Machine Learning for Planetary Science

Machine Learning for Planetary Science
Author: Joern Helbert,Mario D'Amore,Michael Aye,Hannah Kerner
Publsiher: Elsevier
Total Pages: 232
Release: 2022-03-25
ISBN: 0128187220
Category: Science
Language: EN, FR, DE, ES & NL

Machine Learning for Planetary Science Book Excerpt:

Machine Learning for Planetary Science presents planetary scientists with a way to introduce machine learning into the research workflow as increasingly large nonlinear datasets are acquired from planetary exploration missions. The book explores research that leverages machine learning methods to enhance our scientific understanding of planetary data and serves as a guide for selecting the right methods and tools for solving a variety of everyday problems in planetary science using machine learning. Illustrating ways to employ machine learning in practice with case studies, the book is clearly organized into four parts to provide thorough context and easy navigation. The book covers a range of issues, from data analysis on the ground to data analysis onboard a spacecraft, and from prioritization of novel or interesting observations to enhanced missions planning. This book is therefore a key resource for planetary scientists working in data analysis, missions planning, and scientific observation. Includes links to a code repository for sharing codes and examples, some of which include executable Jupyter notebook files that can serve as tutorials Presents methods applicable to everyday problems faced by planetary scientists and sufficient for analyzing large datasets Serves as a guide for selecting the right method and tools for applying machine learning to particular analysis problems Utilizes case studies to illustrate how machine learning methods can be employed in practice

Machine Learning in Heliophysics

Machine Learning in Heliophysics
Author: Thomas Berger,Enrico Camporeale,Bala Poduval,Veronique A. Delouille,Sophie A. Murray
Publsiher: Frontiers Media SA
Total Pages: 135
Release: 2021-11-24
ISBN: 2889716716
Category: Science
Language: EN, FR, DE, ES & NL

Machine Learning in Heliophysics Book Excerpt:

Applications of Convolutional Neural Networks to Problems in Astronomy and Planetary Science

Applications of Convolutional Neural Networks to Problems in Astronomy and Planetary Science
Author: Emma Torres Chickles
Publsiher: Unknown
Total Pages: 72
Release: 2021
ISBN: 1928374650XXX
Category: Electronic Book
Language: EN, FR, DE, ES & NL

Applications of Convolutional Neural Networks to Problems in Astronomy and Planetary Science Book Excerpt:

Large-scale astronomical surveys and planetary missions have produced huge amounts of data. The exponential growth in data volume has allowed the application of novel data science techniques, including machine learning. We use machine learning methods to analyze and extract new information from two enormous datasets: images of impact craters captured by the Mars Reconnaissance Orbiter (MRO) and time-series data collected by the Transiting Exoplanet Survey Satellite (TESS). Using images of impact craters captured by the MRO, we infer the spatial variation in the retention of ejecta deposits on Mars. We do this by training a convolutional neural network (CNN) to detect the presence of ejecta deposits around small craters. Our machine learning method to detect pre- served ejecta deposits will enable the study of the processes driving landscape evolution on Mars. In a methodologically relevant but independent study, we conduct a census of different types of variability of nearby stars using photometric time-series data produced by TESS. We do this by extracting representational features from light curves using a convolutional autoencoder and clustering these features. Our unsupervised machine learning method will accelerate the augmentation of variable star catalogues, which are essential for studies of stellar magnetism, stellar evolution, and the habitability of hosted exoplanets.

Machine Learning

Machine Learning
Author: Yagang Zhang
Publsiher: BoD – Books on Demand
Total Pages: 446
Release: 2010-02-01
ISBN: 9533070331
Category: Games & Activities
Language: EN, FR, DE, ES & NL

Machine Learning Book Excerpt:

Machine learning techniques have the potential of alleviating the complexity of knowledge acquisition. This book presents today’s state and development tendencies of machine learning. It is a multi-author book. Taking into account the large amount of knowledge about machine learning and practice presented in the book, it is divided into three major parts: Introduction, Machine Learning Theory and Applications. Part I focuses on the introduction to machine learning. The author also attempts to promote a new design of thinking machines and development philosophy. Considering the growing complexity and serious difficulties of information processing in machine learning, in Part II of the book, the theoretical foundations of machine learning are considered, and they mainly include self-organizing maps (SOMs), clustering, artificial neural networks, nonlinear control, fuzzy system and knowledge-based system (KBS). Part III contains selected applications of various machine learning approaches, from flight delays, network intrusion, immune system, ship design to CT and RNA target prediction. The book will be of interest to industrial engineers and scientists as well as academics who wish to pursue machine learning. The book is intended for both graduate and postgraduate students in fields such as computer science, cybernetics, system sciences, engineering, statistics, and social sciences, and as a reference for software professionals and practitioners.

Machine Learning Optimization and Data Science

Machine Learning  Optimization  and Data Science
Author: Giuseppe Nicosia,Varun Ojha,Emanuele La Malfa,Giorgio Jansen,Vincenzo Sciacca,Panos Pardalos,Giovanni Giuffrida,Renato Umeton
Publsiher: Springer Nature
Total Pages: 666
Release: 2021-01-06
ISBN: 3030645800
Category: Computers
Language: EN, FR, DE, ES & NL

Machine Learning Optimization and Data Science Book Excerpt:

This two-volume set, LNCS 12565 and 12566, constitutes the refereed proceedings of the 6th International Conference on Machine Learning, Optimization, and Data Science, LOD 2020, held in Siena, Italy, in July 2020. The total of 116 full papers presented in this two-volume post-conference proceedings set was carefully reviewed and selected from 209 submissions. These research articles were written by leading scientists in the fields of machine learning, artificial intelligence, reinforcement learning, computational optimization, and data science presenting a substantial array of ideas, technologies, algorithms, methods, and applications.

Large Meteorite Impacts and Planetary Evolution VI

Large Meteorite Impacts and Planetary Evolution VI
Author: Wolf Uwe Reimold,Christian Koeberl
Publsiher: Geological Society of America
Total Pages: 644
Release: 2021-09-23
ISBN: 081372550X
Category: Science
Language: EN, FR, DE, ES & NL

Large Meteorite Impacts and Planetary Evolution VI Book Excerpt:

"This volume contains a sizable suite of contributions dealing with regional impact records (Australia, Sweden), impact craters and impactites, early Archean impacts and geophysical characteristics of impact structures, shock metamorphic investigations, post-impact hydrothermalism, and structural geology and morphometry of impact structures - on Earth and Mars"--

Discovery Science

Discovery Science
Author: Nathalie Japkowicz,Stan Matwin
Publsiher: Springer
Total Pages: 342
Release: 2015-10-04
ISBN: 3319242822
Category: Computers
Language: EN, FR, DE, ES & NL

Discovery Science Book Excerpt:

This book constitutes the proceedings of the 17th International Conference on Discovery Science, DS 2015, held in banff, AB, Canada in October 2015. The 16 long and 12 short papers presendted together with 4 invited talks in this volume were carefully reviewed and selected from 44 submissions. The combination of recent advances in the development and analysis of methods for discovering scienti c knowledge, coming from machine learning, data mining, and intelligent data analysis, as well as their application in various scienti c domains, on the one hand, with the algorithmic advances in machine learning theory, on the other hand, makes every instance of this joint event unique and attractive.

Algorithmic Learning Theory

Algorithmic Learning Theory
Author: Kamalika Chaudhuri,CLAUDIO GENTILE,Sandra Zilles
Publsiher: Springer
Total Pages: 395
Release: 2015-10-04
ISBN: 3319244868
Category: Computers
Language: EN, FR, DE, ES & NL

Algorithmic Learning Theory Book Excerpt:

This book constitutes the proceedings of the 26th International Conference on Algorithmic Learning Theory, ALT 2015, held in Banff, AB, Canada, in October 2015, and co-located with the 18th International Conference on Discovery Science, DS 2015. The 23 full papers presented in this volume were carefully reviewed and selected from 44 submissions. In addition the book contains 2 full papers summarizing the invited talks and 2 abstracts of invited talks. The papers are organized in topical sections named: inductive inference; learning from queries, teaching complexity; computational learning theory and algorithms; statistical learning theory and sample complexity; online learning, stochastic optimization; and Kolmogorov complexity, algorithmic information theory.

An Astrobiology Strategy for the Search for Life in the Universe

An Astrobiology Strategy for the Search for Life in the Universe
Author: National Academies of Sciences, Engineering, and Medicine,Division on Engineering and Physical Sciences,Space Studies Board,Committee on Astrobiology Science Strategy for the Search for Life in the Universe
Publsiher: National Academies Press
Total Pages: 188
Release: 2019-04-20
ISBN: 0309484162
Category: Science
Language: EN, FR, DE, ES & NL

An Astrobiology Strategy for the Search for Life in the Universe Book Excerpt:

Astrobiology is the study of the origin, evolution, distribution, and future of life in the universe. It is an inherently interdisciplinary field that encompasses astronomy, biology, geology, heliophysics, and planetary science, including complementary laboratory activities and field studies conducted in a wide range of terrestrial environments. Combining inherent scientific interest and public appeal, the search for life in the solar system and beyond provides a scientific rationale for many current and future activities carried out by the National Aeronautics and Science Administration (NASA) and other national and international agencies and organizations. Requested by NASA, this study offers a science strategy for astrobiology that outlines key scientific questions, identifies the most promising research in the field, and indicates the extent to which the mission priorities in existing decadal surveys address the search for life's origin, evolution, distribution, and future in the universe. This report makes recommendations for advancing the research, obtaining the measurements, and realizing NASA's goal to search for signs of life in the universe.

Knowledge Discovery in Big Data from Astronomy and Earth Observation

Knowledge Discovery in Big Data from Astronomy and Earth Observation
Author: Petr Skoda,Fathalrahman Adam
Publsiher: Elsevier
Total Pages: 472
Release: 2020-04-10
ISBN: 0128191554
Category: Science
Language: EN, FR, DE, ES & NL

Knowledge Discovery in Big Data from Astronomy and Earth Observation Book Excerpt:

Knowledge Discovery in Big Data from Astronomy and Earth Observation: Astrogeoinformatics bridges the gap between astronomy and geoscience in the context of applications, techniques and key principles of big data. Machine learning and parallel computing are increasingly becoming cross-disciplinary as the phenomena of Big Data is becoming common place. This book provides insight into the common workflows and data science tools used for big data in astronomy and geoscience. After establishing similarity in data gathering, pre-processing and handling, the data science aspects are illustrated in the context of both fields. Software, hardware and algorithms of big data are addressed. Finally, the book offers insight into the emerging science which combines data and expertise from both fields in studying the effect of cosmos on the earth and its inhabitants. Addresses both astronomy and geosciences in parallel, from a big data perspective Includes introductory information, key principles, applications and the latest techniques Well-supported by computing and information science-oriented chapters to introduce the necessary knowledge in these fields

Machine Learning on Mars

Machine Learning on Mars
Author: Hannah Rae Kerner
Publsiher: Unknown
Total Pages: 269
Release: 2019
ISBN: 1928374650XXX
Category: Machine learning
Language: EN, FR, DE, ES & NL

Machine Learning on Mars Book Excerpt:

There are more than 20 active missions exploring planets and small bodies beyond Earth in our solar system today. Many more have completed their journeys or will soon begin. Each spacecraft has a suite of instruments and sensors that provide a treasure trove of data that scientists use to advance our understanding of the past, present, and future of the solar system and universe. As more missions come online and the volume of data increases, it becomes more difficult for scientists to analyze these complex data at the desired pace. There is a need for systems that can rapidly and intelligently extract information from planetary instrument datasets and prioritize the most promising, novel, or relevant observations for scientific analysis. Machine learning methods can serve this need in a variety of ways: by uncovering patterns or features of interest in large, complex datasets that are difficult for humans to analyze; by inspiring new hypotheses based on structure and patterns revealed in data; or by automating tedious or time-consuming tasks. In this dissertation, I present machine learning solutions to enhance the tactical planning process for the Mars Science Laboratory Curiosity rover and future tactically-planned missions, as well as the science analysis process for archived and ongoing orbital imaging investigations such as the High Resolution Imaging Science Experiment (HiRISE) at Mars. These include detecting novel geology in multispectral images and active nuclear spectroscopy data, analyzing the intrinsic variability in active nuclear spectroscopy data with respect to elemental geochemistry, automating tedious image review processes, and monitoring changes in surface features such as impact craters in orbital remote sensing images. Collectively, this dissertation shows how machine learning can be a powerful tool for facilitating scientific discovery during active exploration missions and in retrospective analysis of archived data.

Machine Learning and Knowledge Discovery in Databases

Machine Learning and Knowledge Discovery in Databases
Author: Ulf Brefeld,Elisa Fromont,Andreas Hotho,Arno Knobbe,Marloes Maathuis,Céline Robardet
Publsiher: Springer Nature
Total Pages: 804
Release: 2020-04-30
ISBN: 3030461335
Category: Computers
Language: EN, FR, DE, ES & NL

Machine Learning and Knowledge Discovery in Databases Book Excerpt:

The three volume proceedings LNAI 11906 – 11908 constitutes the refereed proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2019, held in Würzburg, Germany, in September 2019. The total of 130 regular papers presented in these volumes was carefully reviewed and selected from 733 submissions; there are 10 papers in the demo track. The contributions were organized in topical sections named as follows: Part I: pattern mining; clustering, anomaly and outlier detection, and autoencoders; dimensionality reduction and feature selection; social networks and graphs; decision trees, interpretability, and causality; strings and streams; privacy and security; optimization. Part II: supervised learning; multi-label learning; large-scale learning; deep learning; probabilistic models; natural language processing. Part III: reinforcement learning and bandits; ranking; applied data science: computer vision and explanation; applied data science: healthcare; applied data science: e-commerce, finance, and advertising; applied data science: rich data; applied data science: applications; demo track.

Machine Learning and Artificial Intelligence in Geosciences

Machine Learning and Artificial Intelligence in Geosciences
Author: Anonim
Publsiher: Academic Press
Total Pages: 316
Release: 2020-09-25
ISBN: 0128216840
Category: Science
Language: EN, FR, DE, ES & NL

Machine Learning and Artificial Intelligence in Geosciences Book Excerpt:

Advances in Geophysics, Volume 61 - Machine Learning and Artificial Intelligence in Geosciences, the latest release in this highly-respected publication in the field of geophysics, contains new chapters on a variety of topics, including a historical review on the development of machine learning, machine learning to investigate fault rupture on various scales, a review on machine learning techniques to describe fractured media, signal augmentation to improve the generalization of deep neural networks, deep generator priors for Bayesian seismic inversion, as well as a review on homogenization for seismology, and more. Provides high-level reviews of the latest innovations in geophysics Written by recognized experts in the field Presents an essential publication for researchers in all fields of geophysics

Issues in Artificial Intelligence Robotics and Machine Learning 2013 Edition

Issues in Artificial Intelligence  Robotics and Machine Learning  2013 Edition
Author: Anonim
Publsiher: ScholarlyEditions
Total Pages: 1209
Release: 2013-05-01
ISBN: 1490105972
Category: Computers
Language: EN, FR, DE, ES & NL

Issues in Artificial Intelligence Robotics and Machine Learning 2013 Edition Book Excerpt:

Issues in Artificial Intelligence, Robotics and Machine Learning: 2013 Edition is a ScholarlyEditions™ book that delivers timely, authoritative, and comprehensive information about Expert Systems. The editors have built Issues in Artificial Intelligence, Robotics and Machine Learning: 2013 Edition on the vast information databases of ScholarlyNews.™ You can expect the information about Expert Systems in this book to be deeper than what you can access anywhere else, as well as consistently reliable, authoritative, informed, and relevant. The content of Issues in Artificial Intelligence, Robotics and Machine Learning: 2013 Edition has been produced by the world’s leading scientists, engineers, analysts, research institutions, and companies. All of the content is from peer-reviewed sources, and all of it is written, assembled, and edited by the editors at ScholarlyEditions™ and available exclusively from us. You now have a source you can cite with authority, confidence, and credibility. More information is available at http://www.ScholarlyEditions.com/.

Encyclopedia of Computer Science and Technology

Encyclopedia of Computer Science and Technology
Author: Allen Kent,James G. Williams
Publsiher: CRC Press
Total Pages: 500
Release: 2021-07-28
ISBN: 1000444279
Category: Computers
Language: EN, FR, DE, ES & NL

Encyclopedia of Computer Science and Technology Book Excerpt:

This 41st volume covers Application of Bayesan Belief Networks to Highway Construction to Virtual Reality Software and Technology.

Advances in Machine Learning Research and Application 2012 Edition

Advances in Machine Learning Research and Application  2012 Edition
Author: Anonim
Publsiher: ScholarlyEditions
Total Pages: 1932
Release: 2012-12-26
ISBN: 1464990697
Category: Computers
Language: EN, FR, DE, ES & NL

Advances in Machine Learning Research and Application 2012 Edition Book Excerpt:

Advances in Machine Learning Research and Application / 2012 Edition is a ScholarlyEditions™ eBook that delivers timely, authoritative, and comprehensive information about Machine Learning. The editors have built Advances in Machine Learning Research and Application / 2012 Edition on the vast information databases of ScholarlyNews.™ You can expect the information about Machine Learning in this eBook to be deeper than what you can access anywhere else, as well as consistently reliable, authoritative, informed, and relevant. The content of Advances in Machine Learning Research and Application / 2012 Edition has been produced by the world’s leading scientists, engineers, analysts, research institutions, and companies. All of the content is from peer-reviewed sources, and all of it is written, assembled, and edited by the editors at ScholarlyEditions™ and available exclusively from us. You now have a source you can cite with authority, confidence, and credibility. More information is available at http://www.ScholarlyEditions.com/.

Global Seismicity Dynamics and Data Driven Science

Global Seismicity Dynamics and Data Driven Science
Author: Mitsuhiro Toriumi
Publsiher: Springer Nature
Total Pages: 231
Release: 2020-10-07
ISBN: 981155109X
Category: Science
Language: EN, FR, DE, ES & NL

Global Seismicity Dynamics and Data Driven Science Book Excerpt:

The recent explosion of global and regional seismicity data in the world requires new methods of investigation of microseismicity and development of their modelling to understand the nature of whole earth mechanics. In this book, the author proposes a powerful tool to reveal the characteristic features of global and regional microseismicity big data accumulated in the databases of the world. The method proposed in this monograph is based on (1) transformation of stored big data to seismicity density data archives, (2) linear transformation of microseismicity density data matrixes to correlated seismicity matrixes by means of the singular value decomposition method, (3) time series analyses of globally and regionally correlated seismicity rates, and (4) the minimal non-linear equations approximation of their correlated seismicity rate dynamics. Minimal non-linear modelling is the manifestation for strongly correlated seismicity time series controlled by Langevin-type stochastic dynamic equations involving deterministic terms and random Gaussian noises. A deterministic term is composed minimally with correlated seismicity rate vectors of a linear term and of a term with a third exponent. Thus, the dynamics of correlated seismicity in the world contains linearly changing stable nodes and rapid transitions between them with transient states. This book contains discussions of future possibilities of stochastic extrapolations of global and regional seismicity in order to reduce earthquake disasters worldwide. The dataset files are available online and can be downloaded at springer.com.

Advances in Machine Learning and Computational Intelligence

Advances in Machine Learning and Computational Intelligence
Author: Srikanta Patnaik,Xin-She Yang,Ishwar K. Sethi
Publsiher: Springer Nature
Total Pages: 878
Release: 2020-07-25
ISBN: 9811552436
Category: Technology & Engineering
Language: EN, FR, DE, ES & NL

Advances in Machine Learning and Computational Intelligence Book Excerpt:

This book gathers selected high-quality papers presented at the International Conference on Machine Learning and Computational Intelligence (ICMLCI-2019), jointly organized by Kunming University of Science and Technology and the Interscience Research Network, Bhubaneswar, India, from April 6 to 7, 2019. Addressing virtually all aspects of intelligent systems, soft computing and machine learning, the topics covered include: prediction; data mining; information retrieval; game playing; robotics; learning methods; pattern visualization; automated knowledge acquisition; fuzzy, stochastic and probabilistic computing; neural computing; big data; social networks and applications of soft computing in various areas.

Deep Learning for the Earth Sciences

Deep Learning for the Earth Sciences
Author: Gustau Camps-Valls,Devis Tuia,Xiao Xiang Zhu,Markus Reichstein
Publsiher: John Wiley & Sons
Total Pages: 432
Release: 2021-08-18
ISBN: 1119646162
Category: Technology & Engineering
Language: EN, FR, DE, ES & NL

Deep Learning for the Earth Sciences Book Excerpt:

DEEP LEARNING FOR THE EARTH SCIENCES Explore this insightful treatment of deep learning in the field of earth sciences, from four leading voices Deep learning is a fundamental technique in modern Artificial Intelligence and is being applied to disciplines across the scientific spectrum; earth science is no exception. Yet, the link between deep learning and Earth sciences has only recently entered academic curricula and thus has not yet proliferated. Deep Learning for the Earth Sciences delivers a unique perspective and treatment of the concepts, skills, and practices necessary to quickly become familiar with the application of deep learning techniques to the Earth sciences. The book prepares readers to be ready to use the technologies and principles described in their own research. The distinguished editors have also included resources that explain and provide new ideas and recommendations for new research especially useful to those involved in advanced research education or those seeking PhD thesis orientations. Readers will also benefit from the inclusion of: An introduction to deep learning for classification purposes, including advances in image segmentation and encoding priors, anomaly detection and target detection, and domain adaptation An exploration of learning representations and unsupervised deep learning, including deep learning image fusion, image retrieval, and matching and co-registration Practical discussions of regression, fitting, parameter retrieval, forecasting and interpolation An examination of physics-aware deep learning models, including emulation of complex codes and model parametrizations Perfect for PhD students and researchers in the fields of geosciences, image processing, remote sensing, electrical engineering and computer science, and machine learning, Deep Learning for the Earth Sciences will also earn a place in the libraries of machine learning and pattern recognition researchers, engineers, and scientists.

Machine Learning with TensorFlow Second Edition

Machine Learning with TensorFlow  Second Edition
Author: Mattmann A. Chris
Publsiher: Manning Publications
Total Pages: 456
Release: 2021-02-02
ISBN: 1617297712
Category: Computers
Language: EN, FR, DE, ES & NL

Machine Learning with TensorFlow Second Edition Book Excerpt:

Updated with new code, new projects, and new chapters, Machine Learning with TensorFlow, Second Edition gives readers a solid foundation in machine-learning concepts and the TensorFlow library. Summary Updated with new code, new projects, and new chapters, Machine Learning with TensorFlow, Second Edition gives readers a solid foundation in machine-learning concepts and the TensorFlow library. Written by NASA JPL Deputy CTO and Principal Data Scientist Chris Mattmann, all examples are accompanied by downloadable Jupyter Notebooks for a hands-on experience coding TensorFlow with Python. New and revised content expands coverage of core machine learning algorithms, and advancements in neural networks such as VGG-Face facial identification classifiers and deep speech classifiers. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology Supercharge your data analysis with machine learning! ML algorithms automatically improve as they process data, so results get better over time. You don’t have to be a mathematician to use ML: Tools like Google’s TensorFlow library help with complex calculations so you can focus on getting the answers you need. About the book Machine Learning with TensorFlow, Second Edition is a fully revised guide to building machine learning models using Python and TensorFlow. You’ll apply core ML concepts to real-world challenges, such as sentiment analysis, text classification, and image recognition. Hands-on examples illustrate neural network techniques for deep speech processing, facial identification, and auto-encoding with CIFAR-10. What's inside Machine Learning with TensorFlow Choosing the best ML approaches Visualizing algorithms with TensorBoard Sharing results with collaborators Running models in Docker About the reader Requires intermediate Python skills and knowledge of general algebraic concepts like vectors and matrices. Examples use the super-stable 1.15.x branch of TensorFlow and TensorFlow 2.x. About the author Chris Mattmann is the Division Manager of the Artificial Intelligence, Analytics, and Innovation Organization at NASA Jet Propulsion Lab. The first edition of this book was written by Nishant Shukla with Kenneth Fricklas. Table of Contents PART 1 - YOUR MACHINE-LEARNING RIG 1 A machine-learning odyssey 2 TensorFlow essentials PART 2 - CORE LEARNING ALGORITHMS 3 Linear regression and beyond 4 Using regression for call-center volume prediction 5 A gentle introduction to classification 6 Sentiment classification: Large movie-review dataset 7 Automatically clustering data 8 Inferring user activity from Android accelerometer data 9 Hidden Markov models 10 Part-of-speech tagging and word-sense disambiguation PART 3 - THE NEURAL NETWORK PARADIGM 11 A peek into autoencoders 12 Applying autoencoders: The CIFAR-10 image dataset 13 Reinforcement learning 14 Convolutional neural networks 15 Building a real-world CNN: VGG-Face ad VGG-Face Lite 16 Recurrent neural networks 17 LSTMs and automatic speech recognition 18 Sequence-to-sequence models for chatbots 19 Utility landscape