Merge remote-tracking branch 'MicrosoftDocs/master'

Author: timeyoutakeit

.openpublishing.publish.config.json

@@ -1,22 +1,6 @@
 {
   "build_entry_point": "docs",
   "docsets_to_publish": [
-    {
-      "docset_name": "b362ae64-b1c1-4f4d-a0af-76fe2cfdbd70",
-      "build_source_folder": "b362ae64-b1c1-4f4d-a0af-76fe2cfdbd70",
-      "build_output_subfolder": "b362ae64-b1c1-4f4d-a0af-76fe2cfdbd70",
-      "locale": "en-us",
-      "monikers": [],
-      "moniker_ranges": [],
-      "open_to_public_contributors": false,
-      "type_mapping": {
-        "Conceptual": "Content",
-        "ManagedReference": "Content",
-        "RestApi": "Content"
-      },
-      "build_entry_point": "docs",
-      "template_folder": "_themes"
-    },
     {
       "docset_name": "connected-environment",
       "build_source_folder": "connected-environment",
@@ -129,11 +113,11 @@
     ]
   },
   "need_generate_pdf_url_template": true,
-  "Targets": {  
-    "Pdf": {  
-    "template_folder": "_themes.pdf"  
-    }  
-   },
+  "targets": {
+    "Pdf": {
+      "template_folder": "_themes.pdf"
+    }
+  },
   "need_generate_pdf": false,
   "need_generate_intellisense": false
 }

.openpublishing.redirection.json

@@ -57,9 +57,9 @@
     - name: MSBuild
       tocHref: /visualstudio/msbuild/
       topicHref: /visualstudio/msbuild/msbuild
-    - name: Nodejs
-      tocHref: /visualstudio/nodejs/
-      topicHref: /visualstudio/nodejs/tutorial-nodejs
+    - name: Node.js
+      tocHref: /visualstudio/javascript/
+      topicHref: /visualstudio/javascript/tutorial-nodejs
     - name: Profiling
       tocHref: /visualstudio/profiling/
       topicHref: /visualstudio/profiling/index

b362ae64-b1c1-4f4d-a0af-76fe2cfdbd70/TOC.md

@@ -4,17 +4,13 @@ title: Visual Studio Tools for AI
 metadata:
   document_id: 83ceabb5-fb4f-a709-3fd2-37d024409f31
   title: Visual Studio Tools for AI | Microsoft Docs
-  meta.description:
-  services: ai-tools-visual-studio
+  ms.prod: visual-studio-dev15
+  ms.technology: vs-ai-tools
   author: lisawong19
+  ms.author: liwong
   manager: routlaw
-  ms.service: visual studio
-  ms.tgt_pltfrm: na
-  ms.devlang: na
   ms.topic: landing-page
   ms.date: 10-25-2017
-  ms.author: liwong
-  ms.technology: vs-ai-tools
 abstract:
   description: Visual Studio Tools for AI is an integrated development environment to build, test, and deploy deep learning solutions. Learn how to use AI Tools with our quickstarts, tutorials, and samples.
 sections:

b362ae64-b1c1-4f4d-a0af-76fe2cfdbd70/breadcrumb/toc.yml

@@ -17,27 +17,25 @@ ms.workload:
 
 Visual Studio Tools for AI can be installed on Windows 64-bit operating systems.
 
-## Installing Visual Studio Tools for AI
+## Install Visual Studio Tools for AI
 
-This extension works with [Visual Studio](https://docs.microsoft.com/visualstudio/) 2015, 2017, Community edition or higher.
+This extension works with Visual Studio 2015 and Visual Studio 2017, Community edition or higher.
 
-To install, download from the [Visual Studio MarketPlace](http://aka.ms/vstoolsforai) or from within Visual Studio
+To install, download from the [Visual Studio Marketplace](http://aka.ms/vstoolsforai) or from within Visual Studio
 
-1. **Tools**> **Extensions and Updates**
+1. **Tools** > **Extensions and Updates**
 
 ![install CUDA on Windows](media\installation\extensions.png)
 
 1. **Search** in upper right-hand corner for "Tools for AI"
 2. Select **Visual Studio Tools for AI**
 3. Click **Download**
 
-
-## Preparing your local machine
+## Prepare your local machine
 
 Before training deep learning models on your local computer you should make sure you have the latest applicable prerequisites installed. This includes making sure the latest drivers and libraries for your NVIDIA GPU (if you have one). You should also ensure you have installed Python and Python libraries such as NumPy, SciPy, and appropriate deep learning frameworks such as Microsoft Cognitive Toolkit (CNTK), TensorFlow, Caffe2, MXNet, Keras, Theano, PyTorch and/or Chainer that you plan to use in your project.
 
 > [!NOTE]
->
 > Software introduction in the following subsections is excerpted from their homepages.
 
 ### NVIDIA GPU driver
@@ -62,23 +60,21 @@ To install CUDA
 
 [cuDNN](https://developer.nvidia.com/cudnn) (CUDA Deep Neural Network library) is a GPU-accelerated library of primitives for deep neural networks by NVIDIA. cuDNN v6 is required by latest deep learning frameworks.
 
-To install cuDNN
-- Visit [here](https://developer.nvidia.com/rdp/cudnn-download) to download and install the latest package.
+To install cuDNN:
+
+- Visit [NVIDIA Developer](https://developer.nvidia.com/rdp/cudnn-download) to download and install the latest package.
 - Ensure to add the directory containing cuDNN binary to the %PATH% or $Path environment variable.
 - On Windows, you can copy cudnn64_6.dll to "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v8.0\bin".
 
 > [!NOTE]
->
-> Previous deep learning frameworks such as CNTK 2.0 and TensorFlow 1.2.1 need cuDNN v5.1.
-> However, you can install multiple cuDNN versions together.
-
+> Previous deep learning frameworks such as CNTK 2.0 and TensorFlow 1.2.1 need cuDNN v5.1. However, you can install multiple cuDNN versions together.
 
 ### Python
 
-Python has been the primary programming language for deep learning applications.
-**64-bit** Python distribution is required, and [Python 3.5.4](https://www.python.org/downloads/release/python-354/) is recommended for the best compatibility.
+Python has been the primary programming language for deep learning applications. **64-bit** Python distribution is required, and [Python 3.5.4](https://www.python.org/downloads/release/python-354/) is recommended for the best compatibility.
 
 ### To install Python on Windows
+
 - We suggest installing the Python launcher for yourself only, and add Python to the %PATH% environment variable.
 - Ensure to install pip, which is the package management system to install and manage software packages written in Python.
 
@@ -119,52 +115,47 @@ Learn more about install [Python for Visual Studio Tools](../python/installing-p
 
 - **NumPy** is a general-purpose array-processing package designed to efficiently manipulate large multi-dimensional arrays of arbitrary records without sacrificing too much speed for small multi-dimensional arrays.
 
-- **SciPy** (pronounced "Sigh Pie") is open-source software for mathematics, science, and engineering, depending on NumPy.
-Starting from version 1.0.0, SciPy now has official prebuilt wheel package for Windows.
+- **SciPy** (pronounced "Sigh Pie") is open-source software for mathematics, science, and engineering, depending on NumPy. Starting from version 1.0.0, SciPy now has official prebuilt wheel package for Windows.
 
 To install NumPy and SciPy, run the following command in a terminal:
+
 ```bash
 pip3.5 install -U numpy scipy
 ```
 
 > [!NOTE]
->
 > The above command upgrades existing old or unofficial (e.g. third-party packages from http://www.lfd.uci.edu/~gohlke/pythonlibs/ for Windows) NumPy and SciPy to the latest official ones.
 
 ### Microsoft Cognitive Toolkit (CNTK)
 
 The [Microsoft Cognitive Toolkit](https://cntk.ai) is a unified deep-learning toolkit that describes neural networks as a series of computational steps via a directed graph. CNTK supports both Python and BrainScript programming languages.
 
 > [!NOTE]
->
 > CNTK currently does not support macOS.
 
 To install CNTK Python package, see [how to install CNTK](https://docs.microsoft.com/cognitive-toolkit/Setup-CNTK-on-your-machine)
 
 ### TensorFlow
 
-[TensorFlow](https://www.tensorflow.org/) is an open source software library for numerical computation using data flow graphs.
-Refer to [here](https://www.tensorflow.org/install/) for detailed installation.
+[TensorFlow](https://www.tensorflow.org/) is an open source software library for numerical computation using data flow graphs. Refer to [here](https://www.tensorflow.org/install/) for detailed installation.
 
 > [!NOTE]
->
 > As of version 1.2, TensorFlow no longer provides GPU support for macOS.
 
 ### Caffe2
 
-[Caffe2](https://caffe2.ai/) is a lightweight, modular, and scalable deep learning framework.
-Building on the original Caffe, Caffe2 is designed with expression, speed, and modularity in mind.
+[Caffe2](https://caffe2.ai/) is a lightweight, modular, and scalable deep learning framework. Building on the original Caffe, Caffe2 is designed with expression, speed, and modularity in mind.
 
 Currently, there's no prebuilt Caffe2 python wheel package available.
 
 Visit [here](https://caffe2.ai/docs/getting-started.html) to build from source code.
 
 ### MXNet
 
-[Apache MXNet (incubating)](https://mxnet.incubator.apache.org/) is a deep learning framework designed for both efficiency and flexibility.
-It allows you to **mix** [symbolic and imperative programming](http://mxnet.io/architecture/index.html#deep-learning-system-design-concepts) to maximize efficiency and productivity.
+[Apache MXNet (incubating)](https://mxnet.incubator.apache.org/) is a deep learning framework designed for both efficiency and flexibility. It allows you to **mix** [symbolic and imperative programming](http://mxnet.io/architecture/index.html#deep-learning-system-design-concepts) to maximize efficiency and productivity.
 
 To install MXNet, run the following command in a terminal:
+
 - With GPU
     ```bash
     pip3.5 install mxnet-cu80==0.12.0
@@ -179,6 +170,7 @@ To install MXNet, run the following command in a terminal:
 [Keras](https://keras.io/) is a high-level neural networks API, written in Python and capable of running on top of CNTK, TensorFlow, or Theano. It was developed with a focus on enabling fast experimentation. Being able to go from idea to result with the least possible delay is key to doing good research.
 
 To install Keras, please run the following command in a terminal:
+
 ```bash
 pip3.5 install Keras==2.0.9
 ```
@@ -188,20 +180,22 @@ pip3.5 install Keras==2.0.9
 [Theano](http://deeplearning.net/software/theano/) is a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently.
 
 To install Theano, please run the following command in a terminal:
+
 ```bash
 pip3.5 install Theano==0.9.0
 ```
 
 ### PyTorch
 
 [PyTorch](http://pytorch.org/) is a python package that provides two high-level features:
+
 - Tensor computation (like numpy) with strong GPU acceleration
 - Deep Neural Networks built on a tape-based autograd system
 
-To install PyTorch, please run the following command in a terminal:
+To install PyTorch, run the following command in a terminal:
 
 - **Windows**
-    - There is no official wheel package yet. You may download a third-party [Anaconda PyTorch package](https://anaconda.org/peterjc123/pytorch/0.2.1/download/win-64/pytorch-0.2.1-py35h24644ff_0.2.1cu80.tar.bz2).
+    - There's no official wheel package yet. You can download a third-party [Anaconda PyTorch package](https://anaconda.org/pytorch/repo?type=all).
     - Decompress it to your home directory, e.g. "C:\Users\test\pytorch".
     - Add "C:\Users\test\pytorch\Lib\site-packages" to the %PYTHONPATH% environment variable.
 
@@ -210,41 +204,36 @@ To install PyTorch, please run the following command in a terminal:
     pip3.5 install http://download.pytorch.org/whl/torch-0.2.0.post3-cp35-cp35m-macosx_10_7_x86_64.whl
     ```
     > [!NOTE]
-	>
     > macOS binaries don't support CUDA, install from source if CUDA is needed
 
 - **Linux**
     ```bash
     pip3.5 install http://download.pytorch.org/whl/cu80/torch-0.2.0.post3-cp35-cp35m-manylinux1_x86_64.whl
     ```
     > [!NOTE]
-	>
     > This single package supports both GPU and CPU.
 
 Finally, install torchvision on non-Windows:
+
 ```bash
 pip3.5 install torchvision
 ```
 
 ### Chainer
 
-[Chainer](https://chainer.org/) is a Python-based deep learning framework aiming at flexibility.
-It provides automatic differentiation APIs based on the **define-by-run approach** (a.k.a. dynamic computational graphs) as well as object-oriented high-level APIs to build and train neural networks.
+[Chainer](https://chainer.org/) is a Python-based deep learning framework aiming at flexibility. It provides automatic differentiation APIs based on the **define-by-run approach** (a.k.a. dynamic computational graphs) as well as object-oriented high-level APIs to build and train neural networks.
 
 To enable CUDA support, install [CuPy](https://github.com/cupy/cupy):
+
 ```bash
 pip3.5 install cupy
 ```
 
 > [!NOTE]
->
-> On Windows, you need **2015** version of [Microsoft Visual Studio](https://www.visualstudio.com/)
-or [Microsoft Visual C++ Build Tools](http://landinghub.visualstudio.com/visual-cpp-build-tools)
-to compile CuPy with CUDA 8.0.
+> On Windows, you need the 2015 version of [Visual Studio](https://visualstudio.microsoft.com/) or the [Microsoft Visual C++ Build Tools](https://visualstudio.microsoft.com/visual-cpp-build-tools/) to compile CuPy with CUDA 8.0.
 
 To install Chainer, please run the following command in a terminal:
+
 ```bash
 pip3.5 install chainer==3.0.0
-```
-
-
+```

b362ae64-b1c1-4f4d-a0af-76fe2cfdbd70/docfx.json

@@ -16,7 +16,7 @@ ms.workload:
   - "multiple"
 ---
 # C26409 NO_NEW_DELETE
-  Even if code is clean of calls to malloc() and free() we still suggest that you consider better options than explicit use of operators [new and delete](/cpp/cpp/new-and-delete-operators). See more details in the description of the rule *R.11: Avoid calling new and delete explicitly*. The ultimate fix is to start using smart pointers with appropriate factory methods, such as [std::make_unique](/cpp/standard-library/memory-functions#make_unique).
+  Even if code is clean of calls to malloc() and free() we still suggest that you consider better options than explicit use of operators [new and delete](/cpp/cpp/new-and-delete-operators). See more details in the description of the rule *R.11: Avoid calling new and delete explicitly*. The ultimate fix is to start using smart pointers with appropriate factory functions, such as [std::make_unique](/cpp/standard-library/memory-functions#make_unique).
 
 ## Remarks
 - The checker warns on calls to any kind of operator `new` or `delete`: scalar, vector, overloaded versions (global and class-specific), as well as on placement versions. The latter case may require some clarifications on the Core Guidelines in terms of suggested fixes and may be omitted in the future.

b362ae64-b1c1-4f4d-a0af-76fe2cfdbd70/index.md

@@ -20,8 +20,8 @@ Passing a unique pointer by reference assumes that its resource may be released
 
 ## Remarks
 - The limitations from the warning C26410 are applicable here as well.
-- The heuristic to detect "release" or "reset" access to the unique pointer is rather naïve: we only detect calls to assignment operators and methods named "reset" (case-insensitive). Obviously, this doesn’t cover all possible cases of smart pointer modifications (e.g. std::swap, or any special non-const method in a custom smart pointer). It is expected that this warning will produce many false positives on custom types, as well as in some scenarios dealing with standard unique pointers. The heuristic will be improved as we implement more checks focused on smart pointers.
-- Smart pointers are often templates. This brings an interesting limitation related to the fact that the compiler is not required to process template code in templates if it is not used. In some minimal code bases that have very limited use of smart pointer interfaces, the checker may produce unexpected results due its inability to properly identify semantics of the template type (because some important methods may never be used). For the standard `unique_pointer` this limitation if mitigated by recognizing the type’s name. This may be extended in future to cover more well-known smart pointers.
+- The heuristic to detect "release" or "reset" access to the unique pointer is rather naïve: we only detect calls to assignment operators and functions named "reset" (case-insensitive). Obviously, this doesn’t cover all possible cases of smart pointer modifications (e.g. std::swap, or any special non-const function in a custom smart pointer). It is expected that this warning will produce many false positives on custom types, as well as in some scenarios dealing with standard unique pointers. The heuristic will be improved as we implement more checks focused on smart pointers.
+- Smart pointers are often templates. This brings an interesting limitation related to the fact that the compiler is not required to process template code in templates if it is not used. In some minimal code bases that have very limited use of smart pointer interfaces, the checker may produce unexpected results due its inability to properly identify semantics of the template type (because some important functions may never be used). For the standard `unique_pointer` this limitation if mitigated by recognizing the type’s name. This may be extended in future to cover more well-known smart pointers.
 - Lambda expressions with implicit capture-by-reference may lead to surprising warnings about references to unique pointers. Currently all captured reference parameters in lambdas are reported regardless whether they are reset or not. The heuristic here must be extended to correlate lambda fields with lambda parameters. This will be addressed in the next releases (after Visual Studio 2017 version 15.3)
 
 ## Example 1: Unnecessary reference

docs/_breadcrumb/toc.yml

@@ -31,7 +31,7 @@ Using a smart pointer type to pass data to a function indicates that the target
 -  Interpretation of the operations that can affect lifetime of contained objects is quite broad and includes:
 -  any function which accepts pointer or reference parameter to a non-constant smart pointer;
 -  copy or move constructors or assignment operators;
--  non-constant methods.
+-  non-constant functions.
 ## Example
 cumbersome lifetime management
 

docs/ai/index.yml

@@ -29,7 +29,7 @@ Passing shared pointers by reference may be useful in scenarios where callee cod
 -  copy constructor or copy assignment operator (public and non-deleted);
 -  public destructor which is neither deleted nor defaulted. Empty destructors are still counted as user-defined.
 -  The action of resetting or reassigning is interpreted in a more generic way:
--  any call to a non-constant method on a shared pointer can potentially reset the pointer;
+-  any call to a non-constant function on a shared pointer can potentially reset the pointer;
 -  any call to a function which accepts a reference to a non-constant shared pointer can potentially reset or reassign that pointer.
 
 ## Example

docs/ai/installation.md

@@ -32,4 +32,4 @@ Special operations like constructors are assumed to alter behavior of types so t
 -  destructors;
 -  The rule doesn’t check if operations are defined in the same way, i.e. it is okay to mix deleted and defaulted operations with explicitly defined, but they all must be specified somehow if any of them appears.
 -  Access levels are not important and can also be mixed.
--  The warning flags the first non-static method definition of a type, once per type.
+-  The warning flags the first non-static function definition of a type, once per type.