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Neural
Tag: Neural
Artificial Intelligence
Effective Time Series Forecasts: Harnessing RNNs for Predictive Analytics
admin
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November 19, 2024
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Artificial Intelligence
What’s driving interest in neural machine translation is the ability to learn consideration from vast amounts of bilingual data. This post explores the creation of a simple neural machine translation system utilizing the Keras deep learning library and TensorFlow as the backend. “`python import numpy as np from keras.layers import Embedding, Dense, LSTM from keras.models import Model class NeuralMachineTranslator: def __init__(self, source_vocabulary_size, target_vocabulary_size): self.source_embedding = Embedding(input_dim=source_vocabulary_size, output_dim=128, mask_zero=True) self.target_embedding = Embedding(input_dim=target_vocabulary_size, output_dim=128, mask_zero=True) encoder_input = self.source_embedding.input encoder_hidden_state = LSTM(256)(self.source_embedding(encoder_input)) decoder_input = Dense(256)(encoder_hidden_state) decoder_output = LSTM(256, return_sequences=True)(decoder_input) self.model = Model(inputs=self.source_embedding.input, outputs=decoder_output) def translate(self, source_sentence): encoder_input = np.zeros((1, len(source_sentence), 128)) for i in range(len(source_sentence)): word_vector = self.source_embedding.get_weights()[0][source_sentence[i]] encoder_input[0, i] = word_vector output = self.model.predict(encoder_input) return [np.argmax(output[0, i]) for i in range(len(source_sentence))] translator = NeuralMachineTranslator(10000, 5000) print(translator.translate([‘hello’, ‘world’])) “`
admin
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November 4, 2024
0
Artificial Intelligence
What are you trying to do?
admin
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November 1, 2024
0
Artificial Intelligence
You uncertain? A probabilistic approach to extracting uncertainty measures from artificial intelligence models.
admin
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October 24, 2024
0
Robotics
Neural networks rely heavily on data compression techniques to streamline processing and accelerate training. JPEG compression, specifically, offers a novel approach to enhance neural community coaching by leveraging its lossy compression mechanisms. This innovative methodology capitalizes on the compressive power of JPEG to prune irrelevant information from large datasets, thereby reducing memory requirements and speeding up computation times. By strategically applying JPEG-based pruning techniques, neural networks can be optimized for real-time applications, making it an attractive solution for deploying AI models in resource-constrained environments.
admin
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October 10, 2024
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Technology
A novel approach to building neural networks could significantly enhance AI transparency.
admin
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August 31, 2024
0
Artificial Intelligence
Group-equivariant neural networks with escnn
admin
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July 8, 2024
0
Technology
The Finest Prosthetic Leg But, Because of a Neural Interface
admin
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July 2, 2024
0
Artificial Intelligence
Uneven Licensed Robustness through Function-Convex Neural Networks – The Berkeley Synthetic Intelligence Analysis Weblog
admin
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June 26, 2024
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![What’s driving interest in neural machine translation is the ability to learn consideration from vast amounts of bilingual data. This post explores the creation of a simple neural machine translation system utilizing the Keras deep learning library and TensorFlow as the backend. “`python import numpy as np from keras.layers import Embedding, Dense, LSTM from keras.models import Model class NeuralMachineTranslator: def __init__(self, source_vocabulary_size, target_vocabulary_size): self.source_embedding = Embedding(input_dim=source_vocabulary_size, output_dim=128, mask_zero=True) self.target_embedding = Embedding(input_dim=target_vocabulary_size, output_dim=128, mask_zero=True) encoder_input = self.source_embedding.input encoder_hidden_state = LSTM(256)(self.source_embedding(encoder_input)) decoder_input = Dense(256)(encoder_hidden_state) decoder_output = LSTM(256, return_sequences=True)(decoder_input) self.model = Model(inputs=self.source_embedding.input, outputs=decoder_output) def translate(self, source_sentence): encoder_input = np.zeros((1, len(source_sentence), 128)) for i in range(len(source_sentence)): word_vector = self.source_embedding.get_weights()[0][source_sentence[i]] encoder_input[0, i] = word_vector output = self.model.predict(encoder_input) return [np.argmax(output[0, i]) for i in range(len(source_sentence))] translator = NeuralMachineTranslator(10000, 5000) print(translator.translate([‘hello’, ‘world’])) “`](https://i2.wp.com/blogs.rstudio.com/tensorflow/posts/2018-07-30-attention-layer/images/attention.png?w=768&resize=768,0&ssl=1 "What’s driving interest in neural machine translation is the ability to learn consideration from vast amounts of bilingual data. This post explores the creation of a simple neural machine translation system utilizing the Keras deep learning library and TensorFlow as the backend. “`python import numpy as np from keras.layers import Embedding, Dense, LSTM from keras.models import Model class NeuralMachineTranslator: def __init__(self, source_vocabulary_size, target_vocabulary_size): self.source_embedding = Embedding(input_dim=source_vocabulary_size, output_dim=128, mask_zero=True) self.target_embedding = Embedding(input_dim=target_vocabulary_size, output_dim=128, mask_zero=True) encoder_input = self.source_embedding.input encoder_hidden_state = LSTM(256)(self.source_embedding(encoder_input)) decoder_input = Dense(256)(encoder_hidden_state) decoder_output = LSTM(256, return_sequences=True)(decoder_input) self.model = Model(inputs=self.source_embedding.input, outputs=decoder_output) def translate(self, source_sentence): encoder_input = np.zeros((1, len(source_sentence), 128)) for i in range(len(source_sentence)): word_vector = self.source_embedding.get_weights()[0][source_sentence[i]] encoder_input[0, i] = word_vector output = self.model.predict(encoder_input) return [np.argmax(output[0, i]) for i in range(len(source_sentence))] translator = NeuralMachineTranslator(10000, 5000) print(translator.translate([‘hello’, ‘world’])) “`")
