Keras

アルゴリズム:Algorithms

Protected: Application of Neural Networks to Reinforcement Learning Policy Gradient, which implements a strategy with a function with parameters.

Application of Neural Networks to Reinforcement Learning for Digital Transformation, Artificial Intelligence, and Machine Learning tasks Policy Gradient to implement strategies with parameterized functions (discounted present value, strategy update, tensorflow, and Keras, CartPole, ACER, Actor Critoc with Experience Replay, Off-Policy Actor Critic, behavior policy, Deterministic Policy Gradient, DPG, DDPG, and Experience Replay, Bellman Equation, policy gradient method, action history)
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Protected: Generative Deep Learning with Python and Keras (1) Text generation using LSTM

Text-generating DNN using LSTM with python/keras for digital transformation and artificial intelligence tasks
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Protected: Advanced deep learning with Python and Keras (3) Model optimization methods

Optimizing networks for deep learning with python/keras for digital transformation and artificial intelligence tasks
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Protected: Advanced deep learning with Python and Keras (2) Model monitoring using Keras callbacks and TensorBord

Monitoring of networks in deep learning process using pyton/keras for digital transformation and artificial intelligence tasks (monitoring of models using Keras callbacks and TensorBord)
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Comparison of tensorflow, Keras and pytorch

Comparison of tensorflow, keras, and pytorch, deep learning frameworks used for digital transformation and artificial intelligence tasks
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Protected: Advanced deep learning with Python and Keras (1) Complex networks using the Keras Functional API

Construction of complex network models using Keras functional API with python/keras for digital transformation and artificial intelligence tasks (for multimodal problems, etc.)
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Protected: DNNs for text and sequences with python and Keras (4) Sequence processing with bidirectional RNNs and CNNs

Bidirectional RNN and CNN application to sequence data in python/keras for digital transformation and artificial intelligencetasks.
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Protected: DNN for text and sequences with python and Keras (3) Advanced use of recurrent neural networks(GRU)

Analysis of sequence data by GRU with pyhton/keras used for digital transformation and artificial intelligence tasks and improvement by recurrent dropout and recurrent layer stacking
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Protected: DNN for text and sequences with Python and Keras (2) Application of SimpleRNN and LSTM

RNN and LSTM for text/sequence information using python/keras for digital transformation and artificial intelligence tasks
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Protected: DNN for text and sequences with Python and Keras (1) Preprocessing text data for training

Deep learning of natural language (DNN) with python/keras for digital transformation and artificial intelligence tasks, distributed vector generation (using Glove model)
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