Presented By
O’Reilly + Cloudera
Make Data Work
29 April–2 May 2019
London, UK

Schedule: Deep Learning sessions

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9:00 - 17:00 Monday, 29 April & Tuesday, 30 April
Data Science, Machine Learning & AI
Location: London Suite 3
Amir Issaei (Databricks)
The course covers the fundamentals of neural networks and how to build distributed Keras/TensorFlow models on top of Spark DataFrames. Throughout the class, you will use Keras, TensorFlow, Deep Learning Pipelines, and Horovod to build and tune models. You will also use MLflow to track experiments and manage the machine learning lifecycle. NOTE: This course is taught entirely in Python. Read more.
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9:00 - 17:00 Monday, 29 April & Tuesday, 30 April
Ana Hocevar (The Data Incubator)
The TensorFlow library provides for the use of computational graphs, with automatic parallelization across resources. This architecture is ideal for implementing neural networks. This training will introduce TensorFlow's capabilities in Python. It will move from building machine learning algorithms piece by piece to using the Keras API provided by TensorFlow with several hands-on applications. Read more.
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9:00 - 17:00 Monday, 29 April & Tuesday, 30 April
Data Science, Machine Learning & AI
Location: Capital Suite 7
Ian Cook (Cloudera)
Advancing your career in data science requires learning new languages and frameworks—but learners face an overwhelming array of choices, each with different syntaxes, conventions, and terminology. Ian Cook simplifies the learning process by elucidating the abstractions common to these systems. Through hands-on exercises, you'll overcome obstacles to getting started using new tools. Read more.
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9:0012:30 Tuesday, 30 April 2019
Data Science, Machine Learning & AI
Location: Capital Suite 4
Amy Unruh (Google)
This tutorial provides an introduction to designing and building machine learning models on Google Cloud Platform. Through a combination of presentations, demos, and hand-ons labs, you’ll learn machine learning (ML) and TensorFlow concepts, and develop skills in developing, evaluating, and productionizing ML models. Read more.
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13:3017:00 Tuesday, 30 April 2019
Data Science, Machine Learning & AI
Location: Capital Suite 14
Alexander Thomas, Claudiu Branzan (G2 Web Services)
This is a hands-on tutorial for scalable NLP using the highly performant, highly scalable open-source Spark NLP library. You’ll spend about half your time coding as you work through four sections, each with an end-to-end working codebase that you can change and improve. Read more.
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13:3017:00 Tuesday, 30 April 2019
Data Science, Machine Learning & AI
Location: Capital Suite 4
Amy Unruh (Google)
This tutorial provides an introduction to designing and building machine learning models on Google Cloud Platform. Through a combination of presentations, demos, and hand-ons labs, you’ll learn machine learning (ML) and TensorFlow concepts and develop skills in developing, evaluating, and productionizing ML models. Read more.
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13:3017:00 Tuesday, 30 April 2019
Data Science, Machine Learning & AI
Location: Capital Suite 15
Francesca Lazzeri (Microsoft), Aashish Bhateja (Microsoft)
Time series modeling and forecasting has fundamental importance to various practical domains and, during the past few decades, machine learning model-based forecasting has become very popular in the private and the public decision-making process. In this tutorial, we will walk you through the core steps for using Azure Machine Learning to build and deploy your time series forecasting models. Read more.
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11:1511:55 Wednesday, 1 May 2019
Data Engineering and Architecture
Location: Capital Suite 8/9
Moty Fania (Intel)
In this session, Moty Fania will share his experience of implementing a Sales AI platform. It handles processing of millions of website pages and sifting thru millions of tweets per day. The platform is based on unique open source technologies and was designed for real-time, data extraction and actuation. This session highlights the key learnings with a thorough review of the architecture. Read more.
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11:1511:55 Wednesday, 1 May 2019
Data Science, Machine Learning & AI
Location: Capital Suite 17
Sami Niemi (Barclays)
Predicting transaction fraud of debit and credit card payments in real-time is an important challenge, which state-of-art supervised machine learning models can help to solve. Barclays has been developing and testing different solutions and will show how well different models perform in variety of situations like card present and card not present debit and credit card transactions. Read more.
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11:1511:55 Wednesday, 1 May 2019
Data Science, Machine Learning & AI
Location: Capital Suite 14
In this talk you will learn how to use Spark NLP and Apache Spark to standardize semi-structured text. You will see how Indeed standardizes resume content at scale. Read more.
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12:0512:45 Wednesday, 1 May 2019
Data Science, Machine Learning & AI
Location: Capital Suite 17
Arun Kejariwal (Independent), Ira Cohen (Anodot)
Recently, Sequence-2-Sequence has also been used for applications based on time series data. In this talk, we first overview S2S and the early use cases of S2S. Subsequently, we shall walk through how S2S modeling can be leveraged for the aforementioned use cases, viz., real-time anomaly detection and forecasting. Read more.
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14:0514:45 Wednesday, 1 May 2019
Data Science, Machine Learning & AI
Location: Capital Suite 17
David Low (Pand.ai)
Transfer Learning has been proven to be a tremendous success in the Computer Vision field as a result of ImageNet competition. In the past months, the Natural Language Processing field has witnessed several breakthroughs with transfer learning, namely ELMo, OpenAI Transformer, and ULMFit. In this talk, David will be showcasing the use of transfer learning on NLP application with SOTA accuracy. Read more.
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14:5515:35 Wednesday, 1 May 2019
Data Science, Machine Learning & AI
Location: Expo Hall (Capital Hall N24)
Wolff Dobson (Google)
In this talk, we will cover the latest in TensorFlow, both for beginners and for developers migrating from 1.x to 2.0. We'll cover the best ways to set up your model, feed your data to it, and distribute it for fast training. We'll also look at how TensorFlow has been recently upgraded to be more intuitive. Read more.
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16:3517:15 Wednesday, 1 May 2019
Data Science, Machine Learning & AI
Location: Capital Suite 17
Guoqiong Song (Intel)
Collecting and processing massive time series data (e.g., logs, sensor readings, etc.), and detecting the anomalies in real time is critical for many emerging smart systems, such as industrial, manufacturing, AIOps, IoT, etc. This talk will share how to detect anomalies of time series data using Analytics Zoo and BigDL at scale on a standard Spark cluster. Read more.
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11:1511:55 Thursday, 2 May 2019
Data Science, Machine Learning & AI
Location: Capital Suite 17
Scott Stevenson (Faculty)
Modern deep learning systems allow us to build speech synthesis systems with the naturalness of a human speaker. Whilst there are myriad benevolent applications, this also ushers in a new era of fake news. This talk will explore the danger of such systems, as well as how deep learning can also be used to build countermeasures to protect against political disinformation. Read more.
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12:0512:45 Thursday, 2 May 2019
Data Science, Machine Learning & AI
Location: Capital Suite 14
Moshe Wasserblat presents an overview of NLP Architect, an open source DL NLP library that provides SOTA NLP models making it easy for researchers to implement NLP algorithms and for data scientists to build NLP based solution for extracting insight from textual data to improve business operations. Read more.
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14:0514:45 Thursday, 2 May 2019
Data Science, Machine Learning & AI
Location: Capital Suite 17
Deep Learning has enabled massive breakthroughs in offbeat tracks and has enabled better understanding of how an artist paints, how an artist composes music and so on. As part of Nischal & Raghotham’s loved project - Deep Learning for Humans, they want to build a font classifier and showcase to masses how fonts : * Can be classified * Understand how and why two or more fonts are similar Read more.
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14:0514:45 Thursday, 2 May 2019
Data Science, Machine Learning & AI
Location: Expo Hall (Capital Hall N24)
Alex Jaimes (Dataminr)
When emergency events occur, social signals and sensor data are generated. In this talk, I will describe how Machine Learning and Deep Learning are applied in processing large amounts of heterogeneous data from various sources in real time, with a particular focus on how such information can be used for emergencies and in critical events for first responders and for other social good use cases. Read more.
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14:5515:35 Thursday, 2 May 2019
Data Science, Machine Learning & AI
Location: Expo Hall (Capital Hall N24)
Oliver Gindele (Datatonic)
The success of Deep Learning has reached the realm of structured data in the past few years where neural network have shown to improve the effectiveness and predictability of recommendation engines. This session will give a brief overview of such deep recommender systems and how they can be implemented in TensorFlow. Read more.
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14:5515:35 Thursday, 2 May 2019
Data Science, Machine Learning & AI
Location: Capital Suite 17
Yoav Einav (GigaSpaces)
Technological advancements are transforming customer experience, and businesses are beginning to benefit from Deep Learning innovations to automate call center routing to the most proper agent. This session will discuss how Deep Learning models can be run with Intel BigDL and Spark frameworks co-located on an in-memory computing platform to enhance the customer experience without the need for GPUs Read more.