On April 12th, 2018 I gave a talk about Explaining complex machine learning models with LIME at the Hamburg Data Science Meetup - so if you’re intersted: the slides can be found here: https://www.slideshare.net/ShirinGlander/hh-data-science-meetup-explaining-complex-machine-learning-models-with-lime-94218890 Traditional machine learning workflows focus heavily on model training and optimization; the best model is usually chosen via performance measures like accuracy or error and we tend to assume that a model is good enough for deployment if it passes certain thresholds of these performance criteria.
These are my sketchnotes for Sam Charrington’s podcast This Week in Machine Learning and AI about Systems and Software for Machine Learning at Scale with Jeff Dean: Sketchnotes from TWiMLAI talk #124: Systems and Software for Machine Learning at Scale with Jeff Dean You can listen to the podcast here. n this episode I’m joined by Jeff Dean, Google Senior Fellow and head of the company’s deep learning research team Google Brain, who I had a chance to sit down with last week at the Googleplex in Mountain View.
On April 4th, 2018 I gave a talk about Deep Learning with Keras at the Ruhr.Py Meetup in Essen, Germany. The talk was not specific to Python, though - so if you’re intersted: the slides can be found here: https://www.slideshare.net/ShirinGlander/ruhrpy-introducing-deep-learning-with-keras-and-python Ruhr.PY - Introducing Deep Learning with Keras and Python von Shirin Glander There is also a video recording of my talk, which you can see here: https://youtu.
I’ll be talking about Deep Learning with Keras in R and Python at the following upcoming meetup: Ruhr.Py 2018 on Wednesday, April 4th Introducing Deep Learning with Keras and Python Keras is a high-level API written in Python for building and prototyping neural networks. It can be used on top of TensorFlow, Theano or CNTK. In this talk we build, train and visualize a Model using Python and Keras - all interactive with Jupyter Notebooks!
These are my sketchnotes for Sam Charrington’s podcast This Week in Machine Learning and AI about Scaling Machine Learning at Uber with Mike Del Balso: Sketchnotes from TWiMLAI talk #115: Scaling Machine Learning at Uber with Mike Del Balso You can listen to the podcast here. In this episode, I speak with Mike Del Balso, Product Manager for Machine Learning Platforms at Uber. Mike and I sat down last fall at the Georgian Partners Portfolio conference to discuss his presentation “Finding success with machine learning in your company.
These are my sketchnotes for Sam Charrington’s podcast This Week in Machine Learning and AI about Learning “Common Sense” and Physical Concepts with Roland Memisevic: Sketchnotes from TWiMLAI talk #111: Learning “Common Sense” and Physical Concepts with Roland Memisevic You can listen to the podcast here. In today’s episode, I’m joined by Roland Memisevic, co-founder, CEO, and chief scientist at Twenty Billion Neurons. Roland joined me at the RE•WORK Deep Learning Summit in Montreal to discuss the work his company is doing to train deep neural networks to understand physical actions.
On Wednesday, April 25th 2018 I am going to talk about explainability of machine learning models at the Minds Mastering Machines conference in Cologne. The conference will be in German, though. ERKLÄRBARKEIT VON MACHINE LEARNING: WIE KÖNNEN WIR VERTRAUEN IN KOMPLEXE MODELLE SCHAFFEN? Mit Machine-Learning getroffene Entscheidungen sind inhärent schwierig – wenn nicht gar unmöglich – nachzuvollziehen. Die Komplexität einiger der besten Modelle, wie Neuronale Netzwerke, ist genau das, was sie so erfolgreich macht.
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