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Spencer Vann – Surplus Funds Mastery

Original price was: $999.00.Current price is: $49.00.

This Course is available for download now. You can contact us for Screenshots or Demo. Access for this course will be sent on google drive. Join our telegram channel to see updates and occasional discounts. If you want to pay through Paypal or Card contact us – On Telegram Click Here or contact on Mail – [email protected]

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Spencer Vann – Surplus Funds Mastery

Spencer Vann – Surplus Funds Mastery | 2.2 GB

You’re Going To See: – How we use this industry-shaking secret strategy, that we currently use to generate income of over $100k on deals… (Hint: It’s not Wholesaling or Fix & Flipping)

BONUS VIDEO SECTION ADDED ON OVERCOMING JOB INTERVIEW ANXIETY
NEW: 50 PAGE PRINTABLE STUDY GUIDE TO HELP WITH ANSWERING INTERVIEW QUESTIONS

Are you tired of courses based on over-used, outdated data sets?

Inside this class we will work on Real-World datasets, to solve Real-

World business problems. (Definitely not the boring iris or digit classification datasets that we see in every course). In this course we will solve six real-world challenges:

  • Artificial Neural Networks to solve a Customer Churn problem
  • Convolutional Neural Networks for Image Recognition
  • Recurrent Neural Networks to predict Stock Prices
  • Self-Organizing Maps to investigate Fraud
  • Boltzmann Machines to create a Recomender System
  • Stacked Autoencoders* to take on the challenge for th

*Stacked Autoencoders is a brand new technique in Deep Learning which didn’t even exist a couple of years ago. We haven’t seen this method explained anywhere else in sufficient depth.

In Deep Learning A-Z™ we code together with you. Every practical tutorial starts with a blank page and we write up the code from scratch. This way you can follow along and understand exactly how the code comes together and what each line means.

In addition, we will purposefully structure the code in such a way so that you can download it and apply it in your own projects. Moreover, we explain step-by-step where and how to modify the code to insert YOUR dataset, to tailor the algorithm to your needs, to get the output that you are after.

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