Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.

Swift for TensorFlow - Tanmay Bakshi - Advanced Spark and TensorFlow Meetup - June 17, 2019

348 views

Published on

Title

TensorFlow + Swift + OpenAI's Unsupervised Sentiment Neuron, KubeFlow, TFX, Kubernetes, Kafka, Airflow, Jupyter, Scikit

Agenda

1. TensorFlow + Swift + OpenAI's Unsupervised Sentiment Neuron (45 mins)
Speaker: Tanmay Bakshi (https://www.linkedin.com/in/tanmay-bakshi-b15012a1)

(More details coming soon...)

2. KubeFlow, TFX, Kubernetes, Kafka, Airflow, Jupyter, Scikit, GPU/TPU, Kafka, Scikit-Learn and JupyterLab (15 mins)

(More details coming soon...)
** RSVP & LOGIN HERE **

Eventbrite:

https://www.eventbrite.com/e/1-hr-free-pipelineai-gpu-tpu-spark-tensorflow-kubernetes-kafka-scikit-tickets-45852865154

Meetup:

https://www.meetup.com/Advanced-Spark-and-TensorFlow-Meetup/events/kczhrpyxhbcc/

Zoom:

https://zoom.us/j/690414331
Webinar ID: 690 414 331

Phone:

+1 646 558 8656 (US Toll) or +1 408 638 0968 (US Toll)

Related Links

PipelineAI Home: https://pipeline.ai

PipelineAI Community Edition: http://community.pipeline.ai

PipelineAI GitHub: https://github.com/PipelineAI/pipeline

Advanced Spark and TensorFlow Meetup (SF-based, Global Reach): https://www.meetup.com/Advanced-Spark-and-TensorFlow-Meetup

YouTube Videos: https://youtube.pipeline.ai

SlideShare Presentations: https://slideshare.pipeline.ai

Slack Support:
https://joinslack.pipeline.ai

Web Support and Knowledge Base: https://support.pipeline.ai

Email Support: help@pipeline.ai

Published in: Software
  • Be the first to comment

Swift for TensorFlow - Tanmay Bakshi - Advanced Spark and TensorFlow Meetup - June 17, 2019

  1. 1. Tanmay Bakshi TED Speaker, Author, Algorithm-ist Swift for TensorFlow
  2. 2. First-class compiler support for Machine Learning
  3. 3. Native Automatic Differentiation
  4. 4. 1 1 + 𝑒$% 𝑤1 = 1 𝑤3 = 𝑤1 ⋅ −𝑤2 𝑤3, 𝑤5 = 𝑤3 𝑤7 = 𝑤5 ⋅ 𝑤5 ⋅ ln 𝑤6 𝑤8 = −𝑤7
  5. 5. Many-Language problem
  6. 6. C++Python Other
  7. 7. Swift PythonDockerfile
  8. 8. Julia Other
  9. 9. Why change?
  10. 10. Performance & optimizations Flexibility & Control Rapid Prototyping Interoperability & reverse-compatibility Gentle learning curve
  11. 11. TensorFlow is fundamentally not Pythonic. But we’re trying!
  12. 12. import as 10 8 3 3 10 def return for in 1 10000 with as -0.1 print
  13. 13. import var ref Float 10 8 var guess Float 3 3 var distance Float 10 func error Tensor Float Tensor Float Tensor Float Tensor Float let return for _ in 1 10000 let Tensor Float in return -0.1 Optimized for you.
  14. 14. It’s all in your hands.
  15. 15. Write code naturally, Swift for TensorFlow takes care of the rest.
  16. 16. import extension Tensor where Scalar TensorFlowFloatingPoint func squaredError Tensor Scalar Tensor Scalar return self
  17. 17. 1.05x Circle-edge finding 0.95x MLPNN Training Without Compiler Optimizations!
  18. 18. Forcibly safe code
  19. 19. Interoperability!
  20. 20. import let np "numpy" let p1 2 -2 let p2 10 8 func norm PythonObject PythonObject return 2 let ex
  21. 21. Code time!
  22. 22. @TajyMany tanmay bakshi Tanmay Bakshi TajyMany

×