“…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. …”

“…By the end of the book, you will have implemented all primitive and some advanced collision tests, and you will be able to read on geometry and linear Algebra formulas to take forward to your own games! …”

“…What You Will Learn Use new features to write structured and readable object-oriented code Find out how to use lambdas and higher order functions to write clean, reusable, and simple code Write unit tests and integrate Kotlin tests with Java code in a transitioning code base Write real-world production code in Kotlin in the style of microservices Leverage Kotlin's extensions to the Java collections library Use destructuring expressions and find out how to write your own Write code that avoids null pointer errors and see how Java-nullable code can integrate with features in a Kotlin codebase Discover how to write functions in Kotlin, see the new features available, and extend existing libraries Learn to write an algebraic data types and figure out when they should be used In Detail Kotlin has been making waves ever since it was open sourced by JetBrains in 2011; it has been praised by developers across the world and is already being adopted by companies. …”

“…Strong working knowledge of Python, linear algebra, and machine learning is a must. Downloading the example code for this ebook: You can download the example code files for this ebook on GitHub at the following link: https://github.com/TrainingByPackt/Deep-Learning-for-Natural-Language-Processing . …”

“…No previous mathematical understanding is required beyond basic algebra, probability, and statistics: wherever more advanced math is required, the authors explain it fully. …”

Tabla de Contenidos: “…Emerging mobile technologies in the 21st Century: Bibliometric analysis on augmented reality in education -- Antonio Manuel Rodríguez García, José María Romero Rodríguez, Arturo Fuentes Cabrera, Jesús López Belmonte -- 20-30Artículo -- Tic-tac: Pedagogía activa a través de la retro innovación y la creación de recursos audivisuales en educación superior -- Jesús López Belmonte, Arturo Fuentes Cabrera, José María Romero Rodríguez, Antonio Manuel Rodríguez García -- 31-42Artículo -- La importancia del liderazgo del equipo educativo como parte de la organización escolar -- Juan José Victoria Maldonado, Inmaculada Ávalos Ruiz, José Fernández Cerero, Marta Montenegro Rueda -- 43-50Artículo -- Atención a la diversidad de alumnado en la universidad: Construyendo puentes -- José Fernández Cerero, Marta Montenegro Rueda, Inmaculada Ávalos Ruiz, Juan José Victoria Maldonado -- 51-59Artículo -- Liderazgo pedagógico transformacional: Estrategias para alcanzar una convivencia y un clima idóneo -- Blanca Berral Ortiz, Natalia Moreno Palma, Natalia Campos Soto, Juan-Carlos de la Cruz-Campos -- 60-68Artículo -- Aprendizaje cooperativo: Herramientas para potenciarlo en educación superior -- Blanca Berral Ortiz, Santiago Pozo Sánchez, Carmen Rodríguez Jiménez, Magdalena Ramos Navas-Parejo -- 69-76Artículo -- El pensamiento computacional en el currículo de la educación primaria -- Natalia Moreno Palma, Blanca Berral Ortiz, Magdalena Ramos Navas-Parejo, Juan-Carlos de la Cruz-Campos -- 77-89Artículo -- Digitizing the teaching and learning process: Resources for creating digital educational content -- Santiago Pozo Sánchez, Noemí Carmona Serrano, Jesús López Belmonte, Juan Antonio López Núñez -- 90-97Artículo -- De aprender bajo presión a aprender con P.A.S.I.O.N: Una innovación hacia un nuevo paradigma educativo -- Silvia Breiburd -- 98-108Artículo -- Conciencia fonológica como requisito previo para iniciarse en lecto-escritura -- Bernardino Rodríguez Contreras -- 109-122Artículo -- Desarrollar el compromiso cívico desde la escuela: Un desafío para fortalecer la democracia en Chile -- Roberto Mardones Arévalo -- 123-137Artículo -- Validación de un cuestionario para la evaluación de las competencias transversales de los educadores sociales por parte de las entidades de prácticas -- José María Martínez Marín -- 138-147Artículo -- Educación en tiempos de Coronavirus: Experiencia didáctica en un ciclo formativo de grado medio -- Raúl Rodolfo Guerrero Morilla, Eva Orantes González, José María Heredia Jiménez -- 148-162Artículo -- Mejorar la formación docente sobre el riesgo de inundación para lograr una sociedad más resiliente a los efectos del cambio climático -- Álvaro Francisco Morote Seguido, María Hernández Hernández -- 163-177Artículo -- Un estudio sobre el uso del "they" genérico en el aula de lengua inglesa -- Borjan Grozdanoski -- 177-186Artículo -- La historia romaní en el marco de la enseñanza superior: Diseño de actividades para asignaturas del área de humanidades -- Begoña Barrera López -- 187-202Artículo -- Talleres "clown" para desarrollar la autonomía en niñas y niños andinos del Perú en confinamiento -- Haydee Clady Ticona Arapa, Yovana Madeleyne Pari Coila, Dina Tito Arisaca -- 203-218Artículo -- Uso de álgebra tiles con alumnado con trastorno del espectro autista -- Melody García Moya -- 219-232Artículo -- La enseñanza virtual y el aula invertida (Flipped Classroom) en la formación del profesorado de didáctica de las ciencias sociales -- María Pilar Molina Torres -- 233-242Artículo -- Museos, patrimonio cultural e identidad social: Un estudio sobre la percepción del mensaje de la obra de arte en alumnos universitarios -- Victoria Eugenia Lamas Alvarez, Sandra García Martín -- 243-256Artículo -- Aprendizaje-servicio en tiempos de covid-19: Experiencia con alumnado universitario -- Estefanía López Requena -- 257-267Artículo -- Valoración pedagógica del autismo en época de la Covid-19 -- Esther Polo Márquez, Estela Isequilla Alarcón -- 268-280Artículo -- Acreditación universitaria: Una gran oportunidad para la inclusión -- Jairo Omar Delgado Mora, Luz Mireya Pamplona Camargo, Angie Katherine Fonseca Corredor -- 281-291Artículo -- La atención a la diversidad para una educación equitativa: Percepciones del profesorado y especialistas de educación primaria -- Ana Carmen Lozano Requena -- 292-304Artículo -- Aplicación de las funciones lineales y cuadráticas para la formación inicial del profesor de matemática -- Lisbet Dianeyis Oliva Rojas, Luis Alberto Rodríguez Núñez, Marisol García Báez -- 305-313Artículo -- Propuesta didáctica de centro para la enseñanza transversal de las matemáticas a través del ajedrez -- David Molina García, José L. …”

“…You'll need basic knowledge of the Python programming language, AWS, linear algebra, probability, and networking concepts before you get started with this handbook…”

“…Before you get started with this book, you'll need a good understanding of calculus, as well as linear algebra…”

“…What you will learnGrasp important pre-steps in building NLP applications like POS taggingUse transfer and weakly supervised learning using libraries like SnorkelDo sentiment analysis using BERTApply encoder-decoder NN architectures and beam search for summarizing textsUse Transformer models with attention to bring images and text togetherBuild apps that generate captions and answer questions about images using custom TransformersUse advanced TensorFlow techniques like learning rate annealing, custom layers, and custom loss functions to build the latest DeepNLP modelsWho this book is forThis is not an introductory book and assumes the reader is familiar with basics of NLP and has fundamental Python skills, as well as basic knowledge of machine learning and undergraduate-level calculus and linear algebra. The readers who can benefit the most from this book include intermediate ML developers who are familiar with the basics of supervised learning and deep learning techniques and professionals who already use TensorFlow/Python for purposes such as data science, ML, research, analysis, etc…”

“…Learners should have a basic knowledge of linear algebra, calculus, and Python programming to effectively understand matrix calculus. …”

“…A basic knowledge of Python, AWS, linear algebra, probability, and cloud infrastructure is required before you get started with this handbook…”

“…This book skips the confused academic jargon and offers clear explanations that require only basic algebra. As you go, you'll build interesting projects with Python, including models for spam detection and image recognition. …”

“…This book skips the confused academic jargon and offers clear explanations that require only basic algebra. As you go, you'll build interesting projects with Python, including models for spam detection and image recognition. …”

“…A solid understanding of C++ and basic linear algebra, as well as experience in creating custom 3D applications without using premade rendering engines is required…”

“…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. …”

“…There's no heavy theory, and you'll learn how to offload most equations to the SymPy computer algebra system. What's Inside Speak the language of applied geometry Compose geometric transformations economically Craft custom splines for efficient curves and surface generation Confidently use geometry algorithms About the Reader Examples are in Python, and all you need is high school-level math. …”

“…One must have decent Python programming skills and a basic understanding of linear algebra and probability for this course. About The Author Lazy Programmer: The Lazy Programmer is an AI and machine learning engineer with a focus on deep learning, who also has experience in data science, big data engineering, and full-stack software engineering. …”