機械学習:Machine Learning

python

Comparison of tensorflow, Keras and pytorch

Comparison of tensorflow, keras, and pytorch, deep learning frameworks used for digital transformation and artificial intelligence tasks
python

Protected: Evolving Deep Learning with PyTorch(OpenPose, SSD, AnoGAN, Efficient GAN, DCGAN, Self-Attention GAN, BERT, Transformer, GAN, PSPNet, 3DCNN, ECO)

OpenPose, SSD, AnoGAN, Efficient GAN, DCGAN, Self-Attention GAN, BERT, Transformer, GAN using pytorch for Digital Transformation and Artificial Intelligence tasks, PSPNet,3DCNN,ECO and other advanced DNNs
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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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Protected: Deep learning for computer vision with Python and Keras (4) Visualization of CNN training data

Interpreting CNNs with visualization in DNNs using python/keras for digital transformation and artificial intelligence tasks
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Protected: Deep learning for computer vision with Python and Keras (3) Improving CNNs using trained models

Existing models (ImageNet) using pyhton/keras for digital transformation and artificial intelligence AItasks (few data deep learning (DNN) in use)
python

Protected: Deep learning for computer vision with Python and Keras (2) Improving CNNs with small amounts of data through data expansion

Methods to improve CNNs for small amounts of data using pyhton/Keras for digital transformation and artificial intelligence tasks (improving overtraining by data expansion)
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