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starting and re-making function ctrl+shfit+alt+t playback and re-systemCtrl+l Color Scale tool ctrl+shfit+l automatic color scale ctrl+m curveCtrl+b color balance ctrl+u hue saturation ctrl+shfit+u de-color (black and white)Ctrl+shfit+[] quickly tune into a layer top and endAI shortcut KeysCTRL + N new canvasCtrl+d Mobile Re-system (and PS(Remove the selection inPS ) is not the same, but also to distinguish the PS in the re-system function, I often confused. Ctrl + Z Infinite Recovery on
Ai's general personal understanding is that all human wisdom manifests itself in the behavior of new technologies, such as robots. The use of technology "machines" to replace human activities. The specific goals are divided into the following three: theoretical analysis of the effective interpretation of intelligent behavior, interpretation of human intelligence, construction of intelligent artifacts. A machine without blood, without a body, can perceive things around him as human beings, and ca
Classification and logistic regression (classification and logistic regression)Http://www.cnblogs.com/czdbest/p/5768467.htmlGeneralized linear model (generalized Linear Models)Http://www.cnblogs.com/czdbest/p/5769326.htmlGenerate Learning Algorithm (generative learning algorithms)Http://www.cnblogs.com/czdbest/p/5771500.htmlClassification and logistic regression
nodes on the node on behalf of a variety of fractions, example to get the classification result of Class 1The same input is transferred to different nodes and the results are different because the respective nodes have different weights and biasThis is forward propagation.10. MarkovVideoMarkov Chains is made up of state and transitionsChestnuts, according to the phrase ' The quick brown fox jumps over the lazy dog ', to get Markov chainStep, set each word to a state, and then calculate the prob
distributed computing of heterogeneous devices, which can automate models on a variety of platforms, from mobile phones to individual cpu/gpu to hundreds of GPU cards distributed systems. From the current documentation, TensorFlow supports the CNN, RNN, and lstm algorithms, which are the most popular deep neural network models currently in Image,speech and NLP. Open source meaning this time Google Open source depth
understand the task, so "save the Earth" to understand "kill all human beings." This is like a typical predictive algorithm that literally understands the task and ignores the other possibilities or the practical significance of the task.So, in January 2016, Harvard Business School professor Michael Luca, professor of economics Sendhil Mullainathan, and Cornell University professor Jon Kleinberg, published an article titled "Algorithm and Butler" in the Harvard Commercial Review. Call upon the
In recent years, with the rise of big data, cloud computing, mobile Internet, artificial intelligence technology, "machine learning" has become a hot term in the industry. From the field of communication Internet experts, to a variety of enterprises, and even ordinary people, the "machine learning" technology knows. So what exactly is machine learning, and what d
Learning Algorithms from scratch: 10 sorting algorithms (medium)Author: matrix67 Date: 2007-04-06 font size: small, medium, and large. This article is divided into four sections by the gorgeous split line. For the O (nlogn) sorting algorithm, we will introduce Merge Sorting in detail and prove the time complexity of Merge Sorting. Then we will briefly introduce h
Data Structure Learning notes (I) Basic concepts and analysis algorithms and basic concepts of Algorithms The efficiency of the solution is related:Data Organization (bookshelves)Space Utilization (recursion and non-recursion)Algorithm used to solve the problem What is an algorithm: a data object must be associated with a series of operations added to it, and th
similarity of form and function. Both of these methods are useful.Learning Style Based on experience, environment, or any interaction we call input data, an algorithm can model a problem in different ways. In machine learning and AI textbooks, the popular approach is to first consider an algorithmic learning style. The main
Generate learning Algorithms This course outline: 1. Generate learning Algorithms 2. Gaussian discriminant analysis (Gda,gaussian discriminant) - Gaussian distribution (brief) - Contrast Generation learning Algorithm discriminant Learni
Learning notes for "Machine Learning Practice": Implementation of k-Nearest Neighbor algorithms, and "Machine Learning Practice" k- The main learning and research tasks of the last semester were pattern recognition, signal theory, and image processing. In fact, these field
Reinforcement LearningThe solution to the problem of control decision: to design a return function (reward functions), if the learning agent (such as the above four-legged robot, chess AI program) in the decision of a step, to obtain a better result, Then we give the agent some return (such as the return function result is positive), get poor results, then the return function is negative. For example, a qua
Original: http://blog.csdn.net/suipingsp/article/details/41645779Support Vector machines are basically the best supervised learning algorithms, because their English name is SVM. In layman's terms, it is a two-class classification model, whose basic model is defined as the most spaced linear classifier on the feature space, and its learning strategy is to maximiz
, using the sample to match the malignant tumor model and benign tumor model, to see which model matching better, the prognosis is malignant or benign.This approach is to generate learning algorithms.Definitions of two learning algorithms:1) discriminant Learning algorithm:-Direct
In this article we will outline some popular machine learning algorithms.Machine learning algorithms are many, and they have many extensions themselves. Therefore, how to determine the best algorithm to solve a problem is very difficult.Let us first say that based on the learning approach to the classification of the
Support Vector machines are basically the best supervised learning algorithms, because their English name is SVM. In layman's terms, it is a two-class classification model, whose basic model is defined as the most spaced linear classifier on the feature space, and its learning strategy is to maximize the interval and finally transform it into a convex two-time pr
Learning notes for "Machine Learning Practice": two application scenarios of k-Nearest Neighbor algorithms, and "Machine Learning Practice" k- After learning the implementation of the k-Nearest Neighbor Algorithm, I tested the k-Nearest Neighbor Algorithm by referring to th
A bunch of online searches, and finally the links and differences between these concepts are summarized as follows: 1. Data mining: Mining is a very broad concept. It literally means digging up useful information from tons of data. This work bi (business intelligence) can be done, data analysis can be done, even market operations can be done. Using Excel to analyze the data and discover some useful information, the process of guiding your business through this information is also the process of
specific flow of the Lle algorithm is as follows (source: machine Learning Zhou Zhihua version) Lle Algorithm Summary:Key Benefits:1) can learn the local linear low-dimensional manifold of any dimension2) The algorithm comes down to the sparse matrix feature decomposition, the computational complexity is relatively small, the realization is easy.3) can deal with non-linear data, can be non-linear dimensionality reductionMain disadvantages:1) The f
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