Форум по трейдингу, автоматическим торговым системам и тестированию торговых стратегий
What to read and where to learn about Machine Learning
MetaQuotes Software Corp., 2017.08.25 11:37
There are 10 free books related to machine Learning (based on thepixelbeard.com portal.
1. The Elements Of Statistical Learning : Data Mining, Inference and Prediction
This is an ebook by T.Hastie, R. Tibshirani, J.Friedman that lets readers get knowledge about conceptual underpinnings. There are varied topics included like statistical framework and much more.
2. Inductive Logical Programming
Inductive logic programming is a topic in Machine Learning. It is used in research field at the intersection of machine learning and logic programming.
3. Reinforcement Learning : An introduction by Richard S. Sutton, Andrew G. Barto
This is an ebook written by Richard S. Sutton, Andrew G. Barto. With this ebook users can get a grasp of machine learning the easy way.
4. Information Theory, Inference and Learning Algorithms
This is an ebook by David J.C. Mackay in which information theory topic is well explained in such a way that it helps readers attain good knowledge about the practical communication systems viz. arithmetic coding for data compression, sparse-graph codes for error-correction and more.
5. Gaussian Processes for Machine Learning
This is an ebook by Carl E. Rasmussen, Chritopher K.I Williams. With this ebook one can learn a principled, practical, probabilistic approach to learning in kernel machines in quite a simple way.
6. The LION Way
This ebook is written by Roberto Battiti, Mauro Brunato and has been written in such a way that it helps users in machine learning and Intelligent Optimization (LION).
7. Bayesian Reasoning and Machine Learning
This is an ebook that is written by David Barber that targets the final-year undergraduates and master’s students. The topics include linear algebra and calculus. This book helps users learn basic reasoning, advanced techniques within the framework of graphical models and develop skills.
8. A Course In Machine Learning
This ebook by Hal Daume III is a course book that includes a set of introductory material covering different aspects of modern machine learning.
9. Machine Learning, Neural And Statistical Classification
This is an ebook by D. Michie, D.J Spiegelhalter that aims at providing an up-to-date review of different approaches to classification.
10. Introduction To Machine Learning
This ebook is written by Nilis J Nilsson. It includes different topics in machine learning circa 1996 that helps people pursue a middle ground between theory and practice.
Neural Network: discussion/development threads
Neural Network: Indicators and systems development
Neural Network: EAs
Neural Network: The Books
Deep Neural Networks (Part II). Working out and selecting predictors - MT5
New article -
Deep Neural Networks (Part III). Sample selection and dimensionality reduction
Next article -
Deep Neural Networks (Part IV). Creating, training and testing a model of neural network
Taking NEURAL NETWORKS to the NEXT LEVEL - very interesting thread:
This thread won't be about a question or problem, but rather about the anouncement of the presentation and documentation of an exciting
trading concept. I plan to do a series of postings here in order to keep you guys updated.
Anybody who has an opinion on the topic, please don't hesitate to comment, even if you don't have profound machine learning knowledge (I'm
still learning, too - which never ends).
Taking NEURAL NETWORKS to the NEXT LEVEL - very
thanks, Sergey; I'm the starter of that thread; I wasn't aware of those two articles (above) that you linked in Oct2017. The topics are extremily
close to each other, so I realize that I was being a little redundant. I will continue the series nevertheless, because I think the aspect
LSTM+autoencoder might still be of interest.
Forum on trading, automated trading systems
and testing trading strategies
How to Start
with Metatrader 5
Neural Networks Made Easy - MT5
Easy Neural Network - library for MetaTrader 5
The provided test script has included comments which are easy to follow along.
You can save the returned network configuration in a file then load it in different sessions.
This library has made little use of OOP, and should therefore be easy to follow along even by newer programmers.
Practical application of neural networks in trading - the article for MT5
Neural networks made easy (Part 3): Convolutional networks - MT5
Previous two articles related to this subject:
Please enable the necessary setting in your browser, otherwise you will not be able to log in.