ODSC Europe 2018 (Open Data Science Conference) (Part Two)

Bo0mB0oma · March 30, 2019

ODSC Europe 2018 (Open Data Science Conference) (Part Two)

MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 20 Hours | 21.2 GB

Genre: eLearning | Language: English

Royalties for this video set help fund the ODSC Grant Award for open source data science projects.

The Open Data Science Conference has established itself as the leading conference in the field of applied data science. Each ODSC event offers a unique opportunity to learn directly from the core contributors, experts, academics and renowned instructors helping shape the field of data science and artificial intelligence

Presentations cover not only data science modeling but also the languages and tools needed to deploy these models in the real world such as TensorFlow, MXNet, scikit-learn, Kubernetes, and many more.

Our conferences are organized around focus areas to ensure our attendees are at the forefront of this fast emerging field and current with the latest data science languages, tools, and models. You'll find in our East 2018 video catalog some of our most popular focus areas including:

Deep Learning and Machine Learning

Over the last 5 years, we have seen incredible advances in the field of data science thanks to breakthroughs in neural networks, transfer learning, reinforcement learning, and generative adversarial networks (GANs) to name a few. With the advent of Google Voice, Alexa, and other voice assistants, presentations on enabling technologies like NLP, RNNs, and LSTM are popular. Some session to note:

Machine Learning in R Part I-Jared Lander

Machine Learning in R Part II-Jared Lander

Machine learning and statistics: Don't mind the gap-Thomas Wiecki, PhD

Deep Learning for Developers-Julien Simon

Target Leakage in Machine Learning-Yuriy Guts

Ensemble Models Demystified-Kevin Lemagnen

Towards Interpretable Deep Learning-Dr. Wojciech Samek

Delivering Machine Learning at Scale ‚Äì An agile approach to Model Governance-Thomas Cronin

An Introduction to Active Learning-Jennifer Prendki, PhD

Introduction to Machine Learning-Andreas Mueller, PhD

Linguistics in NLP: why so complex?-Mariana Romanyshyn

Deep Learning for Mail Processing-Alexandre Hubert

Introduction to Automatic and Interpretable Machine Learning with H2O and LIME-Jo-fai Chow, PhD

Elegant Machine Learning workshop with Julia and Flux-Avik Sengupta

Deep Learning for Recommender Systems-Oliver Gindele, PhD

Intermediate Machine Learning with scikit-learn-Andreas Mueller, PhD

Multivariate Time Series Forecasting Using Statistical and ML Models-Jeffrey Yau

Democratize Conversational AI-Scaling Academic Research to Industrial Applications-Pei-Hao, PhD

Racing an Autonomous Toy Car from Scratch-Constant Bridon

How to Learn Many, Many Labels with ML-Dr. Michael Swarbrick Jones

Deploying Large Spark Models and scoring in Real time at Scale-Subhojit Banerjee

Resolving Corporate Entities in the International Supply Chain Graph-Timothy Garnett

GPUs Transforming Open Source Integration in Intelligent IoT Networks-Dr. Mo Haghighi

Automated ML: Drink your Coffee and Let the Machine Work for You-Marius Lindauer, PhD

Learning a Universal Latent Space for Accelerated Drug Discovery-Mason Victors

Making Sense of Twitter @Bloomberg For Finance-Edgar Meij

Core Data Science and Data Visualization

As data science advances at a rapid pace, core skills are more important than ever. Our sessions range from beginner to advanced level for core topics. Additionally, data and models need to be actionable and data visualization remains a key skill in any data scientist's toolkit. Some session of note include:

Introduction to Data Science-A Practical Viewpoint-Jesus Rogel-Salazar

Accelerate AI | Fuelling the AI Revolution with Gaming-Alison Lownde

How to Play Fantasy Sports Strategically (and Win)-Dr. Martin Haugh

Understanding Unstructured Data with Language Models-Alex Peattie

Handling Missing Data in Python/Pandas and R-Alexandru Agachi

Competitive Model Stacking: An Introduction to Stacknet Meta Modelling Framework-Marios Michailidis, PhD

Inside the Black Box: How Does a Neural Network Understand Names?-Kfir Bar, PhD

AI Nudging: Data, Privacy, and Manipulation-Karina Vold, PhD

Zen and the Art of Model Maintenance-Joseph Blue

Building Interactive Dashboards in Python: A Hands-on Introduction for Data Scientists-Dr. Pascal Bugnion

Multi-task Learning-Shioulin Sam, PhD

A Gentle Introduction to Survival Models with Applications in Python and R-Violeta Misheva, PhD

Learning Functions: Understanding Gradient Descent, Backpropagation, and Vanishing Gradients-Dr. John D. Kelleher

Big Data Trade FX & Python in Finacial Markets-Saeed Amen

Telling Human stories with Data-Alan Rutter

From Numbers to Narrative: Data Storytelling-Isaac Reyes

Data Science, Management, And Business

Data science is permeating every industry as adoption gathers pace. The management and practice of data science will become increasingly strategically important to all industries including finance and healthcare. Hear from leading experts on important topics including:

Accelerate AI | The people problem of Artificial intelligence-Hugo Pinto

Accelerate AI | Your brain is too small to manage your business-Christopher Bishop

AI Challenges and Opportunities-Luciano Floridi

Accelerate AI | AI For Good-Fact Or Fiction?-Dr Sybil Wong

Accelerate AI | Personalising finance-Igor Volzhanin

Accelerate AI | AI in Business Forecasting: Lessons from Building an Intelligent Cashflow Engine-Johnnie Ball

Peer detection with Massive Payment Transaction Network-Zhe Sun

Data Science Driven Digital Transformation-Kanishka Bhattacharya, PhD

Accelerating AI Innovation in Banking at Scale-Jesper Nordstr+∂m

Accelerate AI | AI in Banking : Challenges, Opportunities and Future-Arif Khan

Accelerate AI | Data Science for Vaccines Research and Development-Dr Duccio Medini

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