Perspectives matters

This document provides an overview of convolutional neural networks (CNNs) and their applications. It discusses the common layers in a CNN like convolutional layers, pooling layers, and fully connected layers. It also covers hyperparameters for convolutional layers like filter size and stride. Additional topics summarized include object detection algorithms like YOLO and R-CNN, face recognition models, neural style transfer, and computational network architectures like ResNet and Inception.

Cheatsheet recurrent-neural-networks

1. Recurrent neural networks (RNNs) allow information to persist from previous time steps through hidden states and can process input sequences of variable lengths. Common RNN architectures include LSTMs and GRUs which address the vanishing gradient problem of traditional RNNs. 2. RNNs are commonly used for natural language processing tasks like machine translation, sentiment classification, and named entity recognition. They learn distributed word representations through techniques like word2vec, GloVe, and negative sampling. 3. Machine translation models use an encoder-decoder architecture with an RNN encoder and decoder. Beam search is commonly used to find high-probability translation sequences. Performance is evaluated using metrics like BLEU score.

Cheatsheet deep-learning-tips-tricks

This document provides tips and tricks for deep learning including data augmentation techniques, batch normalization, training procedures like epochs and mini-batch gradient descent, loss functions like cross-entropy loss, and parameter tuning methods such as transfer learning, adaptive learning rates, dropout, and early stopping. It also discusses good practices like overfitting small batches and gradient checking.

This document provides an overview of key concepts in machine learning including neural networks, convolutional neural networks, recurrent neural networks, reinforcement learning, and control. It defines common neural network components like layers, activation functions, loss functions, and backpropagation. It also explains concepts in convolutional neural networks like convolutional layers and batch normalization. Recurrent neural networks components discussed include different gate types. Reinforcement learning concepts covered are Markov decision processes, policies, value functions, Bellman equations, value iteration algorithm, and Q-learning.

Cheatsheet machine-learning-tips-and-tricks

This document provides an overview of machine learning concepts for assessing model performance including metrics, model selection, and diagnostics. It discusses classification and regression metrics like accuracy, precision, recall, ROC curves, and coefficients of determination. Model selection techniques covered are training/validation/test sets, cross-validation, and regularization. Diagnostics examines bias/variance tradeoffs and remedies for underfitting and overfitting.

Cheatsheet unsupervised-learning

- Unsupervised learning aims to find hidden patterns in unlabeled data. Expectation-maximization and k-means clustering are common unsupervised learning algorithms. - Principal component analysis performs dimension reduction by projecting data onto dimensions that maximize variance. Independent component analysis finds underlying generating sources in data. - This document provides an overview of various unsupervised learning techniques including expectation-maximization, k-means clustering, hierarchical clustering, principal component analysis, and independent component analysis. Formulas and algorithms for each technique are defined.

Cheatsheet supervised-learning

This document provides a summary of supervised learning techniques including linear regression, logistic regression, support vector machines, naive Bayes classification, and decision trees. It defines key concepts such as hypothesis, loss functions, cost functions, and gradient descent. It also covers generative models like Gaussian discriminant analysis, and ensemble methods such as random forests and boosting. Finally, it discusses learning theory concepts such as the VC dimension, PAC learning, and generalization error bounds.

Refresher probabilities-statistics

This document provides an introduction to key concepts in probability and statistics for machine learning. It covers topics such as sample spaces, events, axioms of probability, permutations, combinations, conditional probability, Bayes' rule, random variables, probability distributions, expectations, variance, transformations of random variables, jointly distributed random variables, parameter estimation, and the central limit theorem.

Refresher algebra-calculus

This document provides an overview of key linear algebra and calculus concepts for machine learning, including: 1) Notations for vectors, matrices, and operations like matrix multiplication and transposition. 2) Common matrix properties such as symmetry, positive semi-definiteness, eigenvalues, and singular value decomposition. 3) Derivatives used in calculus on matrices, including the gradient and Hessian of functions with respect to vectors and matrices.

一比一原版(UniSA毕业证)南澳大学毕业证成绩单如何办理

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一比一原版(QUT毕业证)昆士兰科技大学毕业证成绩单如何办理

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