“…Unleash Python and take your small readers on an adventurous ride through the world of programming About This Book Learn to start using Python for some simple programming tasks such as doing easy mathematical calculations. Use logic and control loops to build a nice interesting game. …”

“…Scala will be a valuable tool to have on hand during your data science journey for everything from data cleaning to cutting-edge machine learning About This Book Build data science and data engineering solutions with ease An in-depth look at each stage of the data analysis process - from reading and collecting data to distributed analytics Explore a broad variety of data processing, machine learning, and genetic algorithms through diagrams, mathematical formulations, and source code Who This Book Is For This learning path is perfect for those who are comfortable with Scala programming and now want to enter the field of data science. …”

“…Master an array of machine learning techniques with real-world projec.ts that interface TensorFlow with R, H2O, MXNet, and other languages Key Features Gain expertise in machine learning, deep learning, and predictive modeling techniques Build intelligent end-to-end projects for finance, social media, and a variety of other domains Implement multi-class classification, regression, and clustering in your models Book Description R is one of the most popular languages when it comes to exploring the mathematical side of machine learning and easily performing computational statistics. …”

“…On the Computational and Statistical Interface and "Big Data" Michael Jordan, Professor, UC Berkeley How does statistical decision theory provide a mathematical point of departure for achieving such a blending? …”

“…The authors focus on strategic, tactical, and operational aspects of supply chain management and cover a broad range of topics from forecasting, inventory management, and facility location to transportation, process flexibility, and auctions. Key mathematical models for optimizing the design, operation, and evaluation of supply chains are presented as well as models currently emerging from the research frontier. …”

“…An example-rich guide for beginners to start their reinforcement and deep reinforcement learning journey with state-of-the-art distinct algorithms Key Features Covers a vast spectrum of basic-to-advanced RL algorithms with mathematical explanations of each algorithm Learn how to implement algorithms with code by following examples with line-by-line explanations Explore the latest RL methodologies such as DDPG, PPO, and the use of expert demonstrations Book Description With significant enhancements in the quality and quantity of algorithms in recent years, this second edition of Hands-On Reinforcement Learning with Python has been revamped into an example-rich guide to learning state-of-the-art reinforcement learning (RL) and deep RL algorithms with TensorFlow 2 and the OpenAI Gym toolkit. …”

“…However, every day we rely on the certainty and precision of mathematical strategies that assume the contrary to control and grow wealth in markets. …”

“…Manipulate text with string, textwrap, re (regular expressions), and difflib Use data structures: enum, collections, array, heapq, queue, struct, copy, and more Implement algorithms elegantly and concisely with functools, itertools, and contextlib Handle dates/times and advanced mathematical tasks Archive and data compression Understand data exchange and persistence, including json, dbm, and sqlite Sign and verify messages cryptographically Manage concurrent operations with processes and threads Test, debug, compile, profile, language, import, and package tools Control interaction at runtime with interpreters or the environment…”

“…What You Will Learn Develop skills to use IPython for high performance computing (HPC) Understand the IPython interactive shell Use XeroMQ and MPI to pass messages Integrate third-party tools like R, Julia, and JavaScript with IPython Visualize the data Acquire knowledge to test and document the data Get to grips with the recent developments in the Jupyter notebook system In Detail IPython is an interactive computational environment in which you can combine code execution, rich text, mathematics, plots, and rich media. This book will get IPython developers up to date with the latest advancements in IPython and dive deep into interactive computing with IPython. …”

“…Once you have covered Jupyter, you will dig deep into Python's NumPy package, a powerful extension with advanced mathematical functions. You will then move on to creating NumPy arrays and employing different array methods and functions. …”

“…At its core, machine learning is a mathematical, algorithm-based technology that forms the basis of historical data mining and modern big data science. …”

“…Ebook (PDF version) produced by Mathematical Sciences Publishers (MSP),http://msp.org…”

“…It sketches the intellectual background of Carlo's conception, emphasizing the king's mythical forebears and his love of mathematical order. It shows that the Neapolitan poet and philosopher, Giambattista Vico, influenced the king to incorporate such mythic figures as Hercules and Aeneas into his genealogy and Vanvitelli to introduce their likenesses into Caserta's art, which is in turn integrated with the geometry of the palace's gardens and the numerical sequences of its rooms…”

“…General Topics and Subject Areas, Organized by Chapter Introduction to Problem Solving with Mathematical Software Packages Basic Principles and Calculations Regression and Correlation of Data Introduction to Problem Solving with Excel Introduction to Problem Solving with MATLAB Advanced Problem-Solving Techniques Thermodynamics Fluid Mechanics Heat Transfer Mass Transfer Chemical Reaction Engineering Phase Equilibrium and Distillation Process Dynamics and Control Biochemical Engineering Practical Aspects of Problem-Solving Capabilities Simultaneous Linear Equations Simultaneous Nonlinear Equations Linear, Multiple Linear, and Nonlinear Regressions with Statistical Analyses Partial Differential Equations (Using the Numerical Method of Lines) Curve Fitting by Polynomials with Statistical Analysis Simultaneous Ordinary Differential Equations (Including Problems Involving Stiff Systems, Differential-Algebraic Equations, and Parameter Estimation in Systems of Ordinary Differential Equations) The Book's Web Site (http://www.problemsolvingbook.com) Provides solved and partially solved problem files for all three software packages, plus additional materials Describes discounted purchase options for educational version of POLYMATH available to book purchasers Includes detailed, selected problem solutions in MapleTM, Mathcad®, and MathematicaTM…”

“…Drawing on more than a decade of never-before-published research, Augen shows option traders how to study historical price changes, mitigate risk, limit market exposure, and structure mathematically sound high-return positions. You’ll even discover how to build your own customized, low-cost analytical toolset to transform these state-of-the-art strategies into practical buy/sell signals!…”

“…Basic knowledge of Python and data analytics will help you get the most from this book. Familiarity with mathematical concepts such as algebra and basic statistics will also be useful…”

“…It makes implementing deep networks very transparent, which allows you to validate all the mathematical concepts you are learning. Familiarity with basic deep learning concepts is preferred but not required as we will cover the math behind the code as well. …”

“…What you will learn Build applications using hash tables, dictionaries, and sets Explore how modern hardware affects the actual run-time performance of programs Apply common algorithms such as heapsort and merge sort for string data types Use C++ template metaprogramming to write code libraries Implement a URL shortening service using a bloom filter Use appropriate modern C++ idioms such as std:: array instead of C-style arrays Who this book is for This book is for developers or students who want to revisit basic data structures and algorithm design techniques. Although no mathematical background is required, basic knowledge of complexity classes and Big O notation along with a qualification in an algorithms course will help you get the most out of this book. …”

“…Estimate in comparison to past experience, by modeling the work mathematically, or a hybrid of both. Learn strategies for effective decomposition of work and aspects of the work that likely affect your estimates. …”