Intensive reading of the paper: Bitcoin blockchain Satoshi Nakamoto is actually a paper? What was said?

Welcome to my channel, where I will explain the most classic or cutting-edge models of machine learning and deep learning. I will also talk about how to live in the United States, how to find a job, how to brush LeetCode, and how to quickly integrate into society. If you like it, remember to subscribe and like! If you have anything you want to hear, leave a comment below!

Current tutorial list:
Linear regression (LR), logistic regression (LogR), polynomial regression (PR), Lasso regression, Ridge regression, elastic network (Elastic Net), decision tree (DT), random forest (RF), gradient boosting tree (GBT), XGBoost, LightGBM, CatBoost, Support Vector Machine (SVM), Naive Bayes (NB), K Nearest Neighbor (KNN), Principal Component Analysis (PCA), Independent Component Analysis (ICA), Linear Discriminant Analysis (LDA), t -Distributed Neighbor Embedding (t-SNE), Gaussian Mixture Model (GMM), Cluster Analysis (CA), K-means Clustering (K-means), DBSCAN, HDBSCAN, Hierarchical Clustering (HC), GAN (Generative Adversarial Network ), CGAN, DCGAN, WGAN (Wasserstein GAN), StyleGAN, CycleGAN, VAE (variational autoencoder), GPT (generative pre-training model), BERT, Transformer, LSTM (long short-term memory network), GRU (gated Recurrent unit), RNN (Recurrent Neural Network), CNN (Convolutional Neural Network), AlexNet, VGG, GoogLeNet, ResNet, MobileNet, EfficientNet, Inception, DeepDream, Deep Belief Network (DBN), Autoencoder (AE), Reinforcement Learning (RL), Q-learning, SARSA, DDPG, A3C, SAC, Temporal Differential Learning (TD), Actor-Critic, Adversarial Training, Gradient Descent (GD), Stochastic Gradient Descent (SGD), Batch Gradient Descent (BGD), Adam, RMSprop, AdaGrad, AdaDelta, Nadam, Cross-Entropy Loss, Mean Squared Error Loss, KL Divergence Loss, Hinge Loss function, perceptron, RBF neural network, Hopfield network, Boltzmann machine, deep reinforcement learning (DRL), self-supervised learning, transfer learning, generalized adversarial network (GAN) , Adversarial Generative Network (GAN), Trained Generative Network (TGAN), CycleGAN, Deep Learning Generative Model (DLGM), Autoencoder Generative Adversarial Network (AEGAN), Distributed Autoencoder (DAE), Network Activation Optimizer (NAO) ), Autoencoder (Autoencoder), VQ-VAE, LSTM-VAE, Convolutional Autoencoder (CAE), GAN Autoencoder (GANAE), U-Net, Deep Q Network (DQN), Dual DQN (DDQN) , Prioritized Experience Replay DQN (Prioritized Experience Replay DQN), Multi-agent DQN (Multi-agent DQN), Deep Deterministic Policy Gradient (DDPG), Perceptron (Perceptron), Sparse Autoencoder (SAE), Sparse Representation Classification (SRC) ), Deep Belief Network (DBN), Support Vector Machine (SVM), Ensemble Learning, Random Forest, Extreme Gradient Boosting Tree (XGBoost), AdaBoost, Gradient Boosting Machine, Stacking , Bayesian Optimization, Bayesian Network, EM algorithm (Expectation-Maximization Algorithm), Gaussian Process, Markov Chain Monte Carlo (MCMC), reinforcement learning ( Reinforcement Learning), Unsupervised Learning, Semi-supervised Learning, Supervised Learning, Transfer Learning, Dimensionality Reduction, Feature Selection ), Feature Extraction, Regularization, Normalization, Clustering, Classification, Regression, Dimensionality Reduction, Feature Mapping, Neural Network, Neuron, Activation Function, Loss Function, Optimizer, Learning Rate, Batch Size, Number of Iterations (Epoch), hyperparameter (Hyperparameter), model evaluation (Model Evaluation), cross validation (Cross Validation), confusion matrix (Confusion Matrix), ROC curve (ROC Curve), AUC value (AUC Value), precision (Precision) , Recall, F1 Score, Model Interpretability, Feature Importance, Local Explanation, Global Explanation, Machine Learning Pipeline ), one-click model generation (AutoML), hyperparameter optimization (Hyperparameter Tuning), FFT, Laplace transform, z transform, Fourier transform, short-time Fourier transform (STFT), IIR, FIR, Kalman Filtering, DIP algorithm, wavelet transform