RLSLOG is for sale
Warning! Do not download before hiding your IP with a VPN!
Your IP Address is .   Location is
Your Internet Provider can see what you are downloading!  Hide your IP ADDRESS with a VPN!
We strongly recommend using a reliable VPN client to hide yourself on the Internet. It's FREE!
Hide me now!
Releaselog

Ensemble Machine Learning by Ankit Dixit-P2P

Ensembling is a technique of combining two or more similar or dissimilar machine learning algorithms to create a model that delivers superior prediction power. This book will show you how you can use many weak algorithms to make a strong predictive model. This book contains Python code for different machine learning algorithms so that you can easily understand and implement it in your own systems. This book covers different machine learning algorithms that are widely used in the practical world to make predictions and classifications. It addresses different aspects of a prediction framework, such as data pre-processing, model training, validation of the model, and more. You will gain knowledge of different machine learning aspects such as bagging (decision trees and random forests), Boosting (Ada-boost) and stacking (a combination of bagging and boosting algorithms). Then you’ll learn how to implement them by building ensemble models using TensorFlow and Python libraries such as scikit-learn and NumPy. As machine learning touches almost every field of the digital world, you’ll see how these algorithms can be used in different applications such as computer vision, speech recognition, making recommendations, grouping and document classification, fitting regression on data, and more. By the end of this book, you’ll understand how to combine machine learning algorithms to work behind the scenes and reduce challenges and common problems.

Ensemble Machine Learning by Ankit Dixit-P2P
English |  ePUB reader |   Tech & Devices  | 8 MB
Download :  Uploadocean –   Upload4earn–  NTi

Comments

Feel free to post your Ensemble Machine Learning by Ankit Dixit-P2P torrent, subtitles, samples, free download, quality, NFO, rapidshare, depositfiles, uploaded.net, rapidgator, filefactory, netload, crack, serial, keygen, requirements or whatever-related comments here. Don't be rude (permban), use only English, don't go offtopic and read FAQ before asking a question. Owners of this website aren't responsible for content of comments.
  1. Mirror2Man
    July 29th, 2018 | 14:45
  2. active
    July 29th, 2018 | 14:59
  3. bino
    July 29th, 2018 | 16:25
  4. cat
    July 29th, 2018 | 17:57

Leave a reply