Vermont Code Camp 2019 - Intro to Deep Learning for Developers

For Vermont locals:
Join the BTV Dev Slack Community! http://btvdev-slackin.herokuapp.com/
Vermont Women in Machine Learning and Data Science Meetup - https://www.meetup.com/VT-WiMLDS/
Burlington Data Scientists Meetup - https://www.meetup.com/Burlington-Data-Scientists/

Talk Resources:
*Machine Learning Frameworks*
TensorFlow - https://www.tensorflow.org/
Train your first neural network: basic classification | TensorFlow -https://www.tensorflow.org/tutorials/keras/basic_classification
Keras - https://keras.io/
PyTorch - https://pytorch.org/
Caffe2 - https://caffe2.ai/

*Resources on IBM Developer*
IBM Developer - https://developer.ibm.com/
IBM Cloud - https://ibm.biz/BdzXfW
Center for Open-Source Data & AI Technologies (CODAIT) - http://codait.org/
IBM Developer Model Asset Exchange (MAX) - https://developer.ibm.com/exchanges/models/
Model Asset Exchange (MAX) Code Patterns - https://developer.ibm.com/patterns/category/model-asset-exchange/
🧙😺 magicat - https://github.com/CODAIT/magicat
Veremin - https://veremin.mybluemix.net/

*Preparing, Building, and Training AI Models*
Project Jupyter - https://jupyter.org/
IBM Developer Model Asset Exchange (MAX) - https://developer.ibm.com/exchanges/models/
IBM Watson Knowledge Catalog - https://www.ibm.com/cloud/watson-knowledge-catalog
IBM Watson Studio - https://www.ibm.com/cloud/watson-studio

*Deploying and Running AI Models*
Data Mining Group (PMML & PFA) - http://dmg.org/
ONNX - https://onnx.ai/
ONNX.js - https://github.com/Microsoft/onnxjs
TensorFlow.js - https://js.tensorflow.org/
TensorFlow Lite - https://www.tensorflow.org/lite
Core ML - https://developer.apple.com/machine-learning/
IBM Watson Machine Learning - https://www.ibm.com/cloud/machine-learning

*Operating, Managing, and Making AI Systems Explainable, Fair, and Robust*
Fabric for Deep Learning (FfDL) - https://github.com/IBM/FfDL
AI Fairness 360 Toolkit - https://github.com/IBM/AIF360
Adversarial Robustness 360 Toolbox - https://github.com/IBM/adversarial-robustness-toolbox
AI Explainability 360 Toolkit - https://github.com/IBM/AIX360/
IBM Watson OpenScale - https://www.ibm.com/cloud/watson-openscale

*Let's Play with Deep Learning!*
Veremin, the Deep Learning Theremin - http://ibm.biz/veremin
magicat, the Command Line image analyzer tool - https://www.npmjs.com/package/magicat
Model Asset eXchange (MAX), ready to use deep learning models - http://ibm.biz/max-models
AI Fairness 360, mitigate unwanted bias from your models - http://aif360.mybluemix.net/data

Transcript

1. Vermont Code Camp 2019 Intro to Deep Learning for Developers — Maureen McElaney Developer Advocate Center for Open-Source Data & AI Technologies ↳ (CODAIT) @Mo_Mack | @ibmcodait | @IBMDeveloper medium.com/codait github.com/codait | github.com/IBM developer.ibm.com
2. Is AI overhyped?
3. Yes.
4. Thank you for coming to my talk.
5. ↳ General Artificial Intelligence Metal Skull With Terminator Eye by L.C. Nøttaasen, on Flickr <https://flic.kr/p/6xh2Dr> (CC BY-SA 2.0).
6. ↳ Broad Artificial Intelligence -[ electrIc b88Gal88 ]- by JD Hancock, on Flickr <https://flic.kr/p/6x9t1H> (CC BY 2.0).
7. ↳ Narrow Artificial Intelligence Danbo on the Lookout by IQRemix, on Flickr <https://flic.kr/p/x5oWjP> (CC BY-SA 2.0).
8. Towards Data Science: Cousins of Articifial Intelligence by Seema Singh https://towardsdatascience.com/cousins-of-artificial-intelligence-dda4edc27b55
9. Applications for Deep Learning include (but are not limited to...) • Image, audio, text classification • Object recognition • Image caption generation • Natural language processing • Speech to text conversion
10. Software Programming Untitled by Marcin Wichary, on Flickr <https://flic.kr/p/68Lhc9> (CC BY 2.0).
11. 01011001001011000001 10101101010100100110 00010110101010011100 10101010110101010100 10001001011101000101 Software Program Business Logic Input Program Execution Output
12. Input Layer Hidden Layers Output Layer
14. Backpropagation Labeled Training Data Coat Sneaker T-shirt Sneaker Pullover Output Errors Pullover Coat Coat Sneaker T-shirt ❌ ❌ ❌ Fashion-MNIST dataset by Zalando Research, on GitHub <https://github.com/zalandoresearch/fashion-mnist> (MIT License).
15. Input Output Sneaker 98% Neural Network Inferencing Fashion-MNIST dataset by Zalando Research, on GitHub <https://github.com/zalandoresearch/fashion-mnist> (MIT License).
16. Demo: ⬐ Object identification and image segmentation with magicat
17. https://www.npmjs.com/package/magicat
18. Install magicat $ npm install -g magicat + magicat@1.4.9 added 255 packages from 209 contributors in 11.798s
19. Animal photos by Susanne Nilsson, on Flickr <https://www.flickr.com/photos/infomastern/> (CC BY-SA 2.0). 23895621638_535be71dee_k.jpg 37038669284_899d7784a9_k.jpg 37489697170_31d05aa027_k.jpg 37699459356_24fd526a5e_k.jpg 37699976806_5ce694be36_k.jpg
20. Scan Directory with magicat $ magicat . --contains sheep Scanning directory '~/tfjs-demos/magicat' for sheep... Sheep found in: 37038669284_899d7784a9_k.jpg 37489697170_31d05aa027_k.jpg
21. Save Image Segment with magicat $ magicat 37699976806_5ce694be36_k.jpg --save horse The image '37699976806_5ce694be36_k.jpg' contains the following segments: background, horse. saved 37699976806_5ce694be36_k-horse.png Animal photos by Susanne Nilsson, on Flickr <https://www.flickr.com/photos/infomastern/> (CC BY-SA 2.0).
22. Demo: ⬐ Veremin, A Video Theremin Based on PoseNet
23. “The Octopus Project @ Chop Suey, Seattle, WA, 11/5/2007” by donte, on Flickr <https://flic.kr/p/3SA3vV>
24. ↳ http://ibm.biz/veremin
25. ↳ Deep Learning Code Patterns https://developer.ibm.com/patterns/category/model-asset-exchange/
26. Machine Learning Libraries Untitled by Marcin Wichary, on Flickr <https://flic.kr/p/68Lhc9> (CC BY 2.0). The Leeds Library by Michael D Beckwith, on Flickr <https://flic.kr/p/r6ip1G> (CC0 1.0).
29. Applying Deep Learning Untitled by Marcin Wichary, on Flickr <https://flic.kr/p/68Lhc9> (CC BY 2.0). Sharpest tool in the shed by Lachlan Donald, on Flickr <https://flic.kr/p/fkmB7T> (CC BY 2.0).
30. Data Science Expertise Computing Resources High-Quality Training Data Model Deployment Time Model Integration Inferencing Code And more… Sharpest tool in the shed by Lachlan Donald, on Flickr <https://flic.kr/p/fkmB7T> (CC BY 2.0).
31. ↳ IBM Developer Model Asset Exchange (MAX) https://developer.ibm.com/exchanges/models/ Classify Generate Recognize
32. Microservice Choose deployable model Deep Learning asset from the Model Asset Exchange (MAX) Deploy Swagger specification Inference endpoint Metadata endpoint Input preprocessing, model execution, and output post-processing Deploy model Use model https://developer.ibm.com/exchanges/models/
33. AI Lifecycle Deploy & Run Operate & Manage Prepare, Build & Train ⇄ ↳ ⬏
34. ↳ Preparing, Building, and Training AI Models Model Asset Exchange (MAX) Data Asset Exchange (DAX)
35. ⬏ Deploying and Running AI Models ONNX.js Model Asset Exchange (MAX)
36. ⇄ Operating and Managing AI Systems Trusted and Explainable AI • AI Fairness 360 Toolkit: Fairness metrics for machine learning models, explanations for these metrics, and algorithms to mitigate bias • Adversarial Robustness 360 Toolbox: Python library for adversarial attacks and defenses for neural networks • AI Explainability 360 Toolkit: Interpretability and explainability of data and machine learning models
37. Resources: Machine Learning Frameworks TensorFlow https://www.tensorflow.org/ Train your first neural network: basic classification | TensorFlow https://www.tensorflow.org/tutorials/keras/basic_classification Keras https://keras.io/ PyTorch https://pytorch.org/ Caffe2 https://caffe2.ai/
38. Resources: IBM Developer IBM Developer https://developer.ibm.com/ IBM Cloud https://ibm.biz/BdzXfW Center for Open-Source Data & AI Technologies (CODAIT) http://codait.org/ IBM Developer Model Asset Exchange (MAX) https://developer.ibm.com/exchanges/models/ Model Asset Exchange (MAX) Code Patterns https://developer.ibm.com/patterns/category/model-asset-exchange/ magicat https://github.com/CODAIT/magicat Veremin https://veremin.mybluemix.net/
39. Resources: Preparing, Building, and Training AI Models Project Jupyter https://jupyter.org/ IBM Developer Model Asset Exchange (MAX) https://developer.ibm.com/exchanges/models/ IBM Watson Knowledge Catalog https://www.ibm.com/cloud/watson-knowledge-catalog IBM Watson Studio https://www.ibm.com/cloud/watson-studio
40. Resources: Deploying and Running AI Models Data Mining Group (PMML & PFA) http://dmg.org/ ONNX https://onnx.ai/ ONNX.js https://github.com/Microsoft/onnxjs TensorFlow.js https://js.tensorflow.org/ TensorFlow Lite https://www.tensorflow.org/lite Core ML https://developer.apple.com/machine-learning/ IBM Watson Machine Learning https://www.ibm.com/cloud/machine-learning
41. Resources: Operating and Managing AI Systems Fabric for Deep Learning (FfDL) https://github.com/IBM/FfDL AI Fairness 360 Toolkit https://github.com/IBM/AIF360 Adversarial Robustness 360 Toolbox https://github.com/IBM/adversarial-robustness-toolbox AI Explainability 360 Toolkit https://github.com/IBM/AIX360/ IBM Watson OpenScale https://www.ibm.com/cloud/watson-openscale
42. What will you build with deep learning? Dynamic Earth - Continental Shelf by NASA Goddard Space Flight Center, on Flickr <https://flic.kr/p/ch8t25> (CC BY 2.0).
43. Thank you. Maureen McElaney Developer Advocate Center for Open-Source Data & AI Technologies ↳ (CODAIT) — @Mo_Mack | @ibmcodait | @IBMDeveloper medium.com/codait github.com/codait | github.com/IBM developer.ibm.com