“…What you will learn Become familiar with the basic features of the TensorFlow library Get to know Linear Regression techniques with TensorFlow Learn SVMs with hands-on recipes Implement neural networks to improve predictive modeling Apply NLP and sentiment analysis to your data Master CNN and RNN through practical recipes Implement the gradient boosted random forest to predict housing prices Take TensorFlow into production Who this book is for If you are a data scientist or a machine learning engineer with some knowledge of linear algebra, statistics, and machine learning, this book is for you. …”

“…TABLE OF CONTENTS THE MONOCHROMATIC VISION Past, Present, and Future Thinking in Black and White Contrast Color Implied The Tonal Landscape Visual Implication Seeing in Black and White Pre-Visualization Techniques Black and White Composition Framing Patterns and Lines Shape and Form High Key Low Key Shades of Gray Finding Monochromatic Subjects Black and White at Night Photographing People Portraiture in Black and White BLACK AND WHITE IN THE DIGITAL ERA Digital Black and White Roadmap The RAW Advantage Black and White in Adobe Camera RAW Black and White in Lightroom Grayscale Conversion Using Presets HSL Conversion Exposure Gradients and Adjustments Exporting Virtual Copies into Photoshop as Layers Multiple Layers and Masking in Photoshop Multi-RAW Processing in Photoshop Black and White in Photoshop Blending with Black Using the Channel Mixer Black & White Adjustment Layers Silver Efex Pro Topaz B&W Effects OnOne Perfect B&W The Digital Analog to the Analog Zone System Combining Conversions in Photoshop CREATIVE BLACK AND WHITE OPPORTUNITIES Lighting and Monochromatic Photos Creating High-Key Effects Creating Low-Key Effects HDR in Black and White Shooting for HDR Using HDR Software Toning and Tinting Tinting with a Black & White Adjustment Layer Split Toning The Ansel Adams Effect Selective Color Hand Coloring Using LAB Color Inverting the L Channel Equalizing LAB Channels Swapping Tonalities Using Blending Modes LAB Inversions Soft Focus Adding Soft Focus Pinhole Effect Solarization Using Curves to Solarize an Image Simulating the Blossfeldt Effect Duotone and Tritone Effects Adding and Reducing Noise Film Effects Infrared Camera Conversions Infrared Conversion without an IR Camera Creating X-Ray Images MOBILE BLACK AND WHITE Black and White using a Cell Phone Camera Worki..…”

“…What you will learn Understand how to use machine learning algorithms for regression and classification problems Implement ensemble techniques such as averaging, weighted averaging, and max-voting Get to grips with advanced ensemble methods, such as bootstrapping, bagging, and stacking Use Random Forest for tasks such as classification and regression Implement an ensemble of homogeneous and heterogeneous machine learning algorithms Learn and implement various boosting techniques, such as AdaBoost, Gradient Boosting Machine, and XGBoost Who this book is for This book is designed for data scientists, machine learning developers, and deep learning enthusiasts who want to delve into machine learning algorithms to build powerful ensemble models. …”

“…Complete Coverage of New and Improved Inkscape 0.48 Features Live Path Effects for tasks such as adding patterns along a path while maintaining complete editability More than two hundred pre-built SVG filters for adding textures, manipulating color, and so forth A tiling tool to create complex tilings with thirteen different symmetries Clipping and Masking objects An integrated spell checker Built-in bitmap-to-vector tracing The ability to export to more than a dozen graphics formats, including PDF and PostScript The ability to import from more than two dozen graphics formats, including PDF, Adobe Illustrator, Corel Draw, AutoCad, and XFig Graphics Complete vector drawing program supporting the W3C SVG standard Three path-drawing tools (Pencil, Pen, Calligraphy) Five shape-drawing tools (Rectangles, Ellipses, Stars, Spirals, 3D Boxes) Linear and Radial Gradients Pattern fills Tweak Tool for fine adjus..…”

“…Enhance the look using tone controls and curves, dodging and burning, and tools built for specific types of images, such as Landscape Enhancer, Adjustable Gradient, and B & W Conversion. The Erase and Clone & Stamp tools make it easy to remove unexpected objects and glitches such as lens dust spots. …”

“…Each chapter will assist you in implementing different reinforcement learning techniques, such as Markov decision processes (MDPs), Q-learning, actor-critic methods, SARSA, and deterministic policy gradient algorithms, to build logical self-learning agents. …”

“…What you will learn Setup your Azure Machine Learning workspace for data experimentation and visualization Perform ETL, data preparation, and feature extraction using Azure best practices Implement advanced feature extraction using NLP and word embeddings Train gradient boosted tree-ensembles, recommendation engines and deep neural networks on Azure Machine Learning Use hyperparameter tuning and Azure Automated Machine Learning to optimize your ML models Employ distributed ML on GPU clusters using Horovod in Azure Machine Learning Deploy, operate and manage your ML models at scale Automated your end-to-end M..…”

“…Some session of note include: Bayesian Statistics Made Simple Gradient Descent, Demystified Comparing Models Using Resampling and Bayesian Methods Next Generation Indexes For Big Data Engineering Probabilistic Programming with PyMC3 Racial Bias in Facial Recognition Software Visualization throughout the Data Science Workflow: Datafy All The Things Data Science, Management..…”

“…This authoritative Canvas reference covers The canvas element—using it with other HTML elements, handling events, printing a canvas, and using offscreen canvases Shapes—drawing, dragging, erasing, and editing lines, arcs, circles, curves, and polygons; using shadows, gradients, and patterns Text—drawing, positioning, setting font properties; building text controls Images—drawing, scaling, clipping, processing, and animating Animations—creating smooth, efficient, and portable animations Sprites—implementing animated objects that have painters and behaviors Physics—modeling physical systems (falling bodies, pendulums, and projectiles), and implementing tweening for nonlinear motion and animation Collision detection—advanced techniques, clearly explained Game development—all aspects of game development, such as time-based motion and high score support, implemented in a game engine Custom controls—infrastructure for implementing custom controls; implementing progress bars, sliders, and an image panner Mobile applications—fitting Canvas apps on a mobile screen, using media queries, handling touch events, and specifying iOS5 artifacts, such as app icons Throughout the book, Geary discusses high-quality, reusable code to help p..…”

“…After an introduction to regularization and methods to diagnose when to use it, you'll start implementing regularization techniques on linear models, such as linear and logistic regression, and tree-based models, such as random forest and gradient boosting. You'll then be introduced to specific regularization methods based on data, high cardinality features, and imbalanced datasets. …”

“…Alles, was Sie über Machine Learning wissen müssen, auf nur 200 Seiten Von Support Vector Machines über Gradient Boosting und tiefe neuronale Netze bis hin zu unüberwachten Lernmethoden Zahlreiche Tipps und Empfehlungen für den praktischen Einsatz Sie möchten Machine Learning verstehen und dafür nicht unendlich viel Zeit aufwenden und Hunderte von Seiten lesen? …”

“…Next, you'll implement key ML algorithms such as clustering, gradient boosting, random forest, and XGBoost. The book will guide you through constructing classifiers and features for malware, which you'll train and test on real samples. …”

“…Lesson 5: Formatting Text Have you ever wanted to outline text or fill letters with a gradient or a picture? PowerPoint makes this very easy to do. …”

“…After that, you will learn about deep Q-learning, policy gradient algorithms, actor-critic methods, model-based methods, and multi-agent reinforcement learning. …”

“…You'll learn how bright minds predicted which drivers would likely avoid filing insurance claims in Brazil and see how expert Kagglers used gradient-boosting methods to model Walmart unit sales time-series data. …”

“…You'll discover how to implement advanced deep reinforcement learning algorithms such as actor-critic, deep deterministic policy gradients, deep-Q networks, proximal policy optimization, and deep recurrent Q-networks for training your RL agents. …”

“…What You Will Learn Learn the basics of machine learning and neural networks Understand the architecture of neural networks Learn the basics of training a DNN using the Gradient Descent algorithm Learn how to implement a complete DNN using NumPy Learn to create a complete structure for DNN from scratch using Python Work on a project using deep learning for the IRIS dataset Audience This course is designed for anyone who is interested in data science or interested in taking their data-speak to a higher level. …”

“…By the end, you will be proficient in translating ML model predictions into a trading strategy that operates at daily or intraday horizons, and in evaluating its performance.What you will learnLeverage market, fundamental, and alternative text and image dataResearch and evaluate alpha factors using statistics, Alphalens, and SHAP valuesImplement machine learning techniques to solve investment and trading problemsBacktest and evaluate trading strategies based on machine learning using Zipline and BacktraderOptimize portfolio risk and performance analysis using pandas, NumPy, and pyfolioCreate a pairs trading strategy based on cointegration for US equities and ETFsTrain a gradient boosting model to predict intraday returns using AlgoSeek s high-quality trades and "es dataWho this book is forIf you are a data analyst, data scientist, Python developer, investment analyst, or portfolio manager interested in getting hands-on machine learning knowledge for trading, this book is for you. …”

“…You will: Understand web scraping, its applications/uses, and how to avoid web scraping by hitting publicly available rest API endpoints to directly get data Develop a web scraper and crawler from scratch using lxml and BeautifulSoup library, and learn about scraping from JavaScript-enabled pages using Selenium Use AWS-based cloud computing with EC2, S3, Athena, SQS, and SNS to analyze, extract, and store useful insights from crawled pages Use SQL language on PostgreSQL running on Amazon Relational Database Service (RDS) and SQLite using SQLalchemy Review sci-kit learn, Gensim, and spaCy to perform NLP tasks on scraped web pages such as name entity recognition, topic clustering (Kmeans, Agglomerative Clustering), topic modeling (LDA, NMF, LSI), topic classification (naive Bayes, Gradient Boosting Classifier) and text similarity (cosine distance-based nearest neighbors) Handle web archival file formats and explore Common Crawl open data on AWS Illustrate practical applications for web crawl data by building a similar website tool and a technology profiler similar to builtwith.com Write scripts to create a backlinks database on a web scale similar to Ahrefs.com, Moz.com, Majestic.com, etc., for search engine optimization (SEO), competitor research, and determining website domain authority and ranking Use web crawl data to build a news sentiment analysis system or alternative financial analysis covering stock market trading signals Write a production-ready crawler in Python using Scrapy framework and deal with practical workarounds for Captchas, IP rotation, and more…”

“…Skill Level Intermediate Advanced Learn How To Recognize which type of transformer-based model is best for a given task Understand how transformers process text and make predictions Fine-tune a transformer-based model Create pipelines using fine-tuned models Deploy fine-tuned models and use them in production Who Should Take This Course Intermediate/advanced machine learning engineers with experience with ML, neural networks, and NLP Those interested in state-of-the art NLP architecture Those interested in productionizing NLP models Those comfortable using libraries like Tensorflow or PyTorch Those comfortable with linear algebra and vector/matrix operations Course Requirements Python 3 proficiency with some experience working in interactive Python environments including Notebooks (Jupyter/Google Colab/Kaggle Kernels) Comfortable using the Pandas library and either Tensorflow or PyTorch Understanding of ML/deep learning fundamentals including train/test splits, loss/cost functions, and gradient descent About Pearson Video Training: Pearson publishes expert-led video tutorials covering a wide selection of technology topics designed to teach you the skills you need to succeed. …”