How to Use Tensorboard (LIVE)

We’re going to learn how the visualizer that comes with Tensorflow works in this live stream. We’ll go through a bunch of different features and test out its functionality both programmatically and visually.


4:41 code begins
37:07 tensorboard visualization begins

Code for this video:
https://github.com/llSourcell/how_to_…

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More Learning resources:
https://www.tensorflow.org/get_starte…
http://ischlag.github.io/2016/06/04/h…
https://www.youtube.com/watch?v=3bown…
https://blog.altoros.com/visualizing-…
http://www.titiapps.com/hands-on-tens…

Join us in the Wizards Slack channel:
http://wizards.herokuapp.com/

And please support me on Patreon:
https://www.patreon.com/user?u=3191693

Streaming Live from UploadVR’s Studio in San Francisco!: https://www.youtube.com/uploadvr

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The Mandelbrot Set – The only video you need to see!

This video has been edited together from several other video documentaries to describe the Mandelbrot set! An incredible mathematical formula explaining fractals and geometry! Several mathematicians and scientists explain this phenomenon in clear detail. Please enjoy!

We are not the owners of these video clips nor do we claim to be. This video is for educational & entertainment purposes only.

 

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Free Machine Learning eBooks – March 2017

Here are three eBooks available for free.

MACHINE LEARNING

Edited by Abdelhamid Mellouk and Abdennacer Chebira

Machine Learning can be defined in various ways related to a scientific domain concerned with the design and development of theoretical and implementation tools that allow building systems with some Human Like intelligent behaviour.

Machine Learning addresses more specifically the ability to improve automatically through experience.

UNDERSTANDING MACHINE LEARNING

by Shai Ben-David and Shai Shalev-Shwartz

Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way.

The book provides an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics of the field, the book covers a wide array of central topics that have not been addressed by previous textbooks. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds.

Designed for an advanced undergraduate or beginning graduate course, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics, and engineering.

NEURAL NETWORKS

by D. Kriesel

The purpose of this book is to help you master the core concepts of neural networks, including modern techniques for deep learning.

After working through the book you will have written code that uses neural networks and deep learning to solve complex pattern recognition problems.

And you will have a foundation to use neural networks and deep learning to attack problems of your own devising.

To check those books and receive announcements when new free eBooks are published, click here.

Top DSC Resources

Follow us on Twitter: @DataScienceCtrl | @AnalyticBridge

Original post here

Posted by Emmanuelle Rieuf on March 20, 2017 at 4:00pm

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How to Generate Music with Tensorflow (LIVE)

This live session will focus on the details of music generation using the Tensorflow library. The goal is for you to understand the details of how to encode music, feed it to a well tuned model, and use it to generate really cool sounds. And I’m going to NOT use Google Hangouts, instead I’ll do this with a green screen and a DSLR camera 🙂


Code for this video:
https://github.com/llSourcell/music_d…

Please subscribe! And like. And comment. That’s what keeps me going.

My Udacity course is open for enrollments until this Saturday at midnight:
https://www.udacity.com/course/deep-l…

More Learning Resources:
http://www.asimovinstitute.org/analyz…
http://www.hexahedria.com/2015/08/03/…
https://github.com/hexahedria/biaxial…
http://www.hexahedria.com/2016/08/08/…
https://magenta.tensorflow.org/
https://github.com/farizrahman4u/seq2seq
http://stackoverflow.com/questions/14…
https://github.com/vishnubob/python-midi

Join us in the Wizards Slack channel:
http://wizards.herokuapp.com/

Please support me on Patreon:
https://www.patreon.com/user?u=3191693

Streaming Live from UploadVR’s Studio in San Francisco!: https://www.youtube.com/uploadvr

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Customer Insights From Zoomdata and Cloudera

Understanding and preventing churn (customer loss) requires connecting to all customer touch points – transaction data, call logs, customer complains, social media engagement – to create a complete customer view in real time first. This demo then showcases how this US telco analyzes root causes of churn by discerning key behavior and customer journey, identifies customer profiles at risk accordingly, and executes plan to prevent churn proactively.

 

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Virtualizing Analytics with Apache Spark

In the race to invent multi-million dollar business opportunities with exclusive insights, data scientists and engineers are hampered by a multitude of challenges just to make one use case a reality – the need to ingest data from multiple sources, apply real-time analytics, build machine learning algorithms, and intermix different data processing models, all while navigating around their legacy data infrastructure that is just not up to the task. This need has created the demand for Virtual Analytics, where the complexities of disparate data and technology silos have been abstracted away, coupled with a powerful range of analytics and processing horsepower, all in one unified data platform. This talk describes how Databricks is powering this revolutionary new trend with Apache Spark.


Speaker: Arsalan Tavakoli

This talk was originally presented at Spark Summit East 2017.

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How to Make a Text Summarizer – Intro to Deep Learning #10

I’ll show you how you can turn an article into a one-sentence summary in Python with the Keras machine learning library. We’ll go over word embeddings, encoder-decoder architecture, and the role of attention in learning theory.


Code for this video (Challenge included):
https://github.com/llSourcell/How_to_…

Jie’s Winning Code:
https://github.com/jiexunsee/rudiment…

More Learning resources:
https://www.quora.com/Has-Deep-Learni…
https://research.googleblog.com/2016/…
https://en.wikipedia.org/wiki/Automat…
http://deeplearning.net/tutorial/rnns…
http://machinelearningmastery.com/tex…

Please subscribe! And like. And comment. That’s what keeps me going.

Join us in the Wizards Slack channel:
http://wizards.herokuapp.com/

And please support me on Patreon:
https://www.patreon.com/user?u=3191693

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NoSQL and Hadoop for Solving Big Data #WhiteboardWalkthrough

In this week’s Whiteboard Walkthrough, Dale Kim, Director of Industry Solutions at MapR, gets you up to speed on Apache Hadoop and NoSQL. He talks about the similarities and differences between the two, but most importantly how both technologies should be a requirement for any true big data environment.

http://bit.ly/1RQATva

 

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What to Expect for Big Data and Apache Spark in 2017

Big data remains a rapidly evolving field with new applications and infrastructure appearing every year. In this talk, Matei Zaharia will cover new trends in 2016 / 2017 and how Apache Spark is moving to meet them. In particular, he will talk about work Databricks is doing to make Apache Spark interact better with native code (e.g. deep learning libraries), support heterogeneous hardware, and simplify production data pipelines in both streaming and batch settings through Structured Streaming.

This talk was originally presented at Spark Summit East 2017.

You can view the slides on Slideshare:
http://www.slideshare.net/databricks/…

 

 

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