The Science Of Deep Learning
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The Science Of Deep Learning
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Author : Iddo Drori
language : en
Publisher: Cambridge University Press
Release Date : 2022-08-18
The Science Of Deep Learning written by Iddo Drori and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-08-18 with Computers categories.
The Science of Deep Learning emerged from courses taught by the author that have provided thousands of students with training and experience for their academic studies, and prepared them for careers in deep learning, machine learning, and artificial intelligence in top companies in industry and academia. The book begins by covering the foundations of deep learning, followed by key deep learning architectures. Subsequent parts on generative models and reinforcement learning may be used as part of a deep learning course or as part of a course on each topic. The book includes state-of-the-art topics such as Transformers, graph neural networks, variational autoencoders, and deep reinforcement learning, with a broad range of applications. The appendices provide equations for computing gradients in backpropagation and optimization, and best practices in scientific writing and reviewing. The text presents an up-to-date guide to the field built upon clear visualizations using a unified notation and equations, lowering the barrier to entry for the reader. The accompanying website provides complementary code and hundreds of exercises with solutions.
Deep Learning For The Life Sciences
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Author : Bharath Ramsundar
language : en
Publisher: "O'Reilly Media, Inc."
Release Date : 2019-04-10
Deep Learning For The Life Sciences written by Bharath Ramsundar and has been published by "O'Reilly Media, Inc." this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-04-10 with Science categories.
Deep learning has already achieved remarkable results in many fields. Now it’s making waves throughout the sciences broadly and the life sciences in particular. This practical book teaches developers and scientists how to use deep learning for genomics, chemistry, biophysics, microscopy, medical analysis, and other fields. Ideal for practicing developers and scientists ready to apply their skills to scientific applications such as biology, genetics, and drug discovery, this book introduces several deep network primitives. You’ll follow a case study on the problem of designing new therapeutics that ties together physics, chemistry, biology, and medicine—an example that represents one of science’s greatest challenges. Learn the basics of performing machine learning on molecular data Understand why deep learning is a powerful tool for genetics and genomics Apply deep learning to understand biophysical systems Get a brief introduction to machine learning with DeepChem Use deep learning to analyze microscopic images Analyze medical scans using deep learning techniques Learn about variational autoencoders and generative adversarial networks Interpret what your model is doing and how it’s working
Data Science For Beginners
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Author : Russel R Russo
language : en
Publisher:
Release Date : 2020-02-02
Data Science For Beginners written by Russel R Russo and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-02-02 with categories.
Are you fascinated by Data Science but it seems too complicated? Do you want to learn everything about Artificial Intelligence but it looks like it is an exclusive club? If this is you, please keep reading: you are in the right place, looking at the right book. SInce you are reading these lines you have probably already noticed this: Artificial Intelligence is all around you. Your smartphone that suggests you the next word you want to type, your Netflix account that recommends you the series you may like or Spotify's personalised playlists. This is how machines are learning from you in everyday life. And these examples are only the surface of this technological revolution. Everyone knows (well, almost everyone) how important Data Science is for the growth and success of the biggest tech companies, and many people know about the Machine Learning impact in science, medicine and statistics. Also, it is quite commonly known that Artificial Intelligence, Machine Learning Deep Learning, and the mastering of their most important language, Python, can offer a lot of possibilities in work and business. And you yourself are probably thinking "I surely can see that opportunity, but how can I seize it?" Well, if you kept reading so far you are on the right track to answer your question. Either if you want to start your own AI entreprise, to empower your business or to work in the greatest and most innovative companies, Artificial Intelligence is the future, and Python and Neural Networks programming is The Skill you want to have. The good news is that there is no exclusive club, you can easily (if you commit, of course) learn how to find your way around Artificial Intelligence, Data Science, Deep Learning and Machine Learning, and to do that Data Science for Beginners is the best way. In Data Science for Beginners you will discover: The most effective starting points when training deep neural nets The smartest way to approach Machine Learning What libraries are and which one is the best for you Tips and tricks for a smooth and painless journey into artificial intelligence Why decision tree is the way The TensorFlow parts that are going to make your coding life easy Why python is the best language for Machine Learning How to bring your ideas into a computer How to talk with deep neural networks How to deal with variables and data The most common myths about Machine Learning debunked Even If you don't know anything about programming, understanding Data Science is the ideal place to start. Still, if you already know something about programming but not about how to apply it to Artificial Intelligence, Data Science is what you want to understand. Download now Data Science for Beginners to start your path of Artificial Intelligence.
Proceedings Of The 5th International Conference On Data Science Machine Learning And Applications Volume 2
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Author : Amit Kumar
language : en
Publisher: Springer Nature
Release Date : 2024-10-19
Proceedings Of The 5th International Conference On Data Science Machine Learning And Applications Volume 2 written by Amit Kumar and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-10-19 with Computers categories.
This book includes peer reviewed articles from the 5th International Conference on Data Science, Machine Learning and Applications, 2023, held at the G Narayanamma Institute of Technology and Sciences, Hyderabad on 15-16th December, India. ICDSMLA is one of the most prestigious conferences conceptualized in the field of Data Science & Machine Learning offering in-depth information on the latest developments in Artificial Intelligence, Machine Learning, Soft Computing, Human Computer Interaction, and various data science & machine learning applications. It provides a platform for academicians, scientists, researchers and professionals around the world to showcase broad range of perspectives, practices, and technical expertise in these fields. It offers participants the opportunity to stay informed about the latest developments in data science and machine learning.
Deep Learning In Science
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Author : Pierre Baldi
language : en
Publisher: Cambridge University Press
Release Date : 2021-07-01
Deep Learning In Science written by Pierre Baldi and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-07-01 with Computers categories.
This is the first rigorous, self-contained treatment of the theory of deep learning. Starting with the foundations of the theory and building it up, this is essential reading for any scientists, instructors, and students interested in artificial intelligence and deep learning. It provides guidance on how to think about scientific questions, and leads readers through the history of the field and its fundamental connections to neuroscience. The author discusses many applications to beautiful problems in the natural sciences, in physics, chemistry, and biomedicine. Examples include the search for exotic particles and dark matter in experimental physics, the prediction of molecular properties and reaction outcomes in chemistry, and the prediction of protein structures and the diagnostic analysis of biomedical images in the natural sciences. The text is accompanied by a full set of exercises at different difficulty levels and encourages out-of-the-box thinking.
Advanced Interdisciplinary Applications Of Deep Learning For Data Science
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Author : Mathew Biju, Soly
language : en
Publisher: IGI Global
Release Date : 2024-12-18
Advanced Interdisciplinary Applications Of Deep Learning For Data Science written by Mathew Biju, Soly and has been published by IGI Global this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-12-18 with Computers categories.
Advanced interdisciplinary applications of deep learning are revolutionizing the field of data science, enabling new solutions across industries. By leveraging neural networks and datasets, deep learning techniques are transforming how businesses analyze, interpret, and gain insights from complex data. In fields like healthcare, finance, engineering, and business, deep learning models can uncover patterns and make predictions to enhance data-driven decision-making while fostering new opportunities in areas like personalized medicine, autonomous systems, and predictive analytics. As deep learning continues evolves, its interdisciplinary impact expands, creating new possibilities for solving challenges in data science. Advanced Interdisciplinary Applications of Deep Learning for Data Science explores innovations of deep learning technology in data science for various industries and sectors. It examines solutions for decision-making and patient or customer service using machine learning, AI, and predictive analytics. This book covers topics such as bioinformatics, environmental monitoring, and medical diseases, and is a useful resource for data scientists, computer engineers, medical professionals, academicians, and researchers.
Hands On Transfer Learning With Python
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Author : Dipanjan Sarkar
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-08-31
Hands On Transfer Learning With Python written by Dipanjan Sarkar and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-08-31 with Computers categories.
Deep learning simplified by taking supervised, unsupervised, and reinforcement learning to the next level using the Python ecosystem Key Features Build deep learning models with transfer learning principles in Python implement transfer learning to solve real-world research problems Perform complex operations such as image captioning neural style transfer Book Description Transfer learning is a machine learning (ML) technique where knowledge gained during training a set of problems can be used to solve other similar problems. The purpose of this book is two-fold; firstly, we focus on detailed coverage of deep learning (DL) and transfer learning, comparing and contrasting the two with easy-to-follow concepts and examples. The second area of focus is real-world examples and research problems using TensorFlow, Keras, and the Python ecosystem with hands-on examples. The book starts with the key essential concepts of ML and DL, followed by depiction and coverage of important DL architectures such as convolutional neural networks (CNNs), deep neural networks (DNNs), recurrent neural networks (RNNs), long short-term memory (LSTM), and capsule networks. Our focus then shifts to transfer learning concepts, such as model freezing, fine-tuning, pre-trained models including VGG, inception, ResNet, and how these systems perform better than DL models with practical examples. In the concluding chapters, we will focus on a multitude of real-world case studies and problems associated with areas such as computer vision, audio analysis and natural language processing (NLP). By the end of this book, you will be able to implement both DL and transfer learning principles in your own systems. What you will learn Set up your own DL environment with graphics processing unit (GPU) and Cloud support Delve into transfer learning principles with ML and DL models Explore various DL architectures, including CNN, LSTM, and capsule networks Learn about data and network representation and loss functions Get to grips with models and strategies in transfer learning Walk through potential challenges in building complex transfer learning models from scratch Explore real-world research problems related to computer vision and audio analysis Understand how transfer learning can be leveraged in NLP Who this book is for Hands-On Transfer Learning with Python is for data scientists, machine learning engineers, analysts and developers with an interest in data and applying state-of-the-art transfer learning methodologies to solve tough real-world problems. Basic proficiency in machine learning and Python is required.
Deep Learning With Tensorflow
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Author : Giancarlo Zaccone
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-03-30
Deep Learning With Tensorflow written by Giancarlo Zaccone and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-03-30 with Computers categories.
Delve into neural networks, implement deep learning algorithms, and explore layers of data abstraction with the help of TensorFlow. Key Features Learn how to implement advanced techniques in deep learning with Google's brainchild, TensorFlow Explore deep neural networks and layers of data abstraction with the help of this comprehensive guide Gain real-world contextualization through some deep learning problems concerning research and application Book Description Deep learning is a branch of machine learning algorithms based on learning multiple levels of abstraction. Neural networks, which are at the core of deep learning, are being used in predictive analytics, computer vision, natural language processing, time series forecasting, and to perform a myriad of other complex tasks. This book is conceived for developers, data analysts, machine learning practitioners and deep learning enthusiasts who want to build powerful, robust, and accurate predictive models with the power of TensorFlow, combined with other open source Python libraries. Throughout the book, you’ll learn how to develop deep learning applications for machine learning systems using Feedforward Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, Autoencoders, and Factorization Machines. Discover how to attain deep learning programming on GPU in a distributed way. You'll come away with an in-depth knowledge of machine learning techniques and the skills to apply them to real-world projects. What you will learn Apply deep machine intelligence and GPU computing with TensorFlow Access public datasets and use TensorFlow to load, process, and transform the data Discover how to use the high-level TensorFlow API to build more powerful applications Use deep learning for scalable object detection and mobile computing Train machines quickly to learn from data by exploring reinforcement learning techniques Explore active areas of deep learning research and applications Who this book is for The book is for people interested in machine learning and machine intelligence. A rudimentary level of programming in one language is assumed, as is a basic familiarity with computer science techniques and technologies, including a basic awareness of computer hardware and algorithms. Some competence in mathematics is needed to the level of elementary linear algebra and calculus.
Advanced Network Technologies And Intelligent Computing
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Author : Anshul Verma
language : en
Publisher: Springer Nature
Release Date : 2024-08-07
Advanced Network Technologies And Intelligent Computing written by Anshul Verma and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-08-07 with Computers categories.
The 4-volume proceedings set CCIS 2090, 2091,2092 and 2093 constitute the refereed post-conference proceedings of the Third International Conference on Advanced Network Technologies and Intelligent Computing, ANTIC 2023, held in Varanasi, India, during December 20-22, 2023. The 87 full papers and 11 short papers included in this book were carefully reviewed and selected from 487 submissions. The conference papers are organized in topical sections on: Part I - Advanced Network Technologies. Part II - Advanced Network Technologies; Intelligent Computing. Part III - IV - Intelligent Computing.
Mastering Spark With R
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Author : Javier Luraschi
language : en
Publisher: O'Reilly Media
Release Date : 2019-10-07
Mastering Spark With R written by Javier Luraschi and has been published by O'Reilly Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-07 with Computers categories.
If you’re like most R users, you have deep knowledge and love for statistics. But as your organization continues to collect huge amounts of data, adding tools such as Apache Spark makes a lot of sense. With this practical book, data scientists and professionals working with large-scale data applications will learn how to use Spark from R to tackle big data and big compute problems. Authors Javier Luraschi, Kevin Kuo, and Edgar Ruiz show you how to use R with Spark to solve different data analysis problems. This book covers relevant data science topics, cluster computing, and issues that should interest even the most advanced users. Analyze, explore, transform, and visualize data in Apache Spark with R Create statistical models to extract information and predict outcomes; automate the process in production-ready workflows Perform analysis and modeling across many machines using distributed computing techniques Use large-scale data from multiple sources and different formats with ease from within Spark Learn about alternative modeling frameworks for graph processing, geospatial analysis, and genomics at scale Dive into advanced topics including custom transformations, real-time data processing, and creating custom Spark extensions