CNN

python

Protected: Applying Neural Networks to Reinforcement Learning Deep Q-Network Applying Deep Learning to Value Assessment

Application of Neural Networks to Reinforcement Learning for Digital Transformation, Artificial Intelligence, and Machine Learning tasks Deep Q-Network Prioritized Replay, Multi-step applying deep learning to value assessment Deep Q-Network applying deep learning to value assessment (Prioritized Replay, Multi-step Learning, Distibutional RL, Noisy Nets, Double DQN, Dueling Network, Rainbow, GPU, Epsilon-Greedy method, Optimizer, Reward Clipping, Fixed Target Q-Network, Experience Replay, Average Experience Replay, Mean Square Error, Mean Squared Error, TD Error, PyGame Learning Enviroment, PLE, OpenAI Gym, CNN
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: 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)
python

Protected: Deep Learning for Computer Vision with Python and Keras(1) – Convolution and Pooling

Overview of convolution and pooling in pyhton/keras/CNN for image recognition using deep learning for digital transformation and artificial intelligence tasks.
機械学習:Machine Learning

Protected: Applying Deep Learning to Speech Recognition

Overview of neural network applications (TDNN, RNN, CNN) and deep learning applications (LSTM, CTC) for speech recognition technology used in digital transformation and artificial intelligence tasks
推論技術:inference Technology

Protected: Local Features (3) About Various Descriptors(SIFT,SURF,BRIEF,BRISK,HGO,GIST)

Overview of local descriptors (SIFT descriptors, CNN, SURF descriptors, BRISK descriptors, HLAC descriptors, GIST descriptors) for local feature extraction, which is the first step in image recognition for use in digital transformation (DX) and artificial intelligence (AI) tasks.
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