broken links (tensorflow#2033)

DOC

after moving to mono repo some links in README (and other markdown) files got broken or point to the archived repos. this PR updates such links that i found.

Author: vabarbosa

CONTRIBUTING.md

@@ -23,7 +23,7 @@ use GitHub pull requests for this purpose. Consult
 information on using pull requests.
 
 ## Continuous Integration
-Continuous Integration tests run for every PR that is sent against TensorFlow.js repositories. Any time you push to the branch, they will re-run. Before asking for a review, make sure that the continous integration tests are passing.
+Continuous Integration tests run for every PR that is sent against TensorFlow.js repositories. Any time you push to the branch, they will re-run. Before asking for a review, make sure that the continuous integration tests are passing.
 
 To see the logs from the Cloud Build CI, please join either
 our [discussion](https://groups.google.com/a/tensorflow.org/forum/#!forum/tfjs)
@@ -67,7 +67,7 @@ add new backends.
 ### Adding an op
 
 When adding ops to the library and deciding whether to write a kernel
-implementation in [backend.ts](https://github.com/tensorflow/tfjs-core/blob/master/src/backends/backend.ts),
+implementation in [backend.ts](/tfjs-core/src/backends/backend.ts),
 be sure to check out the TensorFlow ops list [here](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/ops/ops.pbtxt).
 This list shows the kernels available for the TensorFlow C API. To ensure that
 we can bind to this with node.js, we should ensure that our backend.ts

README.md

@@ -23,20 +23,20 @@ other client-side data.
 
 This repository contains the logic and scripts that combine
 four packages:
-- [TensorFlow.js Core](https://github.com/tensorflow/tfjs-core),
+- [TensorFlow.js Core](/tfjs-core),
   a flexible low-level API, formerly known as *deeplearn.js*.
-- [TensorFlow.js Layers](https://github.com/tensorflow/tfjs-layers),
+- [TensorFlow.js Layers](/tfjs-layers),
   a high-level API which implements functionality similar to
   [Keras](https://keras.io/).
-- [TensorFlow.js Data](https://github.com/tensorflow/tfjs-data),
+- [TensorFlow.js Data](/tfjs-data),
   a simple API to load and prepare data analogous to
   [tf.data](https://www.tensorflow.org/guide/datasets).
-- [TensorFlow.js Converter](https://github.com/tensorflow/tfjs-converter),
+- [TensorFlow.js Converter](/tfjs-converter),
   tools to import a TensorFlow SavedModel to TensorFlow.js
 
 If you care about bundle size, you can import those packages individually.
 
-If you are looking for Node.js support, check out the [TensorFlow.js Node repository](https://github.com/tensorflow/tfjs-node).
+If you are looking for Node.js support, check out the [TensorFlow.js Node directory](/tfjs-node).
 
 ## Examples
 

tfjs-automl/README.md

@@ -109,7 +109,7 @@ Make sure you can access those files as static assets from your web app by servi
 ### Demo
 
 The object detection demo lives in
-[demo/object_classification](./demo/object_classification). To run it:
+[demo/object_detection](./demo/object_detection). To run it:
 
 ```sh
 cd demo/object_detection

tfjs-core/README.md

@@ -14,7 +14,7 @@ To keep track of issues we use the [tensorflow/tfjs](https://github.com/tensorfl
 You can install TensorFlow.js via yarn or npm. We recommend using the
 [@tensorflow/tfjs](https://www.npmjs.com/package/@tensorflow/tfjs) npm package,
 which gives you both this Core API and the higher-level
-[Layers API](https://github.com/tensorflow/tfjs-layers):
+[Layers API](/tfjs-layers):
 
 ```js
 import * as tf from '@tensorflow/tfjs';
@@ -31,7 +31,7 @@ import * as tfc from '@tensorflow/tfjs-core';
 // No Layers API.
 ```
 
-For info about development, check out [DEVELOPMENT.md](./DEVELOPMENT.md).
+For info about development, check out [DEVELOPMENT.md](/DEVELOPMENT.md).
 
 ## For more information
 

tfjs-layers/README.md

@@ -1,7 +1,7 @@
 # TensorFlow.js Layers: High-Level Machine Learning Model API
 
 A part of the TensorFlow.js ecosystem, TensorFlow.js Layers is a high-level
-API built on [TensorFlow.js Core](https://github.com/tensorflow/tfjs-core),
+API built on [TensorFlow.js Core](/tfjs-core),
 enabling users to build, train and execute deep learning models in the browser.
 TensorFlow.js Layers is modeled after
 [Keras](https://keras.io/) and

tfjs-node/WINDOWS_TROUBLESHOOTING.md

@@ -8,7 +8,7 @@ Currently, node-gyp requires Python 2.x to work properly. If Python 3.x is insta
 
 ## 'The system cannot find the patch specified' Exceptions
 
-This can happen for a variety of reasons. First, to inspect what is missing either `cd node_modules/@tensorflow/tfjs-node` or clone the [tensorflow/tfjs-node repo](https://github.com/tensorflow/tfjs-node).
+This can happen for a variety of reasons. First, to inspect what is missing either `cd node_modules/@tensorflow/tfjs-node` or clone the [tensorflow/tfjs repo](https://github.com/tensorflow/tfjs).
 
 After `cd`'ing or cloning, run the following command (you might need node-gyp installed globablly `npm install -g node-gyp`):
 

tfjs-react-native/README.md

@@ -115,7 +115,7 @@ export class App extends React.Component {
 After gathering feedback in the alpha release we will add an example to the [tensorflow/tfjs-examples](https://github.com/tensorflow/tfjs-examples) repository.
 
 For now you can take a look at [`integration_rn59/App.tsx`](integration_rn59/App.tsx) for an example of what using tfjs-react-native looks like.
-The [Webcam demo folder](integration_rn70/components/webcam) has an example of a style transfer app.
+The [Webcam demo folder](integration_rn59/components/webcam) has an example of a style transfer app.
 
 ![style transfer app initial screen](images/rn-styletransfer_1.jpg)
 ![style transfer app initial screen](images/rn-styletransfer_2.jpg)