最適化:Optimization

微分積分:Calculus

Machine Learning Professional Series: Topic Models Post-Reading Notes

Topic models using probability generation models to extract sentence topics to be used in digital transformation (DX) and artificial intelligence (AI) tasks.
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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)
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.
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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: 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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