In that LSTM example, I see year ahead having the Discuss Travellers dataset.
This video was recorded in San Francisco on February 5th, 2019.
Slides from the session can be viewed here: https://www.slideshare.net/0xdata/marios-michailidis-mathias-muller-h2oai-time-series-with-h2o-driverless-ai-h2o-world-san-francisco
Driverless AI is H2O.ai’s latest flagship product for automatic machine learning. It fully automates some of the most challenging and productive tasks in applied data science such as feature engineering, model tuning, model ensembling and model deployment. Driverless AI turns Kaggle-winning grandmaster recipes into production-ready code, and is specifically designed to avoid common mistakes such as under- or overfitting, data leakage or improper model validation, some of the hardest challenges in data science. Avoiding these pitfalls alone can save weeks or more for each model, and is necessary to achieve high modeling accuracy.
Driverless AI is now equipped with time-series functionality. Time series helps forecast sales, predict industrial machine failure and more. With the time series capability in Driverless AI, H2O.ai directly addresses some of the most pressing concerns of organizations across industries for use cases such as transactional data in capital markets, in retail to track in-store and online sales, and in manufacturing with sensor data to improve supply chain or predictive maintenance.
Bio: Marios Michailidis is a Competitive Data Scientist at H2O.ai. He holds a Bsc in accounting Finance from the University of Macedonia in Greece, an Msc in Risk Management from the University of Southampton and a PhD in machine learning at from UCL . He has worked in both marketing and credit sectors in the UK Market and has led many analytics’ projects with various themes including: acquisition, retention, recommenders, fraud detection, portfolio optimization and more. He is the creator of KazAnova, a freeware GUI for credit scoring and data mining 100% made in Java as well as is the creator of StackNet Meta-Modelling Framework. In his spare time he loves competing on data science challenges and was ranked 1st out of 500,000 members in the popular Kaggle.com data competition platform. He currently ranks 3rd.
Bio: A Kaggle Grandmaster and a Data Scientist at H2O.ai, Mathias Müller holds an AI and ML focused diploma (eq. M.Sc.) in computer science from Humboldt University in Berlin. During his studies, he keenly worked on computer vision in the context of bio-inspired visual navigation of autonomous flying quadrocopters. Prior to H2O.ai, he as a machine learning engineer for FSD Fahrzeugsystemdaten GmbH in the automotive sector. His stint with Kaggle was a chance encounter as he stumbled upon the data competition platform while looking for a more ML-focused platform as compared to TopCoder. This is where he entered his first predictive modeling competition and climbed up the ladder to be a Grandmaster. He is an active contributor to XGBoost and is working on Driverless AI with H2O.ai.
In the first half of this video, Jo-Fai will share his joyful (yet sometimes very painful) Kaggle experience since joining the data mining competition platform. Coming from a rather traditional engineering background, data science was once like a complete myth to him. Joe will explain why participating in Kaggle is one of the most effective ways to kick-start a data science career. He will also explain how he used H2O for two Kaggle competitions: Rossmann Store Sales (2015) and Santander Product Recommendation (2016).
View slides here: http://bit.ly/2lsrD3F
Jo-fai (or Joe) Chow is a data scientist at H2O.ai. Before joining H2O, he was in the business intelligence team at Virgin Media in UK where he developed data products to enable quick and smart business decisions. He also worked remotely for Domino Data Lab in US as a data science evangelist promoting products via blogging and giving talks at meetups. Joe has a background in water engineering. Before his data science journey, he was an EngD research engineer at STREAM Industrial Doctorate Centre working on machine learning techniques for drainage design optimization. Prior to that, he was an asset management consultant specialized in data mining and constrained optimization for the utilities sector in UK and abroad. He also holds a MSc in Environmental Management and a BEng in Civil Engineering.
In the second part of this talk, Abhishek will present his research in applying deep learning for time series prediction. He is focused on applying these new methods in the field of astronomy to light curves.
View slides here: http://bit.ly/2mLX4qF
Abhishek Malali is a Master’s of Engineering student at Harvard University specializing in Computational Sciences. He focuses on applying machine learning research to time series applications. Currently he is working on time series prediction on irregular time series using deep learning architectures.
Just watched my video on how to make up to 2.1 billion? Do you have so much money that you don’t know where to start spending? While that‘s fine and dandy, I would much rather continue to get richer and richer!!! What can I say? I eat greed for breakfast, lunch, and dinner! If you are interested in turning your fortunes into wealth that will last centuries! Check out this video, I hope you enjoy!
Thank you all for watching!!!
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In this video we walk through the basics of 5G and we try to identify 5G companies that have some sort of Moat in their home markets that could lead to greater profits as the 5G network Expands.
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Investing Basics Playlist
Investing Books I like:
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For downloadable versions of these lectures, please go to the following link:
This lecture provides an overview of Time Series forecasting techniques and the process of creating effective forecasts. We will go through some of the popular statistical methods including time series decomposition, exponential smoothing, Holt-Winters, ARIMA, and GLM Models. These topics will be discussed in detail and we will go through the calibration and diagnostics effective time series models on a number of diverse datasets.
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This video shows the usage and performance for the genetic algo. S&P500 Index daily chart with RSI and cycles combination. GA evolves possible trading systems.
Once we’ve decided on the most suitable model for a particular prediction problem, how do we predict for new data? How do we save our new predictions to Excel to share them?
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Books that have helped us on our Financial Independence Journey
The Bogleheads‘ Guide to Investing: https://amzn.to/2UjEc4j
The Intelligent Investor: https://amzn.to/2MKGokh
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Our Rich Journey, We Invested ,000 Today & We’re Sharing Our Stock Portfolio (Ep. 1 – Dividend Income): We decided to invest ,000 in high dividend stocks. We selected ten stocks and we’re sharing those stocks and our portfolio with you. In this video, we share with you the ten stocks that we invested in, our criteria for picking the stocks that we selected, and we show you our gains and losses on the stocks to date. We’ll also be investing an additional 3 each month (roughly ,000 a year). So, each month, we’ll share our high dividend individual stock portfolio with you, including our gains and losses!
If you like our video, please make sure to „like“ the video and subscribe to our channel. We post two videos a week related to our journey towards financial independence, including making money, saving money, and investing money. Make sure to check out all our videos and . . . join the journey!
Check out our most recent Our Rich Journey videos:
End Of Year Money Moves – 2018: https://youtu.be/HmHNAHMLdYs
Achieving Financial Independence in a Consumer Culture: https://youtu.be/Mfgq0VI6U5Q
How to Retire Early & Afford Healthcare (Less than 0/month for Family): https://youtu.be/pApBuNJZflU
Recession Coming – How to Thrive: https://youtu.be/XEAmANYN4kY
Exposing Bad Money Advice: https://youtu.be/DFIGzRyoIbg
We are not financial advisors. Our videos are for educational purposes only and merely cite our own personal opinions. In order to make the best financial decision that suits your own needs, you must conduct your own research and seek the advice of a licensed financial advisor if necessary. Know that all investments involve some form of risk and there is no guarantee that you will be successful in making, saving, or investing money; nor is there any guarantee that you won’t experience any loss when investing. Always remember to make smart decisions and do your own research!
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Find out more about URANIUM INVESTING now: https://katusaresearch.com/nextbigrush These are 3 mining stocks I am buying in April.
$ ANF Anfield Gold, with a mention of $ LUM Lumina Gold
$ F Fiore Exploration
$ SWA Sarama Resources
Look our for part II!
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