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.
( TensorFlow Training – https://www.edureka.co/ai-deep-learning-with-tensorflow )
This Edureka Recurrent Neural Networks tutorial video (Blog: https://goo.gl/4zxMfU) will help you in understanding why we need Recurrent Neural Networks (RNN) and what exactly it is. It also explains few issues with training a Recurrent Neural Network and how to overcome those challenges using LSTMs. The last section includes a use-case of LSTM to predict the next word using a sample short story
Below are the topics covered in this tutorial:
1. Why Not Feedforward Networks?
2. What Are Recurrent Neural Networks?
3. Training A Recurrent Neural Network
4. Issues With Recurrent Neural Networks – Vanishing And Exploding Gradient
5. Long Short-Term Memory Networks (LSTMs)
6. LSTM Use-Case
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Check our complete Deep Learning With TensorFlow playlist here: https://goo.gl/cck4hE
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How it Works?
1. This is 21 hrs of Online Live Instructor-led course. Weekend class: 7 sessions of 3 hours each.
2. We have a 24×7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course.
3. At the end of the training you will have to undergo a 2-hour LIVE Practical Exam based on which we will provide you a Grade and a Verifiable Certificate!
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About the Course
Edureka’s Deep learning with Tensorflow course will help you to learn the basic concepts of TensorFlow, the main functions, operations and the execution pipeline. Starting with a simple “Hello Word” example, throughout the course you will be able to see how TensorFlow can be used in curve fitting, regression, classification and minimization of error functions. This concept is then explored in the Deep Learning world. You will evaluate the common, and not so common, deep neural networks and see how these can be exploited in the real world with complex raw data using TensorFlow. In addition, you will learn how to apply TensorFlow for backpropagation to tune the weights and biases while the Neural Networks are being trained. Finally, the course covers different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks and Autoencoders.
Delve into neural networks, implement Deep Learning algorithms, and explore layers of data abstraction with the help of this Deep Learning with TensorFlow course.
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Who should go for this course?
The following professionals can go for this course:
1. Developers aspiring to be a ‚Data Scientist‘
2. Analytics Managers who are leading a team of analysts
3. Business Analysts who want to understand Deep Learning (ML) Techniques
4. Information Architects who want to gain expertise in Predictive Analytics
5. Professionals who want to captivate and analyze Big Data
6. Analysts wanting to understand Data Science methodologies
However, Deep learning is not just focused to one particular industry or skill set, it can be used by anyone to enhance their portfolio.
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Why Learn Deep Learning With TensorFlow?
TensorFlow is one of the best libraries to implement Deep Learning. TensorFlow is a software library for numerical computation of mathematical expressions, using data flow graphs. Nodes in the graph represent mathematical operations, while the edges represent the multidimensional data arrays (tensors) that flow between them. It was created by Google and tailored for Machine Learning. In fact, it is being widely used to develop solutions with Deep Learning.
Machine learning is one of the fastest-growing and most exciting fields out there, and Deep Learning represents its true bleeding edge. Deep learning is primarily a study of multi-layered neural networks, spanning over a vast range of model architectures. Traditional neural networks relied on shallow nets, composed of one input, one hidden layer and one output layer. Deep-learning networks are distinguished from these ordinary neural networks having more hidden layers, or so-called more depth. These kinds of nets are capable of discovering hidden structures within unlabeled and unstructured data (i.e. images, sound, and text), which constitutes the vast majority of data in the world.
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