Transformers are a type of neural network architecture that has gained significant popularity due to their unwavering dedication to achieving optimal results in completing assigned tasks. Deep learning, which is widely recognized as a powerful tool, has significantly transformed the way we operate, proving to be both a lifesaver and a solution to disaster. Big players like OpenAI and DeepMind employ Transformers in their AlphaStar applications. By incorporating attention, the transformer model amplifies the pace of training while preserving precision.
It is ok to say that in certain tasks, the performance of transformers exceeds that of the Google Neural Machine Translation model
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Introduction to the Transformer
An astonishing neural network model named “Transformer” came to light in 2017 by a Google-led team. The transformer is a deep learning model and was proposed in the paper Attention is All You Need. A TensorFlow implementation of it is available as a part of the Tensor2Tensor package. TNN has proven its effectiveness and worth, especially for natural language processing (NLP) tasks. TNNs are not going to replace RNNs, which were introduced by David Rumelhart in 1986. Unfortunately, RNNs have serious limitations. RNNs are trained on long sequences, so gradients tend to explode out of control or vanish to nothing on some occasions. Long-short-term memory neural networks (LSTM) came to the rescue to solve this shortcoming.
AILabPage defines Transformer “A special kind of computer software code is called a transformer that possesses the ability to acquire knowledge in self-learning mode. It utilizes a unique approach to attentiveness, focusing on specific segments of the data provided in order to identify the significant elements”.
Transformers are mainly applied in the domains of computer vision and natural language processing.
Thanks to advancements in machine learning algorithms, price and size reductions in storage capacity, more and more computing power at lower costs, and an explosion in data generation of all kinds, ..
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