Presentation: Recent Deep Learning Advancements, Revolution Far from Over!

Track: Deep Learning Applications & Practices

Location: Cyril Magnin I

Duration: 9:20am - 10:10am

Day of week: Wednesday

Share this on:

Abstract

It started with Backpropagation, convolutional neural networks and RMSprop but these are only the first wave of innovations that got deep learning started. Some recent advancements include Population Based Training (PBT), Neuroevolution and ML Managed Services. We will discuss how deep learning research is becoming increasingly automated, more computationally efficient and serverless.

You will learn tips on how to automatically search the best deepnet topology for your task, scale up your machine learning experiments to run on thousands of nodes and to do hyperparameter optimization like pro!

Speaker: Arshak Navruzyan

Founder @fellowshipai

Arshak Navruzyan is a machine learning focused technology leader. He founded Fellowship.AI applied machine learning fellowship program and is the Chief Technology Officer at Sentient Technologies. He has delivered AI solutions for multi-billion dollar quantitative hedge funds, numerous venture funded startups and some of the largest telecoms in the world. Arshak has been in technology leadership roles at Argyle Data, Alpine Data Labs, Endeca/Oracle.

Find Arshak Navruzyan at

Proposed Tracks

  • Real-World Data Engineering

    Showcasing DataEng tech and highlighting the strengths of each in real-world applications.

  • Deep Learning Applications & Practices

    Deep learning lessons using Tensorflow, Keras, PyTorch, Caffe across machine translation, computer vision.

  • AI Meets the Physical World

    The track where AI touches the physical world, think drones, ROS, NVidea, TPU and more.

  • Data Architectures You've Always Wondered About

    How did they do that? Real-time predictive pipelines at places like Uber, Self-Driving Cars at Google, Robotic Warehouses from Ocado in the UK, are all possible examples.

  • Applied ML for Software

    Practical machine learning inside the data centers and on software engineering teams.

  • Time Series Patterns & Practices

    Stocks, ad tech/real-time bidding, and anomaly detection. Patterns and practices for more effective Time Series work.