Neural Networks

Protected: Applying Neural Networks to Reinforcement Learning Applying Deep Learning to Strategy:Advanced Actor Critic (A2C)

Application of Neural Networks to Reinforcement Learning for Digital Transformation, Artificial Intelligence, and Machine Learning tasks Implementation of Advanced Actor Critic (A2C) applying deep learning to strategies (Policy Gradient method, Q-learning, Gumbel Max Trix, A3C (Asynchronous Advantage Actor Critic))

2023.03.02

アルゴリズム:Algorithmsグラフ理論スパースモデリング幾何学:Geometry強化学習微分積分:Calculus最適化:Optimization機械学習:Machine Learning深層学習:Deep Learning確率・統計:Probability and Statistics線形代数:Linear Algebra

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

2023.02.02

pythonアルゴリズム:Algorithmsグラフ理論スパースモデリング幾何学:Geometry強化学習微分積分:Calculus最適化:Optimization機械学習:Machine Learning深層学習:Deep Learning確率・統計:Probability and Statistics線形代数:Linear Algebra集合論:Set theory

Protected: Application of Neural Networks to Reinforcement Learning Value Function Approximation, which implements value evaluation as a function with parameters.

Application of Neural Networks to Reinforcement Learning used for Digital Transformation, Artificial Intelligence, and Machine Learning tasks Examples of implementing value evaluation with functions with parameters (CartPole, Q-table, TD error, parameter update, Q-Learning, MLPRegressor, Python)

2023.01.19

アルゴリズム:Algorithmsグラフ理論幾何学:Geometry強化学習微分積分:Calculus最適化:Optimization機械学習:Machine Learning深層学習:Deep Learning確率・統計:Probability and Statistics線形代数:Linear Algebra

Protected: Application of Neural Networks to Reinforcement Learning (2) Basic Framework Implementation

Implementation of a basic framework for reinforcement learning with neural networks utilized for digital transformation, artificial intelligence and machine learning tasks (TensorBoard, Image tab, graphical, real-time, progress check, wrapper for env. Observer, Trainer, Logger, Agent, Experience Replay, episode, action probability, policy, Epsilon-Greedy method, python)

2023.01.04

アルゴリズム:Algorithms強化学習微分積分:Calculus最適化:Optimization機械学習:Machine Learning深層学習:Deep Learning確率・統計:Probability and Statistics線形代数:Linear Algebra

Implementation of Neural Networks and Error Back Propagation using Clojure

Implementation of neural nets and error back propagation using Clojure for digital transformation (DX), artificial intelligence (AI), and machine learning (ML) tasks

2022.11.29

アルゴリズム:Algorithms微分積分:Calculus最適化:Optimization機械学習:Machine Learning深層学習:Deep Learning確率・統計:Probability and Statistics線形代数:Linear Algebra

Protected: Machine Translation Today and Tomorrow – Different Machine Learning Approaches for Natural Language

Present and future of machine translation for digital transformation, artificial intelligence, and machine learning tasks - Different machine learning approaches for natural language machine translation based on attentional neural nets, machine translation based on encoding and decoding, recurrent neural nets, machine translation based on neural nets and neural models, neural net-based translation, tree-to-strong translation, pre-ordering, parse trees and parsing, word mapping, phrase-based translation

2022.10.15

アルゴリズム:Algorithmsグラフ理論ベイズ推定最適化:Optimization機械学習:Machine Learning深層学習:Deep Learning確率・統計:Probability and Statistics線形代数:Linear Algebra自然言語処理:Natural Language Processing

Protected: Equivalence of neural networks (deep learning) and Gaussian processes

On the equivalence of Gaussian processes and neural networks, an applied model of stochastic generative models used in digital transformation, artificial intelligence, and machine learning tasks from Neal's paper

2022.10.01

アルゴリズム:Algorithmsベイズ推定微分積分:Calculus最適化:Optimization機械学習:Machine Learning深層学習:Deep Learning確率・統計:Probability and Statistics線形代数:Linear Algebra

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

2021.10.07

機械学習:Machine Learning深層学習:Deep Learning音声信号認識技術