional neural networks, RNNs have loops, allowing information to be passed from one step in the sequence to the next. Characteristics RNNs are particularly known for their characteristic ability to “remember” previous inputs in the sequence using their hidden state, which makes them very effective for tasks that involve sequential data.
2023/07/22 ·
Convolutional Neural Networks (CNN)
Convolutional Neural Networks (CNN) Convolutional Neural Networks (CNN) are a type of deep neural network that are particularly successful in processing data with a grid-like topology, such as an image. CNNs exploit spatial correlations within the data by applying a series of filters that create a hierarchical representation of the data, making them extremely efficient at image recognition and other computer vision tasks. Characteristics Convolutional Layers: The primary building blocks of CNNs that perform a convolutional operation on the input data.
2023/07/18 ·
Transformer Models
Transformer Models Transformer models have revolutionized the field of Natural Language Processing (NLP) by introducing a new approach to handling sequence data. Developed by Vaswani et al. in the 2017 paper “Attention is All You Need,” this architecture relies heavily on self-attention mechanisms to weigh the importance of words in a sequence, thus improving the model’s understanding of the context. Characteristics Unlike previous sequence handling models like RNNs and LSTMs that process data sequentially, transformers process all the sequence inputs simultaneously.
2023/07/16 ·