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Top rated computer neural networks

Here are some top-rated computer neural networks, grouped by category:

Image Recognition:

  1. ResNet (2015): A deep residual network that won the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) in 2015.
  2. Inception (2014): A network developed by Google that uses multiple parallel branches to improve image recognition accuracy.
  3. DenseNet (2017): A network that uses dense connectivity to improve feature extraction and reduce the number of parameters.
  4. MobileNet (2017): A lightweight network designed for mobile devices, which uses depthwise separable convolutions to reduce computational costs.

Natural Language Processing (NLP):

  1. BERT (2018): A language model developed by Google that uses bidirectional transformers to achieve state-of-the-art results in many NLP tasks.
  2. transformer (2017): A network architecture that uses self-attention mechanisms to model complex dependencies in sequential data.
  3. LSTM (1997): A type of recurrent neural network (RNN) that uses long short-term memory cells to learn long-term dependencies in sequential data.
  4. Word2Vec (2013): A network that uses word embeddings to represent words as vectors in a high-dimensional space.

Speech Recognition:

  1. WaveNet (2016): A network developed by Google that uses dilated convolutions to model raw audio waveforms.
  2. DeepSpeech (2014): A network developed by Baidu that uses RNNs and convolutional neural networks (CNNs) to recognize speech.
  3. CTC (2006): A network that uses connectionist temporal classification to recognize speech in a sequence-to-sequence manner.

Reinforcement Learning:

  1. AlphaGo (2016): A network developed by Google DeepMind that used deep reinforcement learning to beat a human world champion in Go.
  2. DQN (2013): A network that uses deep Q-networks to learn optimal policies in complex environments.
  3. Policy Gradient Methods (2014): A family of algorithms that use policy gradients to optimize policies in reinforcement learning tasks.
  4. Actor-Critic Methods (2016): A family of algorithms that combine policy-based and value-based methods to improve the efficiency of reinforcement learning.

Other notable neural networks:

  1. Generative Adversarial Networks (GANs) (2014): A network that uses generative and discriminative models to generate new data samples.
  2. Autoencoders (1986): A network that uses dimensionality reduction to learn compact representations of data.
  3. Restricted Boltzmann Machines (RBMs) (1986): A network that uses stochastic binary units to model complex distributions.

Note: The dates listed are the dates of publication or introduction of each neural network, and may not necessarily reflect the date of their development or implementation.

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