Tag：Perceptron

Time：20201219
By Dorian LazarCompile  VKSource: toward Data Science Perceptron is a component of artificial neural network, which is a simplified model of biological neurons in the brain. Perceptron is the simplest neural network, which consists of only one neuron. The perceptron algorithm was invented by Frank Rosenblatt in 1958. Here is a picture of a […]

Time：2020126
1 multilayer perceptron Definition: multilayer perceptron is to introduce one or more hidden layers into single layer neural network, namely input layer, hidden layer and output layer 2. Activation function of multilayer perceptron If there is no activation function, the multilayer perception opportunity degenerates into a single layer The formula of multilayer perceptron: hidden layer […]

Time：2020916
Tensorflow 1. X deep learning Cookbook Protocol: CC byncsa 4.0 Don’t worry about your image, just about how to achieve your goals. ——Principles, living principles 2.3. C Online reading Apachecn interview and job exchange group 724187166 Apachecn learning resources catalog Tensorflow 1. X deep learning Secrets Preface 1、 Introduction to tensorflow 2、 Return 3、 Neural […]

Time：2020910
First of all, we need to know what the principle of multilayer perceptron is? Multi layer perceptron (MLP) introduces the concept of multilayer neural network The number of the correct weight matrix w should be:256 times 256 times 1000 + 1000 + 10 = 65546000（inputs_ numbers*hidden_ numbers）+hidden_ Numbers times output_ numbers Hidden layer: The following […]

Time：2020827
The notes are reproduced in GitHub project：https://github.com/NLPLOVE/IntroductionNLP 5. Perceptron classification and sequence tagging In Chapter 4, we use hidden Markov model to implement the first Chinese word segmentation based on sequence tagging, but the effect is not satisfactory. In fact, the hidden Markov model assumes that what people say only depends on a hidden {B.M., […]

Time：2020825
The notes are reproduced in GitHub project：https://github.com/NLPLOVE/IntroductionNLP 6. Conditional random fields and sequence labeling This chapter introduces a new sequential tagging model conditional random field. This model belongs to the same family of structured learning as perceptron, but its performance is more powerful than perceptron. In order to clarify the origin and development of the […]

Time：2020822
From linearly separable to linearly nonseparable PLA has three different forms from linear separable to linear non separable. Linearly separable: PLA A little mistake: pocket algorithm Strictly nonlinear: $Φ (x) $+ PLA Next, explain in detail how these three models come from. Evolution of PLA The full name of PLA is perceptron linear algorithm, that […]

Time：2020617
Get the source code of scikit learn project Whether on windows or Linux, you can directly use git cloning project. Before cloning, you need to transfer the official project fork to your GitHub warehouse. Project address: https://github.com/scikitlearn/scikitlearn.git Tools for studying source code Pycharm IDE User guide in official documents Some pre knowledge needed to study […]

Time：2020527
background: I always want to sort out my understanding and thinking of generalized linear model, so I have this essay. premise： 1. First of all, the introduction model will follow theThree elementsTo expand, i.eModel(parameter space of the model),strategy(how to choose the optimal model, generally referred to as cost function / loss function),algorithm(method of model learning […]

Time：2020514
Is it OK not to add bias term to neurons? The answer is, noEveryone knows what bias is in neural network, and since the first perceptron is implemented, everyone knows that neurons need to add bias term. But have you ever thought about why we use the bias term? As far as I’m concerned, I […]

Time：202055
Tensorflow is very convenient to realize the perceptron. Here we first implement a simple example. Here is the simplest example of the perceptron, which can be used as the entrylevel debugging code import tensorflow as tf a = [[1, 1], [1, 0], [0, 1], [0, 0]] b = [[1], [0], [0], [0]] input = 2 […]

Time：2020311
Recently I am reading books related to machine learning. By the way, I will write out the parts I read every day and share them with you. Let’s learn, discuss and make progress together! As the first blog of machine learning, I’m going to start with the perceptron and then update other content slowly. Before […]