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

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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


  • Deep Learning Applications & Practices

    Deep learning lessons using tooling such as Tensorflow & PyTorch, across domains like large-scale cloud-native apps and fintech, and tacking concerns around interpretability of ML models.

  • Predictive Data Pipelines & Architectures

    Best practices for building real-world data pipelines doing interesting things like predictions, recommender systems, fraud prevention, ranking systems, and more.

  • ML in Action

    Applied track demonstrating how to train, score, and handle common machine learning use cases, including heavy concentration in the space of security and fraud

  • Real-world Data Engineering

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