#mnist

[samejack]

Keras 教學 - 機器學習與類神經網路

Keras 教學 – 機器學習與類神經網路

最近在研究 Keras 與深度學習，順便記錄一下學習的過程 (完整 Keras 投影片)。現在網路上已經有很多的資源可以幫助我們進行 Machine Learning，像是 GPU 可以用 CoLab 提供的 P100 來訓練，足夠應付一般的實驗工作。如果要訓練比較龐大的影像資料，還是自己買 GPU 或租用雲端 MaaS 服務吧，不然免費的 CoLab 穩定性不太夠，而且只有一顆 GPU。因為學習心得有點多，內容可能會分幾篇文章進行介紹。廢話說完，那就先開始吧…… (more…)

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[mateusz8005]

DCGAN training with MNIST data (10 epochs).

Implementation: https://github.com/mateusz800/GAN_playground/tree/master/DCGAN

[yasudai]

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#プログラミング勉強中 #画像データ #mnist #dataset #パイソン3 #programming #python3 #jupyternotebook https://www.instagram.com/p/B_g0hJggElk/?igshid=jndmi5k9coxc

[mobinmirarab]

La base de données MNIST pour Modified ou Mixed National Institute of Standards and Technology, est une base de données de chiffres écrits à la main. C'est un jeu de données très utilisé en apprentissage automatique La reconnaissance de l'écriture manuscrite est un problème difficile, et un bon test pour les algorithmes d'apprentissage. La base MNIST est devenu un test standard. Elle regroupe 60000 images MNIST دیتاست ارقام دست نویس انگلیسی ۶۰هزار تصویر تشخیص هر یک از اعداد به کمک الگوریتم های هوش مصنوعی، مجموعه داده بسیار پر کاربرد در یادگیری ماشین است. #mnist #datascience #machinelearning #deeplearning #artificial_intelligence #bigdata #python #datascientist #dataanalysis #data #programmer #coding #artificialneuralnetwork #algorithm #sql https://www.instagram.com/p/B4KG7AFArfu/?igshid=z39afvj1uh9x

[petaminds]

Save, restore, visualise Graph with TensorFlow v2.0 & KERAS

Save, restore, visualise Graph with TensorFlow v2.0 & KERAS

TensorFlow 2.0 is coming really soon. Therefore, we quickly show some useful features, i.e., save and load a pre-trained model, with v.2 syntax. To make it more intuitive, we will also visualise the graph of the neural network model.

Benefits of saving a model

Quick answer: to save time, easy-share, and fast deploy.

A SavedModel contains a complete TensorFlow program, including weights…

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[whitehat-solutions]

Neural Networks (E01: introduction)

Hello everyone, in this series we explore machine learning with neural networks. In particular, so that our neural network recognizes handwritten digits. In this first video I want to introduce neural networks a simple example. Imagine we have two kinds of plants. One with red flowers and the other with purple. And one day we decide to measure the length and width of their petals. For simplicity, I call these properties x1 and x2 Because we have only two properties, we can distribute the data in two dimensions. We see red and purple flowers created two separate groups. We can draw a line that divides them. This is called "decision boundaries". If we get new data and are on one side of the border, we predict that it is a red flower. On the other hand, it would be purple. We want to calculate this boundary after our neural network. We will start by creating a simple network. It will have two input neurons x1, x2 for our two properties. And two output neurons that show probability, that the flower is red or purple. Then we can join the input neurons with the first output. And the forces (or weights) of these connections will be w1 and w2. Then r, the probability that the flower will be red, is given by the equation x1 times w1 + x2 times w2. We can also connect the second output neuron with input, to calculate p, which is equal to x1 times w3 + x2 times w4. Back to the chart - where r is greater than red and where p is greater than r violet. The weight parameter determines the slope "decision boundaries". To properly separate red flowers from purple, the boundary must be capable of vertical displacement. Back to our network. Add a slider to each output neuron. Scales and sliders together they can reliably divide the data. However, there is a great limitation. Our boundary is always straight. For this example, this is fine, but now I will show something, what can not be divided by a straight line. For this we will need a more advanced network. We will insert in our network a new layer, called the "hidden layer". We can join the first hidden neuron to the input layer and get a1, equal x1 * w1 + x2 * w2 + b1, which is our old r. We can do the same for the other two hidden neurons, to get a2 and a3. Next, we connect the first output neuron to the hidden layer. We get the output a1 * w7 + a2 * w8 + and a3 * w9 + b4. The same for the second output neuron. Now we have more scales and sliders, with whom I can play. But I can still just create straight lines. That's because it's all there is we add linear equations, resulting in other linear equations. So we have to find a way to create a nonlinear one. One solution is to use some sigmoid function. You can see this on the screen For small values it is functional value zero, for large values, the value is one. We will connect this to our network by hiding hidden values and the output layer with this function. By the way, this is called activation function. now a change in the weight of a single neuron changes the slope of the activation function and shifting the scroll bar function to the sides. In the graph of the activation function she was able to create a curve. With the right value of scales and sliders can be divided red and purple flowers. Of course, I now change my values manually, but the sense of the neural network is to arrive at the correct values automatically. You can see it here. Let's see how this works a bit later in this series. For now, it is important to understand, how weights and sliders decide which areas red and blue purple flowers in this example, in order to classify new data, when we do not know where it belongs. Note that we only work with two properties. That is why we work in two dimensions. If we had a third property, it is not difficult to imagine the border or surface, if you want, in three dimensions. With four or more features it's not so easy to introduce yourself, but the concept remains the same. I hope this video helped you understand the structure of the neural network with input, hidden and output layers, weights, sliders and activation functions, and general understanding, what the network is learning to divide, specifically the decision boundary. In the next work, we will create a network in Python, so far, hello..

[onlinecourses4all-blog]

Building your own handwritten digit recognition system using MNIST dataset in Python

Read More:-   bit.ly/2Ab8VHF

[ayn]