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Automating deployments on Proxmox with OpenTofu and cloud-init
04.12.2024
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Rubén Llorente ... " { 11 endpoint = "https://192.168.3.15:8006/" 12 username = "root@pam" 13 password = "proxmox" 14 insecure = true 15 tmp_dir = "/var/tmp" 16 17 ssh { 18 agent = true 19 } 20
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Introducing the NoSQL MongoDB database
04.12.2024
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Photo by Wesley Tingey on Unsplash
Capacity": "883cc" 15 } 16 { 17 "type": "Bicycle", 18 "brand: "Canyon, 19 "model": "Ultimate CF SLX", 20 "year": 2022, 21 "frameMaterial": "Carbon", 22 "gears": 22 23 } A vehicle of one type has
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Getting started with AI
26.01.2025
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version isn't even 3.x. I haven't tested the code with Keras 3.x yet, so your mileage may vary if you go that route. CIFAR-10 The model and dataset I use is CIFAR-10 [6]. It is a very common dataset
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Keras: Getting Started with AI
03.12.2024
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, MaxPooling 2D : model.add(layers.BatchNormalization()) model.add(layers.MaxPooling2D(pool_size=(2,2))) A max pooling layer has a pool size, 2x2 in this case, that is used to scan the entire input image (left
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Code IaC for provisioning of preconfigured virtual environments
26.01.2025
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.DevCenter/devcenters/attachednetworks@2024-02-01' = { 18 name: networkConnection.name 19 parent: devcenter 20 ... } 21 22 resource devcenterGalleryImage 'Microsoft.DevCenter/devcenters/galleries/images@2024-02-01' existing = { 23
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Web applications with Julia
26.01.2025
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c = Meta.parse(JSON.parse(msg)["c"]) 12 t = Meta.parse(JSON.parse(msg)["t"]) 13 f1(x) = cos(c * x) 14 f2(x) = t * tan(x) 15 f(x) = f1(x) - f2(x) 16 z = round