Merge pull request #981 from jasonrandrews/spelling

Fixing external link errors

content/install-guides/forge.md

@@ -25,9 +25,9 @@ weight: 1
 [Linaro Forge](https://www.linaroforge.com/) is a server and HPC development tool suite for C, C++, Fortran, and Python high performance code on Linux.
 
 Linaro Forge consists of
-* [Linaro DDT](https://www.linaroforge.com/linaroDdt/) for parallel high-performance application debugging
-* [Linaro MAP](https://www.linaroforge.com/linaroMap/) for performance profiling and optimization advice, and
-* [Linaro Performance Reports](https://www.linaroforge.com/linaroPerformanceReports/) for summarizing and characterizing both scalar and MPI application performance.
+* [Linaro DDT](https://www.linaroforge.com/linaro-ddt/) for parallel high-performance application debugging
+* [Linaro MAP](https://www.linaroforge.com/linaro-map/) for performance profiling and optimization advice, and
+* [Linaro Performance Reports](https://www.linaroforge.com/linaro-performance-reports/) for summarizing and characterizing both scalar and MPI application performance.
 
 ## Supported platforms
 

content/install-guides/py-woa.md

@@ -15,7 +15,7 @@ additional_search_terms:
 minutes_to_complete: 15
 
 ### Link to official documentation
-official_docs: https://www.linaro.org/windows-on-arm/python/
+official_docs: https://www.python.org/doc/
 
 author_primary: Jason Andrews
 

content/learning-paths/embedded-systems/new_debug_targets_armds/dstream.md

@@ -70,6 +70,6 @@ Click `Debug` to connect to the platform, and commence your debug session.
 
 Arm platforms can be very complex, with multiple heterogeneous processors, on-chip power islands, and very high-speed interfaces. The below resources may be helpful if you are experiencing issues configuring for your hardware.
 
-- [Troubleshooting DSTREAM-ST connections](https://developer.arm.com/tools-and-software/embedded/debug-probes/dstream-family/dstream-st/troubleshooting-your-dstream-st-unit)
+- [Troubleshooting DSTREAM-ST connections](https://developer.arm.com/documentation/100892/1-0/Troubleshooting/Troubleshoot-target-connections)
 - [Troubleshooting DSTREAM-PT connections](https://developer.arm.com/documentation/102637)
 - [Help with debugging and tracing targets](https://developer.arm.com/documentation/107551)

content/learning-paths/laptops-and-desktops/llvm_putty/_next-steps.md

@@ -19,11 +19,11 @@ recommended_path: "/learning-paths/laptops-and-desktops/wsl2"
 further_reading:
     - resource:
         title: How to setup Windows on Arm for LLVM development
-        link: https://www.linaro.org/blog/how-to-set-up-windows-on-arm-for-llvm-development/
+        link: https://old.linaro.org/blog/how-to-set-up-windows-on-arm-for-llvm-development/
         type: blog
     - resource:
         title: LLVM - Windows on Arm
-        link: https://www.linaro.org/windows-on-arm/llvm/
+        link: https://linaro.atlassian.net/wiki/spaces/LLVM/overview/
         type: website
 
 # ================================================================================

content/learning-paths/laptops-and-desktops/pinebook-pro/arch.md

@@ -7,7 +7,7 @@ weight: 2
 layout: "learningpathall"
 ---
 
-You can install Arch Linux on the [Pinebook Pro laptop](https://www.pine64.org/pinebook-pro/) and use it for Linux development on Arm. 
+You can install Arch Linux on the [Pinebook Pro laptop](https://pine64.org/devices/pinebook_pro/) and use it for Linux development on Arm. 
 
 ## What should I consider before installing Arch Linux?
 

content/learning-paths/laptops-and-desktops/pinebook-pro/neovim.md

@@ -205,7 +205,7 @@ require("nvim-tree").setup({
 EOF
 ```
 
-Feel free to tweak any settings to your preferences. These were adapted from [Neovim configuration for beginners](https://medium.com/geekculture/neovim-configuration-for-beginners-b2116dbbde84)
+Feel free to tweak any settings to your preferences. These were adapted from [Neovim configuration for beginners](https://medium.com/geekculture/neovim-configuration-for-beginners-part-two-fb3834da91b8)
 
 4. Run the following command inside Neovim to install the plugins
 

content/learning-paths/laptops-and-desktops/win_python/_next-steps.md

@@ -22,7 +22,7 @@ further_reading:
         type: documentation
     - resource:
         title: Windows on Arm now supported in Python 3.11
-        link: https://www.linaro.org/blog/windows-on-arm-now-supported-in-python-3-11-release/
+        link: https://old.linaro.org/blog/windows-on-arm-now-supported-in-python-3-11-release/
         type: blog    
 
 # ================================================================================

content/learning-paths/laptops-and-desktops/win_python/how-to-2.md

@@ -133,5 +133,5 @@ This graph illustrates the computation times and the corresponding performance b
 This learning path walked you through installing native Arm64 Python 3.12 on Windows 11.
 
 You wrote a simple module that applied a fast Fourier transformation (FFT) to a signal and saw the performance improvements that Arm64 Python unlocked. This performance improvement accelerates support for Windows on Arm (WoA). 
-One example is [Linaro's demonstration of porting TensorFlow to Arm64](https://www.linaro.org/blog/setting-up-tensorflow-for-windows-on-arm/) which displays impressive speed improvements and offers tremendous possibilities for AI, data scientists, and researchers reliant on the ease and power of Python
+One example is [Linaro's demonstration of porting TensorFlow to Arm64](https://old.linaro.org/blog/setting-up-tensorflow-for-windows-on-arm/) which displays impressive speed improvements and offers tremendous possibilities for AI, data scientists, and researchers reliant on the ease and power of Python
 

content/learning-paths/servers-and-cloud-computing/keras-core/what-is-keras.md

@@ -30,8 +30,6 @@ The Keras API was integrated in TensorFlow (under `tf.keras`) and Keras Core
 API compatibility was maintained. This means that if you have a model that uses
 Keras in TensorFlow, you can also use the model with PyTorch and JAX.
 
-For more information, [read this article](https://keras.io/keras_core/announcement/).
-
 ## Keras model life-cycle overview
 
 The life-cycle of a Keras model has the following steps:
@@ -127,9 +125,9 @@ The default backend is tensorflow.
 The compilation step in Keras configures the model for the training step.
 
 During the compilation step you define:
-* The optimizer: possible values are `SGD` (Gradient Descend), `Adam`, and `RMSProp`. Available optimizers: https://keras.io/keras_core/api/optimizers/
-* The loss function: possible values are `Categorical Cross-entropy` and `Mean Squared Error`. Available loss functions: https://keras.io/keras_core/api/losses/
-* The metrics to guide training: possible values are `Accuracy` and `Crossentropy`. Available metrics: https://keras.io/keras_core/api/metrics/
+* The optimizer: possible values are `SGD` (Gradient Descend), `Adam`, and `RMSProp`. Available optimizers: https://keras.io/api/optimizers/
+* The loss function: possible values are `Categorical Cross-entropy` and `Mean Squared Error`. Available loss functions: https://keras.io/api/losses/
+* The metrics to guide training: possible values are `Accuracy` and `Crossentropy`. Available metrics: https://keras.io/api/metrics/
 
 Below is an example of compiling a model:
 
@@ -151,7 +149,7 @@ A dataset consists of data needed for training, evaluation, and testing of the m
 {{% notice Note %}}
 Datasets are very specific to a model or an application. You need to have full
 knowledge of the model input layout as it needs to be compatible with the
-dataset structure. Keras provides some common datasets for experimentation, e.g., MNIST, CIFAR10, CIFAR100, and IMDB. The full list of datasets provided by Keras is available here - https://keras.io/keras_core/api/datasets/ 
+dataset structure. Keras provides some common datasets for experimentation, e.g., MNIST, CIFAR10, CIFAR100, and IMDB. The full list of datasets provided by Keras is available here - https://keras.io/api/datasets/ 
 {{% /notice %}}
 
 As part of the Machine Learning development workflow, it is common to partition the dataset

content/learning-paths/servers-and-cloud-computing/sve/_next-steps.md

@@ -6,9 +6,9 @@
 next_step_guidance: >
     As a next step, you may be interested in learning how to use development tools for SVE:
 
-    - For debug tools refer to: [Arm Development Studio](https://developer.arm.com/Tools%20and%20Software/Arm%20Development%20Studio) or [Linaro DDT, part of Linaro Forge](https://www.linaroforge.com/linaroDdt)
+    - For debug tools refer to: [Arm Development Studio](https://developer.arm.com/Tools%20and%20Software/Arm%20Development%20Studio) or [Linaro DDT, part of Linaro Forge](https://www.linaroforge.com/linaro-ddt)
 
-    - For profiling tools refer to: [Arm Streamline](https://developer.arm.com/Tools%20and%20Software/Streamline%20Performance%20Analyzer) or [Linaro MAP, part of Linaro Forge](https://www.linaroforge.com/linaroMap)
+    - For profiling tools refer to: [Arm Streamline](https://developer.arm.com/Tools%20and%20Software/Streamline%20Performance%20Analyzer) or [Linaro MAP, part of Linaro Forge](https://www.linaroforge.com/linaro-map)
 
 If you are interested in migrating applications to Arm servers, follow:
 # 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.