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Six-Step Relational Database Design

Six-Step Relational Database Design

by by Fidel A Captain
158 Pages · 2015 · 5 MB · 3 Downloads · New!
" Happiness doesn't result from what we get, but from what we give. ” ― Ben Carson
Hands-On Convolutional Neural Networks with TensorFlow
by Iffat Zafar, Giounona Tzanidou, Richard Burton, Nimesh Patel, Leonardo Araujo
272 Pages · 2015 · 18 MB · 4,496 Downloads · New!
The “Hands-On Convolutional Neural Networks with TensorFlow: Solve computer vision problems with modeling in TensorFlow and Python” is a great book for those who want to use Convolutional Neural Networks for solving real-world problems. Iffat Zafar, Giounona Tzanidou, Richard Burton, Nimesh Patel, and Leonardo Araujo are the authors of this great book. Convolutional Neural Networks are the most popular architectures used in computer vision apps. This book will guide the readers on how to train machine learning models with TensorFlow.
Mastering Machine Learning Algorithms
by Giuseppe Bonaccorso
798 Pages · 2020 · 15 MB · 1,493 Downloads · New!
Mastering Machine Learning Algorithms is the design, machine theory, programming algorithm, and data modeling book that teaches students to master machine learning. Giuseppe Bonaccorso is the author of this remarkable book. It is an updated, revised, and new edition of the book that is based on research. He is the bestselling author in the New York Times. This book brings an opportunity to master and explore the most essential algorithms for solving complex machine learning challenges. Learn about the latest algorithms and how you can use them for the betterment of humanity. It briefly describes cutting edge applications, time series analysis, regression analysis, and deep learning models.
Statistics, Data Analysis, and Decision Modeling
by James R. Evans
552 Pages · 2012 · 13.6 MB · 2 Downloads · New!
“Statistics, Data Analysis, and Decision Modeling 5th Edition” is a great book on data modeling. This book applies real-world scenarios to the concepts presented, which brings the concepts much “closer to home.” James R. Evans is the author of this book. It focuses on the practical understanding of its topics, allowing readers to develop conceptual insight on fundamental techniques and theories. The book is pretty easy to follow and doesn’t get too boring. The concepts of the book help people who are not major in statistic a lot. It uses simple words and some example to help people to understand the concepts in the book. It states a lot of formulas in a way that it expects you to know how to solve them. However, it is very useful in that it comes with an online subscription and excel content that can be downloaded online. Overall it is challenging book, but you will learn from it.
Data Structures & Algorithms in Kotlin
by , Irina Galata, Matie Suica, Tutorial Team, raywenderlich
424 Pages · 2019 · 25  MB · 0 Downloads · New!
Data Structures & Algorithms in Kotlin is the computer algorithms, programming algorithms and data modeling book which shares the incredible techniques to become expert in data structures. Irina Galata, Matei Suicaare the author of this impressive book. Data structures and algorithms are the key roles to become an expert in development. If you build the foundation strong then you will be leading the industry in the upcoming few years. This book will show you how to implement data structures in Kotlin. Learn the different techniques to solve a robust set of algorithms and implement them flawlessly. This book consists of several chapters and all of them are worth to read. It provides a brief explanation on Kotlin and how to code on it. How to write and test the complexity of the code. Learn about the elementary data structures such as stacks, queues and linked lists. It covers the trees like binary search, binary trees, and AVL trees. What is sorting and different kind of sorting we used to search the data. How to implement bubble sort and including other sorting methods. Learn about the Dijkstra algorithm and how it works to find the shortest path among the points. A comprehensive book for beginners and experts.
Python Feature Engineering Cookbook
by Soledad Galli
372 Pages · 2015 · 6 MB · 2 Downloads · New!
The “Python Feature Engineering Cookbook: Over 70 recipes for creating, engineering, and transforming features to build machine learning models” is a great book for machine learning professionals, AI engineers and data scientists. Soledad Galli is the author of this data mining book. Soledad Galli is a lead data scientist with more than 10 years of experience in world-class academic institutions and renowned business. She has researched, developed and out into production machine learning models for insurance claims, and fraud prevention. In this book, the author uncovers the end-to-end feature engineering process across continuous, discrete and unstructured datasets. So, you will work with the best tools to streamline and improve the quality of your code. Additionally, Soledad Galli covers the Python recipes that will help you automate feature engineering to simplify complex processes. With the help of this book, you will also get to grips with different feature engineering strategies, like power transform, log transform across machine learning, etc. To sum it up, Python Feature Engineering Cookbook is a must-read book for all the Python developers.
Hands-On Simulation Modeling with Python
by Giuseppe Ciaburro
346 Pages · 2020 · 7 MB · 6 Downloads · New!
Hands-On Simulation Modeling with Python is the computer simulation, python development, and data modeling book that describes essential tips to create prototypes. Giuseppe Ciaburro is the author of this remarkable book. It is a comprehensive guide for model designers, simulation developers, and engineers. Get ready to learn the latest computational methods that let user study the behavior of systems. It is time to explore the latest simulation techniques such as statistical simulation and Monte Carlo methods. Giuseppe provides a whole overview of the different types of simulation models. What is the best way to work with continuous distributions and discrete? Get a complete overview of the data generation process and randomness. What is the best way to simulate random walks? How to generate maximum productivity in real-life applications through optimization methods. What are resampling methods and how you can use them to optimize your work? Get ready to create digital prototypes that will assist you to analyze the performance of physical models. What is the importance of python to expert all these functionalities? Discover the key algorithms that guides students step by step in the whole process.