Merge pull request #1267 from ArmDeveloperEcosystem/main

Production update

Author: jasonrandrews

.wordlist.txt

@@ -3114,4 +3114,77 @@ ifunc
 ifuncs
 lm
 memcpy
-multiversioning
+multiversioning
+AwsServerlessDynamoDbLambdaS
+BLASes
+BVH
+Bindless
+Calvo
+Chowdary
+CloudFormation
+GDC
+GLSL
+Helpbox
+Lista
+LttE
+Malhotra
+Mandepudi
+Nikhil
+OPLTK
+Rasterization
+Ravi
+Refractions
+Rohr
+RunsOn
+SSR
+Streamlit
+TLASes
+TorchAO
+Torchchat
+Vulkan's
+Vulkanised
+WebsiteBucket
+aaaa
+barycentric
+bindless
+bonza
+calvo
+chatbot's
+createResponse
+denoising
+getAverageTemperatureButton
+getAverageTemperatureUrl
+jJyHzkWXEfY
+lista
+param
+prismjs
+quicktool
+rasterization
+rasterized
+refractions
+renderer
+skybox
+specular
+ssr
+streamlit
+stylesheet
+torchchat
+tps
+uQ
+vulkan
+vulkanised
+writeTemperaturesUrl
+IPython
+NeuralNetwork
+Sigmoid
+Softmax
+Tanh
+backpropagation
+diskio
+feedforward
+logits
+prem
+softmax
+subclassing
+tanh
+torchsummary

content/install-guides/acfl.md

@@ -80,7 +80,7 @@ You are now ready to install ACfL [manually](#manual) or with [Spack](#spack).
 Use an Arm recommended script to select, download, and install your preferred `ACfL` package.
 
 ```console
-bash <(curl -L https://developer.arm.com/-/media/Files/downloads/hpc/arm-compiler-for-linux/install.sh)
+bash <(curl -L https://developer.arm.com/-/cdn-downloads/permalink/Arm-Compiler-for-Linux/Package/install.sh)
 ```
 
 ## Download and install manually {#manual}

content/learning-paths/cross-platform/eigen-linear-algebra-on-arm/_next-steps.md

@@ -8,10 +8,6 @@ further_reading:
         title: Eigen official Tutorial on Matrix class
         link: https://libeigen.gitlab.io/docs/group__TutorialMatrixClass.html
         type: documentation
-    - resource:
-        title: CS Tutorial on Eigen
-        link: https://cs2240.graphics/assignments/eigen_tutorial.pdf
-        type: documentation
     - resource:
         title: Eigen Webinar from Linaro
         link: https://static.linaro.org/connect/webinars/presentations/Eigen_Webinar_3.pdf

content/learning-paths/cross-platform/pytorch-digit-classification-architecture/Figures/01.png

@@ -0,0 +1,46 @@
+---
+title: Create a PyTorch model for digit classification
+draft: true
+cascade:
+    draft: true
+
+minutes_to_complete: 40
+
+who_is_this_for: This is an introductory topic for software developers interested in learning how to use PyTorch to create a feedforward neural network for digit classification. 
+
+learning_objectives:
+    - Prepare a PyTorch development environment.
+    - Understand the MNIST digit dataset.
+    - Create a neural network architecture using PyTorch.
+
+prerequisites:
+    - Any computer which can run Python3 and Visual Studio Code, this can be Windows, Linux, or macOS.
+
+author_primary: Dawid Borycki
+
+### Tags
+skilllevels: Introductory
+subjects: ML
+armips:
+    - Cortex-A
+    - Cortex-X
+    - Neoverse
+operatingsystems:
+    - Windows
+    - Linux
+    - macOS
+tools_software_languages:
+    - Android Studio
+    - Coding
+shared_path: true
+shared_between:
+    - servers-and-cloud-computing
+    - laptops-and-desktops
+    - smartphones-and-mobile
+
+### FIXED, DO NOT MODIFY
+# ================================================================================
+weight: 1                       # _index.md always has weight of 1 to order correctly
+layout: "learningpathall"       # All files under learning paths have this same wrapper
+learning_path_main_page: "yes"  # This should be surfaced when looking for related content. Only set for _index.md of learning path content.
+---

content/learning-paths/cross-platform/pytorch-digit-classification-architecture/Figures/02.png

@@ -0,0 +1,43 @@
+---
+# ================================================================================
+#       Edit
+# ================================================================================
+
+next_step_guidance: >
+   Proceed to Get Started with Arm Performance Studio for mobile to continue learning about Android performance analysis.
+
+# 1-3 sentence recommendation outlining how the reader can generally keep learning about these topics, and a specific explanation of why the next step is being recommended.
+
+recommended_path: "/learning-paths/smartphones-and-mobile/ams/"
+
+# Link to the next learning path being recommended(For example this could be /learning-paths/servers-and-cloud-computing/mongodb).
+
+
+# further_reading links to references related to this path. Can be:
+    # Manuals for a tool / software mentioned   (type: documentation)
+    # Blog about related topics                 (type: blog)
+    # General online references                 (type: website) 
+
+further_reading:
+    - resource:
+        title: PyTorch
+        link: https://pytorch.org
+        type: documentation    
+    - resource:
+        title: MNIST
+        link: https://en.wikipedia.org/wiki/MNIST_database
+        type: website
+    - resource:
+        title: Visual Studio Code
+        link: https://code.visualstudio.com
+        type: website
+
+
+
+# ================================================================================
+#       FIXED, DO NOT MODIFY
+# ================================================================================
+weight: 21                  # set to always be larger than the content in this path, and one more than 'review'
+title: "Next Steps"         # Always the same
+layout: "learningpathall"   # All files under learning paths have this same wrapper
+---

content/learning-paths/cross-platform/pytorch-digit-classification-architecture/Figures/03.png

@@ -0,0 +1,48 @@
+---
+# ================================================================================
+#       Edit
+# ================================================================================
+
+# Always 3 questions. Should try to test the reader's knowledge, and reinforce the key points you want them to remember.
+    # question:         A one sentence question
+    # answers:          The correct answers (from 2-4 answer options only). Should be surrounded by quotes.
+    # correct_answer:   An integer indicating what answer is correct (index starts from 0)
+    # explanation:      A short (1-3 sentence) explanation of why the correct answer is correct. Can add additional context if desired
+
+
+review:
+    - questions:
+        question: >        
+            Does the input layer of the model flatten the 28x28 pixel image into a 1D array of 784 elements?
+        answers:
+            - "Yes"
+            - "No"
+        correct_answer: 1
+        explanation: >
+            Yes, the model uses nn.Flatten() to reshape the 28x28 pixel image into a 1D array of 784 elements for processing by the fully connected layers.
+    - questions:
+        question: >
+            Does the model use dropout layers with a 20% dropout rate after each hidden layer?
+        answers:
+            - "Yes"
+            - "No"
+        correct_answer: 1
+        explanation: >
+            Yes, the model applies dropout layers after each hidden layer, randomly setting 20% of the neurons to 0 during training to prevent overfitting. 
+    - questions:
+        question: >
+            Will the model make random predictions if it’s run before training?
+        answers:
+            - "Yes"
+            - "No"
+        correct_answer: 1
+        explanation: >
+            Yes, however in such the case the model will produce random outputs, as the network has not been trained to recognize any patterns from the data. 
+
+# ================================================================================
+#       FIXED, DO NOT MODIFY
+# ================================================================================
+title: "Review"                 # Always the same title
+weight: 20                      # Set to always be larger than the content in this path
+layout: "learningpathall"       # All files under learning paths have this same wrapper
+---