『ゼロから作る Deep Learning ❸』(O'Reilly Japan, 2020)
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Updated
May 27, 2024 - Python
『ゼロから作る Deep Learning ❸』(O'Reilly Japan, 2020)
This repository is the collection of research papers in Deep learning, computer vision and NLP.
Training with FP16 weights in PyTorch
Quasi-recurrent Neural Networks for Keras
Different deep learning architectures are implemented for time series classification and prediction purposes.
My continuing work on the Numer.ai machine learning challenge.
The program uses HOG and LBP features to detect human in images. First, use the HOG feature only to detect humans. Next, combine the HOG feature with the LBP feature to form an augmented feature (HOG-LBP) to detect human. A Two-Layer Perceptron (feedforward neural network) will be used to classify the input feature vector into human or no-human.
Where all the state-of-the-art computer vision Algorithms meet
The code here can be used to train a Transformer Neural Network to perform symbol recovery at the receiver end.
This project was done as a part of COMP9444 Neural Networks and Deep Learning Course Project.
Multilayer Perceptron Neural network for binary classification between two type of breast cancer ("benign" and "malignant" )using Wisconsin Breast Cancer Database
Predict the future BTCUSDT cryptocurrency exchange prices using neural network.
Implementation of TD Gammon algorithm by Gerald Tesauro at IBM's Thomas J. Watson Research Center in Python.
Keras implementation of Deep Convolutional Generative Adversarial Networks, code run base on tensorflow or theano
Machine learning for decrypting classical ciphers
my code for paper Self-supervised Sample Mining with Switchable Selection Criteria for Object Detection
Using pygame to create a 2d pong game, then using gym and tensorflow to read the pixels on the screen using a CNN and then model the actions with a Qlearning RNN to beat the ai opponent
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