Materias: “…Science Periodicals…”

Materias: “…Science Arab countries Periodicals…”

Materias: “…Social sciences Periodicals…”

Materias: “…Social sciences Methodology Periodicals…”

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Materias:

“…The research topics addressed by Advanced Modeling and Simulation in Engineering Sciences (AMSES) cover the vast domain of the advanced modeling and simulation of materials, processes and structures governed by the laws of mechanics. …”

“…"Teaching and Learning in Communication Sciences & Disorders (TLCSD) publishes articles that reflect current and exemplary scholarship of teaching and learning (SoTL) research in speech-language pathology and audiology."…”

“…"Offers an exciting publication outlet for Physical Science reports. It covers all areas of Theoretical Physics, Geophysical & Atmospheric Physics, Radiation & Medical Physics, Solid State Physics, Astrophysics & Cosmology, Environmental Chemistry, Physical Chemistry, Organic & Inorganic Chemistry, Artificial Intelligence, Computer Architecture & Engineering, Database Management Systems, Human-Computer Interaction, Operating Systems & Networking, Pure Mathematics, Number theory, Applied mathematics, Mathematical Finance, Numerical Analysis & Scientific Computing, Topology & Differential Geometry, Biostatistics & Data Science Decision Analysis and Support, Econometric Modelling, Planetary Exploration and Exoplanets"…”

“…You don't need any prior experience with Anaconda, but a working knowledge of Python and data science basics is a must…”

Tabla de Contenidos: “…Intro -- Table of Contents -- About the Author -- About the Technical Reviewer -- Chapter 1: An Introduction to Artificial Intelligence in Medical Sciences and Psychology -- Context of the Book -- The Book's Central Point -- Artificial Intelligence Subsets Covered in this Book -- Structure of the Book -- Tools Used in This Book -- Python Distribution Package -- Anaconda Distribution Package -- Jupyter Notebook -- Python Libraries -- Encapsulating Artificial Intelligence -- Implementing Algorithms -- Supervised Algorithms -- Unsupervised Algorithms -- Artificial Neural Networks -- Conclusion -- Chapter 2: Realizing Patterns in Diseases with Neural Networks -- Classifying Cardiovascular Disease Diagnosis Outcome Data by Executing a Deep Belief Network -- Preprocessing the Cardiovascular Disease Diagnosis Outcome Data -- Debunking Deep Belief Networks -- Designing the Deep Belief Network -- Relu Activation Function -- Sigmoid Activation Function -- Training the Deep Belief Network -- Outlining the Deep Belief Network's Predictions -- Considering the Deep Neural Network's Performance -- Accuracy Fluctuations Across Epochs in Training and Cross-Validation -- Binary Cross-Entropy Loss Fluctuations Across Epochs in Training and Cross-Validation -- Classifying Diabetes Diagnosis Outcome Data by Executing a Deep Belief Network -- Executing a Deep Belief Network to Classify Diabetes Diagnosis Outcome Data -- Outlining the Deep Belief Network's Predictions -- Considering the Deep Neural Network's Performance -- Accuracy Fluctuations Across Epochs in Training and Cross-Validation -- Binary Cross-Entropy Loss Fluctuations Across Epochs in Training and Cross-Validation -- Conclusion -- Chapter 3: A Case for COVID-19: Considering the Hidden States and Simulation Results -- Executing the Hidden Markov Model -- Descriptive Analysis…”

“…Fichte a composé sa grande œuvre philosophique, la Doctrine de la science, de 1794/95 à 1814. Ce projet est d’une radicalité absolue: aller plus loin que le Kant de la Critique de la raison pure dans l’explication de la genèse conjointe de la conscience, du savoir et du monde objectif. …”

“…Updated for the R 4.0 release, this book teaches you techniques for both data manipulation and visualization and shows you the best way for developing new software packages for R. Beginning Data Science in R 4, Second Edition details how data science is a combination of statistics, computational science, and machine learning. …”

Tabla de Contenidos: “…Chapter 1: What is Productive and Efficient Data Science -- Chapter 2: Better Programming Principles for Efficient Data Science -- Chapter 3: How to Use Python Data Science Packages more Productively -- Chapter 4: Writing Machine Learning Code More Productively -- Chapter 5: Modular and Productive Deep Learning Code -- Chapter 6: Build Your Own Machine Learning Estimator/Package -- Chapter 7: Some Cool Utility Packages -- Chapter 8: Testing the Machine Learning Code -- Chapter 9: Memory and Timing Profiling -- Chapter 10: Scalable Data Science -- Chapter 11: Parallelized Data Science -- Chapter 12: GPU-Based Data Science for High Productivity -- Chapter 13: Other Useful Skills to Master -- Chapter 14: Wrapping It Up…”

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