Building deep reinforcement learning applications on BigDL and Spark
[size=1.2]Arsenii Mustafin (Fudan University)
[size=1.5]Deep reinforcement learning is a thriving area and has wide applications in industry. Arsenii Mustafin shares his experience developing deep reinforcement learning applications on BigDL and Spark.
Convergence of modalities in language technology
[size=1.2]Hassan Sawaf (Amazon Web Services)
[size=1.5]With today’s device and user interface technology and also the advent of advanced machine learning and deep learning models, input and output modalities are converging in many different dimensions. Hassan Sawaf offers a brief overview of research in human language technology and machine learning in merging information that is captured by the senses of machines.
Democratizing deep reinforcement learning
[size=1.2]Danny Lange (Unity Technologies)
[size=1.5]Danny Lange offers an overview of deep reinforcement learning, an exciting new chapter in AI’s history that is changing the way we develop and test learning algorithms that can later be used in real life.
Get your hard hat: Intelligent industrial systems with deep reinforcement learning
[size=1.2]Mark Hammond (Bonsai)
[size=1.5]Mark Hammond explores a wide breadth of real-world applications of deep reinforcement learning, including robotics, manufacturing, energy, and supply chain. Mark also shares best practices and tips for building and deploying these systems, highlighting the unique requirements and challenges of industrial AI applications.
Modernizing the healthcare industry with AI
[size=1.2]Arjun Bansal (Intel)
[size=1.5]Artificial intelligence is transforming every industry, but the role it will play in healthcare is profound. Arjun Bansal explains how AI can give physicians new insights and speed time to diagnosis by leveraging vast amounts of healthcare data and how it can reduce the time and money spent to develop new medicines.
Practical considerations when shifting to using deep learning for your text data
[size=1.2]Emmanuel Ameisen (Insight Data Science), Yan Kou (Insight Data Science)
[size=1.5]Emmanuel Ameisen and Yan Kou share a guide for moving your company toward deep learning using a collection of NLP best practices gathered from conversations with 75+ teams from Google, Facebook, Amazon, Twitter, Salesforce, Airbnb, Capital One, Bloomberg, and others.
Turning machine learning research into products for industry
[size=1.2]Reza Zadeh (Matroid | Stanford)
[size=1.5]Reza Zadeh details three challenges on the way to building cutting-edge ML products, with a focus on computer vision, offering examples, recommendations, and lessons learned.
Understanding automation
[size=1.2]Ben Lorica (O'Reilly Media), Roger Chen (Computable Labs)