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Optimizing networks for deep learning with python/keras for digital transformation and artificial intelligence tasks
機械学習:Machine Learning
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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)
微分積分:Calculus This is a good introduction to deep learning (Machine Learning Startup Series)Reading Notes
Overview of deep learning for digital transformation and artificial intelligence tasks, including machine learning, gradient descent, regularization, error back propagation, self-encoders, convolutional neural networks, recurrent neural networks, Boltzmann machines, and reinforcement 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
python 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.)
python 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.
python 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
python 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
python 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)