Explore >> Select a destination
|
You are here 
|
research.google | ||
| | | | |
blog.research.google
|
|
| | | | | [AI summary] Google Research introduces TimesFM, a decoder-only foundation model for time-series forecasting with zero-shot capabilities, pre-trained on 100 billion real-world time-points, outperforming existing methods in various domains. | |
| | | | |
bdtechtalks.com
|
|
| | | | | The transformer model has become one of the main highlights of advances in deep learning and deep neural networks. | |
| | | | |
ai.googleblog.com
|
|
| | | | | [AI summary] This blog post discusses Google Research's exploration of transfer learning through the T5 model, highlighting its application in natural language processing tasks and the development of the C4 dataset. | |
| | | | |
matt.might.net
|
|
| | | [AI summary] This text explains how a single perceptron can learn basic Boolean functions like AND, OR, and NOT, but fails to learn the non-linearly separable XOR function. This limitation led to the development of modern artificial neural networks (ANNs). The transition from single perceptrons to ANNs involves three key changes: 1) Adding multiple layers of perceptrons to create Multilayer Perceptron (MLP) networks, enabling modeling of complex non-linear relationships. 2) Introducing non-linear activation functions like sigmoid, tanh, and ReLU to allow networks to learn non-linear functions. 3) Implementing backpropagation and gradient descent algorithms for efficient training of multilayer networks. These changes allow ANNs to overcome the limitations of ... | ||
