Home | Amazing | Today | Tags | Publishers | Years | Search 
Hands-On Machine Learning for Algorithmic Trading: Design and implement investment strategies based on smart algorithms that learn from data using Python
Hands-On Machine Learning for Algorithmic Trading: Design and implement investment strategies based on smart algorithms that learn from data using Python

Explore effective trading strategies in real-world markets using NumPy, spaCy, pandas, scikit-learn, and Keras

Key Features

  • Implement machine learning algorithms to build, train, and validate algorithmic models
  • Create your own algorithmic design process to apply...
Bayesian Reasoning and Machine Learning
Bayesian Reasoning and Machine Learning
We live in a world that is rich in data, ever increasing in scale. This data comes from many di erent sources in science (bioinformatics, astronomy, physics, environmental monitoring) and commerce (customer databases, nancial transactions, engine monitoring, speech recognition, surveillance, search). Possessing the knowledge as to...
Optimisation in Signal and Image Processing
Optimisation in Signal and Image Processing

This book describes the optimization methods most commonly encountered in signal and image processing: artificial evolution and Parisian approach; wavelets and fractals; information criteria; training and quadratic programming; Bayesian formalism; probabilistic modeling; Markovian approach; hidden Markov models; and metaheuristics (genetic...

Diagrammatic Reasoning in AI
Diagrammatic Reasoning in AI

This book is really the end product of over a decade of work, on and off, on diagrammatic reasoning in artificial intelligence (AI). In developing this book, I drew inspiration from a variety of sources: two experimental studies, the development of two prototype systems, an extensive literature review and analysis in AI,...

Think Stats
Think Stats

If you know how to program, you have the skills to turn data into knowledge using the tools of probability and statistics. This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python.

You'll work with a case study throughout the...

Statistical Bioinformatics: with R
Statistical Bioinformatics: with R

Bioinformatics is an emerging field in which statistical and computational techniques are used extensively to analyze and interpret biological data obtained from high-throughput genomic technologies. Genomic technologies allow us to monitor thousands of biological processes going on inside living organisms in one snapshot, and are...

Symbolic and Quantitative Approaches to Reasoning with Uncertainty: 11th European Conference
Symbolic and Quantitative Approaches to Reasoning with Uncertainty: 11th European Conference

This book constitutes the refereed proceedings of the 11th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, ECSQARU 2011, held in Belfast, UK, in June/July 2011. The 60 revised full papers presented together with 3 invited talks were carefully reviewed and selected from 108 submissions. The papers are...

The Bayesian Choice: From Decision-Theoretic Foundations to Computational Implementation (Springer Texts in Statistics)
The Bayesian Choice: From Decision-Theoretic Foundations to Computational Implementation (Springer Texts in Statistics)

The main purpose of statistical theory is to derive from observations of a random phenomenon an inference about the probability distribution underlying this phenomenon. That is, it provides either an analysis (description) of a past phenomenon, or some predictions about a future phenomenon of a similar nature. In this book, we insist...

Theory of Optimal Search
Theory of Optimal Search
This book deals with the problem of optimal allocation of effort to detect a  target. A Bayesian approach is taken in which it is assumed that there is a  prior distribution for the target's location which is known to the searcher as  well as a function which relates the conditional probability of detecting...
Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series)
Probabilistic Graphical Models: Principles and Techniques (Adaptive Computation and Machine Learning series)

Most tasks require a person or an automated system to reason--to reach conclusions based on available information. The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. The approach is model-based, allowing interpretable models to be constructed and then manipulated by reasoning...

Machine Learning: Discriminative and Generative (The Springer International Series in Engineering and Computer Science)
Machine Learning: Discriminative and Generative (The Springer International Series in Engineering and Computer Science)

Machine Learning: Discriminative and Generative covers the main contemporary themes and tools in machine learning ranging from Bayesian probabilistic models to discriminative support-vector machines. However, unlike previous books that only discuss these rather different approaches in isolation, it bridges the two...

Brain-Mind Machinery: Brain-inspired Computing and Mind Opening
Brain-Mind Machinery: Brain-inspired Computing and Mind Opening

Brain and mind continue to be a topic of enormous scientific interest. With the recent advances in measuring instruments such as two-photon laser scanning microscopy and fMRI, the neuronal connectivity and circuitry of how the brain's various regions are hierarchically interconnected and organized are better understood now than ever...

Result Page: 13 12 11 10 9 8 7 6 5 4 
©2024 LearnIT (support@pdfchm.net) - Privacy Policy