lstm machine learning

Machine learning system Design (Building machines learning Systems with Python)-Willi Richert Luis Pedro Coelho General statementThe book is 2014, after reading only found that there is a second version of the update, 2016. Recommended to read the latest version, the ability to read English version of the proposal, Chinese translation in some places more awkward

Project applicability analysis of main machine learning algorithmsSome time ago Alphago with the Li Shishi of the war and related deep study of the news brush over and over the circle of friends. Just this thing, but also in the depth of machine learning to further expand, and the breadth of

11.1 What to do first11.2 Error AnalysisError measurement for class 11.3 skew11.4 The tradeoff between recall and precision11.5 Machine-Learning data11.1 what to do firstThe next video will talk about the design of the machine learning system. These videos will talk about the major problems you will encounter when desi

Writing programming and writing machine learning modelsBased on the different machine learning models, a large number of characteristic variables are used to predict the fluctuation of the underlying asset price, and the prediction results are evaluated. Machine

This article is part of the third chapter of "Neural networks and deep learning", which describes how to select the value of the initial hyper-parameter in the machine learning algorithm. (This article will continue to add)Learning Rate (learning rate,η)When using the gradie

TensorFlow integrates and implements a variety of machine learning-based algorithms that can be called directly.Supervised learning1) Decision Trees (decision tree)Decision tree is a tree structure, providing people with decision-making basis, decision tree can be used to answer yes and no problem, it through the tree structure of the various situations are represented, each branch represents a choice (sele

steepness factor to these nonlinear functions, adjust the saturation region of the nonlinear function, adjust the shape of the training loss function, and adjust the parameter adjustment out of the saturated area.For the sigmoid function, the steepness factor (recorded as λ) can be set as follows: Δs (x) =1/(1+exp (-x/λ))2.1.4 Using numerical optimization techniquesIn order to improve the convergence speed and stability of neural network training, we can also use the numerical optimization algo

I hear that Hulu machine learning is better than a winter weekend.You can click "Machine Learning" in the menu bar to review all the previous installments of this series and comment on your thoughts and comments.At the same time, in order to make everyone better understand Hulu, the menu "about Hulu" also made the corr

This section describes the core of machine learning, the fundamental problem-the feasibility of learning. As we all know about machine learning, the ability to measure whether a machine learni