人工神经网络方法学习步长
内容 (Contents)
- Artificial Neural Network 人工神经网络
- Activation functions 激活功能
- Loss functions 损失函数
人工神经网络 (Artificial Neural Network)
The human brain is the most sophisticated of all supercomputers.An artificial neural network (ANN) is a technique designed to simulate the way the human brain analyzes and processes information. As a human brain learns through experiences so does an ANN . An ANN has self learning capabilities ie. as more and more data becomes available an ANN can improve its’ predictive/modelling capabilities.
人脑是所有超级计算机中最复杂的。人工神经网络(ANN)是一种旨在模拟人脑分析和处理信息的方式的技术。 当人类的大脑通过经验学习时，人工神经网络也是如此。 人工神经网络具有自我学习的能力，即。 随着越来越多的数据变得可用，人工神经网络可以提高其预测/建模能力。
Artificial neural networks are designed to function like the human brain, with neuron nodes interconnected like a web.
人工神经网络被设计为像人的大脑一样运作，神经元节点像网络一样相互连接。
An ANN has hundreds or thousands of artificial neurons called processing units, which are interconnected by nodes. These processing units are composed of input and output units. The input units receive various forms and structures of information based on an internal weighting system, and the neural network attempts to learn about the information presented to produce one output report.
一个人工神经网络有数百或数千个称为处理单元的人工神经元，它们通过节点相互连接。 这些处理单元由输入和输出单元组成。 输入单元基于内部加权系统接收各种形式和结构的信息，而神经网络则试图了解所呈现的信息以生成一份输出报告。
Just like humans need a set of rules and guidelines to process information into a result, ANNs are programmed with a set of learning rules called backpropagation, (backward propagation of error), to improve their output results.
就像人类需要一套规则和准则来将信息处理成结果一样，人工神经网络也被编程有一套称为反向传播(错误的向后传播)的学习规则，以改善其输出结果。
An ANN initially goes through a training phase where it learns to recognize patterns in data, whether visually, aurally, or textually. During this supervised phase, the network compares its actual output produced with what it was meant to produce — the desired output. The difference between both outcomes is adjusted using backpropagation. This means that the network works backward, going from the output unit to the input units to adjust the weight of its connections between the units until the difference between the actual and desired outcome produces the lowest possible error.
人工神经网络最初会经历一个训练阶段，在该阶段中，它将学会从视觉，听觉或文本方面识别数据中的模式。 在此监督阶段，网络将其实际产生的输出与预期产生的输出(期望的输出)进行比较。 两种结果之间的差异可通过反向传播进行调整。 这意味着网络会反向工作 ，从输出单元到输入单元，以调整其在单元之间的连接权重，直到实际结果与期望结果之间的差值产生最小的错误为止。
Let us deep dive into what exactly is an ANN structure!!!
让我们深入了解什么是人工神经网络结构！！！
ANN Structure
人工神经网络结构
The above structure represent an ANN in it’s most basic forms also called as a Perceptron
上面的结构以最基本的形式表示了人工神经网络，也称为Perceptron
A set of inputs denoted as {x1, x2 ,…..xm) each fed into its own connection with a weight denoted as (w1, w2 ,………wm).Every connection has a weight attached which may have either a positive or a negative value associated with it. The neuron sums all the signals it receives, with each signal being multiplied by its associated weights on the connection.
一组表示为{x1，x2，..... xm)的输入，每个输入馈入其自己的连接，权重表示为(w1，w2，...... wm)。每个连接都附加有权重，该权重可以为正或与此相关的负值。 神经元将接收到的所有信号相加，每个信号 乘以其在连接上的关联权重。
This output is then passed through a transfer /activation function, g(y), that is normally non-linear to give the final output ..
然后，此输出通过传递/激活函数g(y)传递，该函数通常是非线性的，以给出最终输出..
The back-propagation ANN is a feed-forward neural network structure that takes the input to the network and multiplies it by the weights on the connections between neurons or nodes; summing their products before passing it through a threshold function to produce an output. The back-propagation algorithm works by minimizing the error between the output and the target (actual) by propagating the error back into the network. The weights on each of the connections between the neurons are changed according to the size of the initial error. The input data are then fed forward again, producing a new output and error. The process is reiterated until an acceptable minimized error is obtained. Each of the neurons uses a transfer/activation function and is fully connected to nodes on the next layer. Once the error reaches the desired value, the training is stopped. The final model is thus a function that is a internally representing of the output in terms of the inputs at that point. A more detailed discussion of the back-propagation algorithm will be carried out in upcoming articles.
反向传播ANN是前馈神经网络结构 ，它将输入输入到网络，并将其乘以神经元或节点之间连接的权重。 对它们的乘积求和，然后再将其通过阈值函数以产生输出。 反向传播算法通过将错误传播回网络来最小化输出和目标(实际)之间的错误。 神经元之间每个连接的权重根据初始误差的大小而变化。 输入数据然后再次被前馈，产生新的输出和错误。 重复该过程，直到获得可接受的最小化误差为止。 每个神经元都使用转移/激活功能，并完全连接到下一层的节点。 一旦误差达到期望值，训练就会停止。 因此，最终模型是一个函数，该函数根据该点的输入在内部表示输出。 反向传播算法的更详细讨论将在以后的文章中进行。
激活功能： (Activation functions:)
( Kindly re-read this topic post covering all the posts as it contains terms which would be explained later but however covering these here since it wou
