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bird-classification-workshop

Introduction

Machine learning is a game-changing technology with vast potential in every industry, yet many teams struggle with how to get started. In this “train the trainer” workshop, we share a sample project you can use with your customers and internal teams to have fun while diving deep on deep learning. You will get hands-on experience using Amazon SageMaker to build and deploy a neural network based on a publicly available dataset of 48,000 bird images.

You will also create a custom project for AWS DeepLens that detects birds and triggers species identification. By the end of the workshop, you will have a working end-to-end solution. Prerequisites: hands-on experience with Python, AWS Lambda, Amazon SNS, and Amazon S3 are required to get the most value from the workshop.

Getting started

In this workshop, you have an AWS DeepLens device in front of you connected to a monitor, keyboard, and mouse. You can use the DeepLens device to run the entire workshop, or you can use your laptop to do all of the labs other than the DeepLens lab (lab 5).

Log into the AWS console

Once the browser is open, type console.aws.amazon.com into the url bar. (Note: If the login page says "Root user sign in" and there's already an email showing, select Sign in to a different account and then type in your AWS Account number on your card.)

Once your login page shows three fields, please enter the following:

  • Account ID or alias: the AWS Account number on your card
  • IAM user name: the User name on your card
  • Password: Aws2017!

Next, make sure you're in N. Virginia region. Proceed to Lab 1.

If you are using DeepLens instead of your laptop

AWS DeepLens runs an Ubuntu OS. Login to the device with the password Aws2017! for the aws_cam username.

We have already pre-registered your DeepLens device to your workshop account. You can find the information for your account on the card in front of you taped to your monitor.

Open a Firefox browser on the left panel and follow the console login instructions.

Lab overview

The workshop is composed of the following 6 labs:

  • Lab 1 - Prepare images for training
  • Lab 2 - Train the classification model using Amazon SageMaker
  • Lab 3 - Host the trained model and identify your first bird!
  • Lab 4 - Trigger an inference as new pictures arrive in S3
  • Lab 5 - Use AWS DeepLens to detect birds and trigger species identification
  • Lab 6 - Configure SMS text notification with identified species (optional)

Cleaning up

After you are done, it is important to clean up resources in your account so that you will not be billed unexpectedly. If you used an AWS account that was supplied to you just for the purposes of this workshop, then you can skip this step. Otherwise, take the following steps:

  • Delete the SageMaker endpoint
  • Stop the SageMaker notebook instance
  • Delete SageMaker notebook instance
  • Delete any objects you created in your S3 bucket
  • Delete the S3 bucket that you created just for this workshop
  • Delete the DeepLens project you created
  • Delete the SNS topic you created
  • Delete the Lambda functions you created

Acknowledgement for use of the NABirds dataset

Data provided by the Cornell Lab of Ornithology, with thanks to photographers and contributors of crowdsourced data at AllAboutBirds.org/Labs.

This material is based upon work supported by the National Science Foundation under Grant No. 1010818.

Any requests for further use of this data should be directedhere.

Additional setup if you are leading a workshop

If you are setting up this workshop for others, or if you are executing this workshop in your own AWS account (versus attending a workshop at which you are given access to a temporary AWS account that is pre-configured), read these instructions.

About

This workshop uses Amazon SageMaker and AWS DeepLens to identify bird species

Resources

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2 forks
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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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bird-classification-workshop
Introduction
Machine learning is a game-changing technology with vast potential in every industry, yet many teams struggle with how to get started. In this “train the trainer” workshop, we share a sample project you can use with your customers and internal teams to have fun while diving deep on deep learning. You will get hands-on experience using Amazon SageMaker to build and deploy a neural network based on a publicly available dataset of 48,000 bird images.
You will also create a custom project for AWS DeepLens that detects birds and triggers species identification. By the end of the workshop, you will have a working end-to-end solution. Prerequisites: hands-on experience with Python, AWS Lambda, Amazon SNS, and Amazon S3 are required to get the most value from the workshop.
Getting started
In this workshop, you have an AWS DeepLens device in front of you connected to a monitor, keyboard, and mouse. You can use the DeepLens device to run the entire workshop, or you can use your laptop to do all of the labs other than the DeepLens lab (lab 5).
Log into the AWS console
Once the browser is open, type console.aws.amazon.com into the url bar. (Note: If the login page says "Root user sign in" and there's already an email showing, select Sign in to a different account and then type in your AWS Account number on your card.)
Once your login page shows three fields, please enter the following:
  • Account ID or alias: the AWS Account number on your card
  • IAM user name: the User name on your card
  • Password: Aws2017!
Next, make sure you're in N. Virginia region. Proceed to Lab 1.
If you are using DeepLens instead of your laptop
AWS DeepLens runs an Ubuntu OS. Login to the device with the password Aws2017! for the aws_cam username.
We have already pre-registered your DeepLens device to your workshop account. You can find the information for your account on the card in front of you taped to your monitor.
Open a Firefox browser on the left panel and follow the console login instructions.
Lab overview
The workshop is composed of the following 6 labs:
  • Lab 1 - Prepare images for training
  • Lab 2 - Train the classification model using Amazon SageMaker
  • Lab 3 - Host the trained model and identify your first bird!
  • Lab 4 - Trigger an inference as new pictures arrive in S3
  • Lab 5 - Use AWS DeepLens to detect birds and trigger species identification
  • Lab 6 - Configure SMS text notification with identified species (optional)
Cleaning up
After you are done, it is important to clean up resources in your account so that you will not be billed unexpectedly. If you used an AWS account that was supplied to you just for the purposes of this workshop, then you can skip this step. Otherwise, take the following steps:
  • Delete the SageMaker endpoint
  • Stop the SageMaker notebook instance
  • Delete SageMaker notebook instance
  • Delete any objects you created in your S3 bucket
  • Delete the S3 bucket that you created just for this workshop
  • Delete the DeepLens project you created
  • Delete the SNS topic you created
  • Delete the Lambda functions you created
Acknowledgement for use of the NABirds dataset
Data provided by the Cornell Lab of Ornithology, with thanks to photographers and contributors of crowdsourced data at AllAboutBirds.org/Labs.
This material is based upon work supported by the National Science Foundation under Grant No. 1010818.
Any requests for further use of this data should be directedhere.
Additional setup if you are leading a workshop
If you are setting up this workshop for others, or if you are executing this workshop in your own AWS account (versus attending a workshop at which you are given access to a temporary AWS account that is pre-configured), read these instructions.
About
This workshop uses Amazon SageMaker and AWS DeepLens to identify bird species
Resources
Readme
Activity
Stars
6 stars
Watchers
0 watching
Forks
2 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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README.md
README.md
Repository files navigation
bird-classification-workshop
Introduction
Machine learning is a game-changing technology with vast potential in every industry, yet many teams struggle with how to get started. In this “train the trainer” workshop, we share a sample project you can use with your customers and internal teams to have fun while diving deep on deep learning. You will get hands-on experience using Amazon SageMaker to build and deploy a neural network based on a publicly available dataset of 48,000 bird images.
You will also create a custom project for AWS DeepLens that detects birds and triggers species identification. By the end of the workshop, you will have a working end-to-end solution. Prerequisites: hands-on experience with Python, AWS Lambda, Amazon SNS, and Amazon S3 are required to get the most value from the workshop.
Getting started
In this workshop, you have an AWS DeepLens device in front of you connected to a monitor, keyboard, and mouse. You can use the DeepLens device to run the entire workshop, or you can use your laptop to do all of the labs other than the DeepLens lab (lab 5).
Log into the AWS console
Once the browser is open, type console.aws.amazon.com into the url bar. (Note: If the login page says "Root user sign in" and there's already an email showing, select Sign in to a different account and then type in your AWS Account number on your card.)
Once your login page shows three fields, please enter the following:
  • Account ID or alias: the AWS Account number on your card
  • IAM user name: the User name on your card
  • Password: Aws2017!
Next, make sure you're in N. Virginia region. Proceed to Lab 1.
If you are using DeepLens instead of your laptop
AWS DeepLens runs an Ubuntu OS. Login to the device with the password Aws2017! for the aws_cam username.
We have already pre-registered your DeepLens device to your workshop account. You can find the information for your account on the card in front of you taped to your monitor.
Open a Firefox browser on the left panel and follow the console login instructions.
Lab overview
The workshop is composed of the following 6 labs:
  • Lab 1 - Prepare images for training
  • Lab 2 - Train the classification model using Amazon SageMaker
  • Lab 3 - Host the trained model and identify your first bird!
  • Lab 4 - Trigger an inference as new pictures arrive in S3
  • Lab 5 - Use AWS DeepLens to detect birds and trigger species identification
  • Lab 6 - Configure SMS text notification with identified species (optional)
Cleaning up
After you are done, it is important to clean up resources in your account so that you will not be billed unexpectedly. If you used an AWS account that was supplied to you just for the purposes of this workshop, then you can skip this step. Otherwise, take the following steps:
  • Delete the SageMaker endpoint
  • Stop the SageMaker notebook instance
  • Delete SageMaker notebook instance
  • Delete any objects you created in your S3 bucket
  • Delete the S3 bucket that you created just for this workshop
  • Delete the DeepLens project you created
  • Delete the SNS topic you created
  • Delete the Lambda functions you created
Acknowledgement for use of the NABirds dataset
Data provided by the Cornell Lab of Ornithology, with thanks to photographers and contributors of crowdsourced data at AllAboutBirds.org/Labs.
This material is based upon work supported by the National Science Foundation under Grant No. 1010818.
Any requests for further use of this data should be directedhere.
Additional setup if you are leading a workshop
If you are setting up this workshop for others, or if you are executing this workshop in your own AWS account (versus attending a workshop at which you are given access to a temporary AWS account that is pre-configured), read these instructions.
About
This workshop uses Amazon SageMaker and AWS DeepLens to identify bird species
Resources
Readme
Activity
Stars
6 stars
Watchers
0 watching
Forks
2 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Repository files navigation
bird-classification-workshop
Introduction
Machine learning is a game-changing technology with vast potential in every industry, yet many teams struggle with how to get started. In this “train the trainer” workshop, we share a sample project you can use with your customers and internal teams to have fun while diving deep on deep learning. You will get hands-on experience using Amazon SageMaker to build and deploy a neural network based on a publicly available dataset of 48,000 bird images.
You will also create a custom project for AWS DeepLens that detects birds and triggers species identification. By the end of the workshop, you will have a working end-to-end solution. Prerequisites: hands-on experience with Python, AWS Lambda, Amazon SNS, and Amazon S3 are required to get the most value from the workshop.
Getting started
In this workshop, you have an AWS DeepLens device in front of you connected to a monitor, keyboard, and mouse. You can use the DeepLens device to run the entire workshop, or you can use your laptop to do all of the labs other than the DeepLens lab (lab 5).
Log into the AWS console
Once the browser is open, type console.aws.amazon.com into the url bar. (Note: If the login page says "Root user sign in" and there's already an email showing, select Sign in to a different account and then type in your AWS Account number on your card.)
Once your login page shows three fields, please enter the following:
  • Account ID or alias: the AWS Account number on your card
  • IAM user name: the User name on your card
  • Password: Aws2017!
Next, make sure you're in N. Virginia region. Proceed to Lab 1.
If you are using DeepLens instead of your laptop
AWS DeepLens runs an Ubuntu OS. Login to the device with the password Aws2017! for the aws_cam username.
We have already pre-registered your DeepLens device to your workshop account. You can find the information for your account on the card in front of you taped to your monitor.
Open a Firefox browser on the left panel and follow the console login instructions.
Lab overview
The workshop is composed of the following 6 labs:
  • Lab 1 - Prepare images for training
  • Lab 2 - Train the classification model using Amazon SageMaker
  • Lab 3 - Host the trained model and identify your first bird!
  • Lab 4 - Trigger an inference as new pictures arrive in S3
  • Lab 5 - Use AWS DeepLens to detect birds and trigger species identification
  • Lab 6 - Configure SMS text notification with identified species (optional)
Cleaning up
After you are done, it is important to clean up resources in your account so that you will not be billed unexpectedly. If you used an AWS account that was supplied to you just for the purposes of this workshop, then you can skip this step. Otherwise, take the following steps:
  • Delete the SageMaker endpoint
  • Stop the SageMaker notebook instance
  • Delete SageMaker notebook instance
  • Delete any objects you created in your S3 bucket
  • Delete the S3 bucket that you created just for this workshop
  • Delete the DeepLens project you created
  • Delete the SNS topic you created
  • Delete the Lambda functions you created
Acknowledgement for use of the NABirds dataset
Data provided by the Cornell Lab of Ornithology, with thanks to photographers and contributors of crowdsourced data at AllAboutBirds.org/Labs.
This material is based upon work supported by the National Science Foundation under Grant No. 1010818.
Any requests for further use of this data should be directedhere.
Additional setup if you are leading a workshop
If you are setting up this workshop for others, or if you are executing this workshop in your own AWS account (versus attending a workshop at which you are given access to a temporary AWS account that is pre-configured), read these instructions.
About
This workshop uses Amazon SageMaker and AWS DeepLens to identify bird species
Resources
Readme
Activity
Stars
6 stars
Watchers
0 watching
Forks
2 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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bird-classification-workshop
Introduction
Machine learning is a game-changing technology with vast potential in every industry, yet many teams struggle with how to get started. In this “train the trainer” workshop, we share a sample project you can use with your customers and internal teams to have fun while diving deep on deep learning. You will get hands-on experience using Amazon SageMaker to build and deploy a neural network based on a publicly available dataset of 48,000 bird images.
You will also create a custom project for AWS DeepLens that detects birds and triggers species identification. By the end of the workshop, you will have a working end-to-end solution. Prerequisites: hands-on experience with Python, AWS Lambda, Amazon SNS, and Amazon S3 are required to get the most value from the workshop.
Getting started
In this workshop, you have an AWS DeepLens device in front of you connected to a monitor, keyboard, and mouse. You can use the DeepLens device to run the entire workshop, or you can use your laptop to do all of the labs other than the DeepLens lab (lab 5).
Log into the AWS console
Once the browser is open, type console.aws.amazon.com into the url bar. (Note: If the login page says "Root user sign in" and there's already an email showing, select Sign in to a different account and then type in your AWS Account number on your card.)
Once your login page shows three fields, please enter the following:
  • Account ID or alias: the AWS Account number on your card
  • IAM user name: the User name on your card
  • Password: Aws2017!
Next, make sure you're in N. Virginia region. Proceed to Lab 1.
If you are using DeepLens instead of your laptop
AWS DeepLens runs an Ubuntu OS. Login to the device with the password Aws2017! for the aws_cam username.
We have already pre-registered your DeepLens device to your workshop account. You can find the information for your account on the card in front of you taped to your monitor.
Open a Firefox browser on the left panel and follow the console login instructions.
Lab overview
The workshop is composed of the following 6 labs:
  • Lab 1 - Prepare images for training
  • Lab 2 - Train the classification model using Amazon SageMaker
  • Lab 3 - Host the trained model and identify your first bird!
  • Lab 4 - Trigger an inference as new pictures arrive in S3
  • Lab 5 - Use AWS DeepLens to detect birds and trigger species identification
  • Lab 6 - Configure SMS text notification with identified species (optional)
Cleaning up
After you are done, it is important to clean up resources in your account so that you will not be billed unexpectedly. If you used an AWS account that was supplied to you just for the purposes of this workshop, then you can skip this step. Otherwise, take the following steps:
  • Delete the SageMaker endpoint
  • Stop the SageMaker notebook instance
  • Delete SageMaker notebook instance
  • Delete any objects you created in your S3 bucket
  • Delete the S3 bucket that you created just for this workshop
  • Delete the DeepLens project you created
  • Delete the SNS topic you created
  • Delete the Lambda functions you created
Acknowledgement for use of the NABirds dataset
Data provided by the Cornell Lab of Ornithology, with thanks to photographers and contributors of crowdsourced data at AllAboutBirds.org/Labs.
This material is based upon work supported by the National Science Foundation under Grant No. 1010818.
Any requests for further use of this data should be directedhere.
Additional setup if you are leading a workshop
If you are setting up this workshop for others, or if you are executing this workshop in your own AWS account (versus attending a workshop at which you are given access to a temporary AWS account that is pre-configured), read these instructions.
About
This workshop uses Amazon SageMaker and AWS DeepLens to identify bird species
Resources
Readme
Activity
Stars
6 stars
Watchers
0 watching
Forks
2 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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bird-classification-workshop
Introduction
Machine learning is a game-changing technology with vast potential in every industry, yet many teams struggle with how to get started. In this “train the trainer” workshop, we share a sample project you can use with your customers and internal teams to have fun while diving deep on deep learning. You will get hands-on experience using Amazon SageMaker to build and deploy a neural network based on a publicly available dataset of 48,000 bird images.
You will also create a custom project for AWS DeepLens that detects birds and triggers species identification. By the end of the workshop, you will have a working end-to-end solution. Prerequisites: hands-on experience with Python, AWS Lambda, Amazon SNS, and Amazon S3 are required to get the most value from the workshop.
Getting started
In this workshop, you have an AWS DeepLens device in front of you connected to a monitor, keyboard, and mouse. You can use the DeepLens device to run the entire workshop, or you can use your laptop to do all of the labs other than the DeepLens lab (lab 5).
Log into the AWS console
Once the browser is open, type console.aws.amazon.com into the url bar. (Note: If the login page says "Root user sign in" and there's already an email showing, select Sign in to a different account and then type in your AWS Account number on your card.)
Once your login page shows three fields, please enter the following:
  • Account ID or alias: the AWS Account number on your card
  • IAM user name: the User name on your card
  • Password: Aws2017!
Next, make sure you're in N. Virginia region. Proceed to Lab 1.
If you are using DeepLens instead of your laptop
AWS DeepLens runs an Ubuntu OS. Login to the device with the password Aws2017! for the aws_cam username.
We have already pre-registered your DeepLens device to your workshop account. You can find the information for your account on the card in front of you taped to your monitor.
Open a Firefox browser on the left panel and follow the console login instructions.
Lab overview
The workshop is composed of the following 6 labs:
  • Lab 1 - Prepare images for training
  • Lab 2 - Train the classification model using Amazon SageMaker
  • Lab 3 - Host the trained model and identify your first bird!
  • Lab 4 - Trigger an inference as new pictures arrive in S3
  • Lab 5 - Use AWS DeepLens to detect birds and trigger species identification
  • Lab 6 - Configure SMS text notification with identified species (optional)
Cleaning up
After you are done, it is important to clean up resources in your account so that you will not be billed unexpectedly. If you used an AWS account that was supplied to you just for the purposes of this workshop, then you can skip this step. Otherwise, take the following steps:
  • Delete the SageMaker endpoint
  • Stop the SageMaker notebook instance
  • Delete SageMaker notebook instance
  • Delete any objects you created in your S3 bucket
  • Delete the S3 bucket that you created just for this workshop
  • Delete the DeepLens project you created
  • Delete the SNS topic you created
  • Delete the Lambda functions you created
Acknowledgement for use of the NABirds dataset
Data provided by the Cornell Lab of Ornithology, with thanks to photographers and contributors of crowdsourced data at AllAboutBirds.org/Labs.
This material is based upon work supported by the National Science Foundation under Grant No. 1010818.
Any requests for further use of this data should be directedhere.
Additional setup if you are leading a workshop
If you are setting up this workshop for others, or if you are executing this workshop in your own AWS account (versus attending a workshop at which you are given access to a temporary AWS account that is pre-configured), read these instructions.
About
This workshop uses Amazon SageMaker and AWS DeepLens to identify bird species
Resources
Readme
Activity
Stars
6 stars
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0 watching
Forks
2 forks
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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bird-classification-workshop
Introduction
Machine learning is a game-changing technology with vast potential in every industry, yet many teams struggle with how to get started. In this “train the trainer” workshop, we share a sample project you can use with your customers and internal teams to have fun while diving deep on deep learning. You will get hands-on experience using Amazon SageMaker to build and deploy a neural network based on a publicly available dataset of 48,000 bird images.
You will also create a custom project for AWS DeepLens that detects birds and triggers species identification. By the end of the workshop, you will have a working end-to-end solution. Prerequisites: hands-on experience with Python, AWS Lambda, Amazon SNS, and Amazon S3 are required to get the most value from the workshop.
Getting started
In this workshop, you have an AWS DeepLens device in front of you connected to a monitor, keyboard, and mouse. You can use the DeepLens device to run the entire workshop, or you can use your laptop to do all of the labs other than the DeepLens lab (lab 5).
Log into the AWS console
Once the browser is open, type console.aws.amazon.com into the url bar. (Note: If the login page says "Root user sign in" and there's already an email showing, select Sign in to a different account and then type in your AWS Account number on your card.)
Once your login page shows three fields, please enter the following:
  • Account ID or alias: the AWS Account number on your card
  • IAM user name: the User name on your card
  • Password: Aws2017!
Next, make sure you're in N. Virginia region. Proceed to Lab 1.
If you are using DeepLens instead of your laptop
AWS DeepLens runs an Ubuntu OS. Login to the device with the password Aws2017! for the aws_cam username.
We have already pre-registered your DeepLens device to your workshop account. You can find the information for your account on the card in front of you taped to your monitor.
Open a Firefox browser on the left panel and follow the console login instructions.
Lab overview
The workshop is composed of the following 6 labs:
  • Lab 1 - Prepare images for training
  • Lab 2 - Train the classification model using Amazon SageMaker
  • Lab 3 - Host the trained model and identify your first bird!
  • Lab 4 - Trigger an inference as new pictures arrive in S3
  • Lab 5 - Use AWS DeepLens to detect birds and trigger species identification
  • Lab 6 - Configure SMS text notification with identified species (optional)
Cleaning up
After you are done, it is important to clean up resources in your account so that you will not be billed unexpectedly. If you used an AWS account that was supplied to you just for the purposes of this workshop, then you can skip this step. Otherwise, take the following steps:
  • Delete the SageMaker endpoint
  • Stop the SageMaker notebook instance
  • Delete SageMaker notebook instance
  • Delete any objects you created in your S3 bucket
  • Delete the S3 bucket that you created just for this workshop
  • Delete the DeepLens project you created
  • Delete the SNS topic you created
  • Delete the Lambda functions you created
Acknowledgement for use of the NABirds dataset
Data provided by the Cornell Lab of Ornithology, with thanks to photographers and contributors of crowdsourced data at AllAboutBirds.org/Labs.
This material is based upon work supported by the National Science Foundation under Grant No. 1010818.
Any requests for further use of this data should be directedhere.
Additional setup if you are leading a workshop
If you are setting up this workshop for others, or if you are executing this workshop in your own AWS account (versus attending a workshop at which you are given access to a temporary AWS account that is pre-configured), read these instructions.
About
This workshop uses Amazon SageMaker and AWS DeepLens to identify bird species
Resources
Readme
Activity
Stars
6 stars
Watchers
0 watching
Forks
2 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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Repository files navigation
bird-classification-workshop
Introduction
Machine learning is a game-changing technology with vast potential in every industry, yet many teams struggle with how to get started. In this “train the trainer” workshop, we share a sample project you can use with your customers and internal teams to have fun while diving deep on deep learning. You will get hands-on experience using Amazon SageMaker to build and deploy a neural network based on a publicly available dataset of 48,000 bird images.
You will also create a custom project for AWS DeepLens that detects birds and triggers species identification. By the end of the workshop, you will have a working end-to-end solution. Prerequisites: hands-on experience with Python, AWS Lambda, Amazon SNS, and Amazon S3 are required to get the most value from the workshop.
Getting started
In this workshop, you have an AWS DeepLens device in front of you connected to a monitor, keyboard, and mouse. You can use the DeepLens device to run the entire workshop, or you can use your laptop to do all of the labs other than the DeepLens lab (lab 5).
Log into the AWS console
Once the browser is open, type console.aws.amazon.com into the url bar. (Note: If the login page says "Root user sign in" and there's already an email showing, select Sign in to a different account and then type in your AWS Account number on your card.)
Once your login page shows three fields, please enter the following:
  • Account ID or alias: the AWS Account number on your card
  • IAM user name: the User name on your card
  • Password: Aws2017!
Next, make sure you're in N. Virginia region. Proceed to Lab 1.
If you are using DeepLens instead of your laptop
AWS DeepLens runs an Ubuntu OS. Login to the device with the password Aws2017! for the aws_cam username.
We have already pre-registered your DeepLens device to your workshop account. You can find the information for your account on the card in front of you taped to your monitor.
Open a Firefox browser on the left panel and follow the console login instructions.
Lab overview
The workshop is composed of the following 6 labs:
  • Lab 1 - Prepare images for training
  • Lab 2 - Train the classification model using Amazon SageMaker
  • Lab 3 - Host the trained model and identify your first bird!
  • Lab 4 - Trigger an inference as new pictures arrive in S3
  • Lab 5 - Use AWS DeepLens to detect birds and trigger species identification
  • Lab 6 - Configure SMS text notification with identified species (optional)
Cleaning up
After you are done, it is important to clean up resources in your account so that you will not be billed unexpectedly. If you used an AWS account that was supplied to you just for the purposes of this workshop, then you can skip this step. Otherwise, take the following steps:
  • Delete the SageMaker endpoint
  • Stop the SageMaker notebook instance
  • Delete SageMaker notebook instance
  • Delete any objects you created in your S3 bucket
  • Delete the S3 bucket that you created just for this workshop
  • Delete the DeepLens project you created
  • Delete the SNS topic you created
  • Delete the Lambda functions you created
Acknowledgement for use of the NABirds dataset
Data provided by the Cornell Lab of Ornithology, with thanks to photographers and contributors of crowdsourced data at AllAboutBirds.org/Labs.
This material is based upon work supported by the National Science Foundation under Grant No. 1010818.
Any requests for further use of this data should be directedhere.
Additional setup if you are leading a workshop
If you are setting up this workshop for others, or if you are executing this workshop in your own AWS account (versus attending a workshop at which you are given access to a temporary AWS account that is pre-configured), read these instructions.
About
This workshop uses Amazon SageMaker and AWS DeepLens to identify bird species
Resources
Readme
Activity
Stars
6 stars
Watchers
0 watching
Forks
2 forks
Report repository
Releases
Packages
Contributors
Languages