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All Hell Breaks Loose 6.6.23.

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H2O.ai is a software platform that offers a host of machine learning algorithms, as well as one deep net model. It also provides sophisticated data munging, an intuitive UI, and several built-in enhancements for handling data. However, the tools must be run on your own hardware.

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H2O.ai was founded by SriSatish Ambati, Cliff Click, and Arno Candel. In addition to its only deep net – a vanilla MLP – the platform offers a variety of models like GLM, Distributed Random Forest, Naive Bayes, a K-Means clustering model, and a few others. H2O.ai can be linked to multiple data sources in order to train data loads.

The UI is highly intuitive, but you can also work with the tools through other apps like Tableau or Excel. These interfaces allow you to set up a deep net by configuring its hyper-parameters.

H2O.ai needs to be deployed and maintained on your own hardware, which may be a limiting factor. However, the platform comes with many performance enhancements like in-memory map-reduce, columnar compression, and distributed parallel processing. Depending on your hardware’s capabilities, training on big data sets could be completed in a reasonable amount of time. As an added note, it’s unclear whether or not GPU support is a built-in feature at this point in time.

Do you have any experience with the H2O.ai platform? Please comment and share your thoughts.

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https://www.linkedin.com/in/danielpartynski
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This Edureka Live video on "Database Testing using Selenium" will give you brief insights about JDBC and also tell you how to perform database testing using Selenium. This video will cover the following topics:
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00:07:35 STEPS TO CREATE JDBC APPLICATION
00:15:20 DATABASE TESTING USING SELENIUM

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Got a question on the topic? Please share it in the comment section below and our experts will answer it for you.

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This Edureka video on "Artificial Intelligence With Python" will provide you with a comprehensive and detailed knowledge of Artificial Intelligence concepts with hands-on examples.
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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 24x7 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.

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Who should go for this course?

The following professionals can go for this course:

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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

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For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free).

MIT Introduction to Deep Learning 6.S191: Lecture 5
Robust and Trustworthy Deep Learning
Lecturer: Sadhana Lolla (Themis AI, https://themisai.io)
2023 Edition

For all lectures, slides, and lab materials: http://introtodeeplearning.com​

Lecture Outline
0:00 - Introduction and Themis AI
3:46 - Background
7:29 - Challenges for Robust Deep Learning
8:24 - What is Algorithmic Bias?
14:13 - Class imbalance
16:25 - Latent feature imbalance
20:30 - Debiasing variational autoencoder (DB-VAE)
23:24 - DB-VAE mathematics
27:40 - Uncertainty in deep learning
29:50 - Types of uncertainty in AI
32:48 - Aleatoric vs epistemic uncertainty
33:29 - Estimating aleatoric uncertainty
37:42 - Estimating epistemic uncertainty
44:11 - Evidential deep learning
46:44 - Recap of challenges
47:14 - How Themis AI is transforming risk-awareness of AI
49:30 - Capsa: Open-source risk-aware AI wrapper
51:51 - Unlocking the future of trustworthy AI


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