Ratiborus Kms Tools Crack
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How to Heal the Sick
The solid, biblically based methods of healing found in this book can bring you physical health, divine happiness, and abundant life.
Principles of Multimedia
Principles of Multimedia introduces and explains the theoretical concepts related to the representation, storage, compression, transmission and processing of various multimedia components, including text, image, graphics, audio, video and animation, as well as their use across various applications. The book provides the necessary programming tools and analysis techniques concepts to perform practical processing tasks in software labs and to solve numerical problems at the post-graduate level. For this new third edition, every chapter has been updated and the book has been carefully streamlined throughout. Chapter 1 provides an overview of multimedia technology, including the definition, major characteristics, hardware, software, standards, technologies and relevant theorems with mathematical formulations. Chapter 2 covers text, including digital text representations, text editing and processing tools, text application areas and text file formats. Chapters 3 and 4 examine image and graphics, including digital image input and output systems, image editing and processing tools, image application areas, image color management and image file formats, as well as 2D and 3D graphics algorithms, transformations matrices, splines, fractals, vectors, projection, application areas and graphics file formats. Chapter 5 covers audio, including digital audio input and output systems, audio editing and processing tools, audio application areas and audio file formats. Chapter 6 looks at video, including digital video input and output systems, video editing and processing tools, video application areas and video file formats. Chapter 7 focuses on animation, covering 2D and 3D animation algorithms, interpolations, modeling, texture mapping, lights, illumination models, camera, rendering, application areas and animation file formats. Finally, chapter 8 covers compression, including lossless and lossy compression techniques, and various algorithms related to text image audio and video compression. Every chapter includes solved numerical problems, coding examples and references for further reading. Including theoretical explanations, mathematical formulations, solved numerical problems and coding examples throughout, Principles of Multimedia is an ideal textbook for graduate and post-graduate students studying courses on image processing, speech and language processing, signal processing, video object detection and tracking, graphic design and modeling, and related multimedia technologies
Data Science Bookcamp
Author: Leonard Apeltsin
language: en
Publisher: Simon and Schuster
Release Date: 2021-12-07
Learn data science with Python by building five real-world projects! Experiment with card game predictions, tracking disease outbreaks, and more, as you build a flexible and intuitive understanding of data science. In Data Science Bookcamp you will learn: - Techniques for computing and plotting probabilities - Statistical analysis using Scipy - How to organize datasets with clustering algorithms - How to visualize complex multi-variable datasets - How to train a decision tree machine learning algorithm In Data Science Bookcamp you’ll test and build your knowledge of Python with the kind of open-ended problems that professional data scientists work on every day. Downloadable data sets and thoroughly-explained solutions help you lock in what you’ve learned, building your confidence and making you ready for an exciting new data science career. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology A data science project has a lot of moving parts, and it takes practice and skill to get all the code, algorithms, datasets, formats, and visualizations working together harmoniously. This unique book guides you through five realistic projects, including tracking disease outbreaks from news headlines, analyzing social networks, and finding relevant patterns in ad click data. About the book Data Science Bookcamp doesn’t stop with surface-level theory and toy examples. As you work through each project, you’ll learn how to troubleshoot common problems like missing data, messy data, and algorithms that don’t quite fit the model you’re building. You’ll appreciate the detailed setup instructions and the fully explained solutions that highlight common failure points. In the end, you’ll be confident in your skills because you can see the results. What's inside - Web scraping - Organize datasets with clustering algorithms - Visualize complex multi-variable datasets - Train a decision tree machine learning algorithm About the reader For readers who know the basics of Python. No prior data science or machine learning skills required. About the author Leonard Apeltsin is the Head of Data Science at Anomaly, where his team applies advanced analytics to uncover healthcare fraud, waste, and abuse. Table of Contents CASE STUDY 1 FINDING THE WINNING STRATEGY IN A CARD GAME 1 Computing probabilities using Python 2 Plotting probabilities using Matplotlib 3 Running random simulations in NumPy 4 Case study 1 solution CASE STUDY 2 ASSESSING ONLINE AD CLICKS FOR SIGNIFICANCE 5 Basic probability and statistical analysis using SciPy 6 Making predictions using the central limit theorem and SciPy 7 Statistical hypothesis testing 8 Analyzing tables using Pandas 9 Case study 2 solution CASE STUDY 3 TRACKING DISEASE OUTBREAKS USING NEWS HEADLINES 10 Clustering data into groups 11 Geographic location visualization and analysis 12 Case study 3 solution CASE STUDY 4 USING ONLINE JOB POSTINGS TO IMPROVE YOUR DATA SCIENCE RESUME 13 Measuring text similarities 14 Dimension reduction of matrix data 15 NLP analysis of large text datasets 16 Extracting text from web pages 17 Case study 4 solution CASE STUDY 5 PREDICTING FUTURE FRIENDSHIPS FROM SOCIAL NETWORK DATA 18 An introduction to graph theory and network analysis 19 Dynamic graph theory techniques for node ranking and social network analysis 20 Network-driven supervised machine learning 21 Training linear classifiers with logistic regression 22 Training nonlinear classifiers with decision tree techniques 23 Case study 5 solution