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Mike Caro - Poker Tips
Mike Caro - Poker Tips
English | Size: 116.09 MB
Category: Sports / Gambling

This is an old Mike Caro tape, I assume it's an old cassette. I cannot find info on it online. It does not seem to be on sale anymore.
He talks about things like raising in the small blind, the myth of domination in HE, tournament myths, betting in NLHE, and other general Texas Hold 'Em concepts.
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Licklibrary Jam with Rammstein
Licklibrary Jam with Rammstein (2017)
P2P | 05 February 2018 | 1.28 GB
Genre: Tutorials

ormed in 1994, Rammstein is one of the biggest bands in the history of industrial metal, with over 35 million albums sold across the globe This excellent course will show you how to nail seven tracks by the German metal icons whose riff heavy songs are continuing to pack out venues and festivals worldwide!
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Street Theory for Guitarists with Jeff Scheetz
Street Theory for Guitarists with Jeff Scheetz
.MP4, AVC, 970 kbps, 960x540 | English, AAC, 128 kbps, 2 Ch | 4.7 hours | + Tabs, PDF | 2.34 GB
Instructor: Jeff Scheetz

The Ultimate Guide for Practical, Useful Theory for Guitar Players
Learning and applying music theory can be confusing and frustrating for guitar players. Its such a vast subject with hundreds of general principles and concepts. Which ones are essential for you to know? How are they applied in a real world musical context? Which ones will have the most immediate impact on your playing and accelerate your growth as a musician?

Introduction to Deep Learning Using PyTorch by Alfredo Canziani, Goku Mohandas
Introduction to Deep Learning Using PyTorch by Alfredo Canziani, Goku Mohandas
MP4 | Video: AVC 1920x1080 | Audio: AAC 48KHz 2ch | Duration: 1.5 Hours | 1.81 GB
Genre: eLearning | Language: English

Video Description
What is this video about, and why is it important?
This video will serve as an introduction to PyTorch, a dynamic, deep learning framework in Python. In this video, you will learn to create simple neural networks, which are the backbone of artificial intelligence. We will start with fundamental concepts of deep learning (including feed forward networks, back-propagation, loss functions, etc.) and then dive into using PyTorch tensors to easily create our networks. Finally, we will CUDA render our code in order to be GPU-compatible for even faster model training.

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