Deep Generative Models

Download Deep Generative Models full books in PDF, epub, and Kindle. Read online Deep Generative Models ebook anywhere anytime directly on your device. Fast Download speed and no annoying ads. We cannot guarantee that every ebooks is available!

Generative Deep Learning

Download Generative Deep Learning PDF Online Free

Author : David Foster
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 1492041890
Total Pages : 301 pages
Book Rating : 4.4/5 (92 download)

Book Synopsis Generative Deep Learning by : David Foster

Download or read book Generative Deep Learning written by David Foster and published by "O'Reilly Media, Inc.". This book was released on 2019-06-28 with total page 301 pages. Available in PDF, EPUB and Kindle. Book excerpt: Generative modeling is one of the hottest topics in AI. It’s now possible to teach a machine to excel at human endeavors such as painting, writing, and composing music. With this practical book, machine-learning engineers and data scientists will discover how to re-create some of the most impressive examples of generative deep learning models, such as variational autoencoders,generative adversarial networks (GANs), encoder-decoder models and world models. Author David Foster demonstrates the inner workings of each technique, starting with the basics of deep learning before advancing to some of the most cutting-edge algorithms in the field. Through tips and tricks, you’ll understand how to make your models learn more efficiently and become more creative. Discover how variational autoencoders can change facial expressions in photos Build practical GAN examples from scratch, including CycleGAN for style transfer and MuseGAN for music generation Create recurrent generative models for text generation and learn how to improve the models using attention Understand how generative models can help agents to accomplish tasks within a reinforcement learning setting Explore the architecture of the Transformer (BERT, GPT-2) and image generation models such as ProGAN and StyleGAN

Deep Generative Modeling

Download Deep Generative Modeling PDF Online Free

Author : Jakub M. Tomczak
Publisher : Springer
ISBN 13 : 9783030931575
Total Pages : 284 pages
Book Rating : 4.9/5 (315 download)

Book Synopsis Deep Generative Modeling by : Jakub M. Tomczak

Download or read book Deep Generative Modeling written by Jakub M. Tomczak and published by Springer. This book was released on 2022-03-16 with total page 284 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook tackles the problem of formulating AI systems by combining probabilistic modeling and deep learning. Moreover, it goes beyond typical predictive modeling and brings together supervised learning and unsupervised learning. The resulting paradigm, called deep generative modeling, utilizes the generative perspective on perceiving the surrounding world. It assumes that each phenomenon is driven by an underlying generative process that defines a joint distribution over random variables and their stochastic interactions, i.e., how events occur and in what order. The adjective "deep" comes from the fact that the distribution is parameterized using deep neural networks. There are two distinct traits of deep generative modeling. First, the application of deep neural networks allows rich and flexible parameterization of distributions. Second, the principled manner of modeling stochastic dependencies using probability theory ensures rigorous formulation and prevents potential flaws in reasoning. Moreover, probability theory provides a unified framework where the likelihood function plays a crucial role in quantifying uncertainty and defining objective functions. Deep Generative Modeling is designed to appeal to curious students, engineers, and researchers with a modest mathematical background in undergraduate calculus, linear algebra, probability theory, and the basics in machine learning, deep learning, and programming in Python and PyTorch (or other deep learning libraries). It will appeal to students and researchers from a variety of backgrounds, including computer science, engineering, data science, physics, and bioinformatics, who wish to become familiar with deep generative modeling. To engage the reader, the book introduces fundamental concepts with specific examples and code snippets. The full code accompanying the book is available on github. The ultimate aim of the book is to outline the most important techniques in deep generative modeling and, eventually, enable readers to formulate new models and implement them.

Deep Generative Models, and Data Augmentation, Labelling, and Imperfections

Download Deep Generative Models, and Data Augmentation, Labelling, and Imperfections PDF Online Free

Author : Sandy Engelhardt
Publisher : Springer Nature
ISBN 13 : 3030882101
Total Pages : 278 pages
Book Rating : 4.0/5 (38 download)

Book Synopsis Deep Generative Models, and Data Augmentation, Labelling, and Imperfections by : Sandy Engelhardt

Download or read book Deep Generative Models, and Data Augmentation, Labelling, and Imperfections written by Sandy Engelhardt and published by Springer Nature. This book was released on 2021-09-29 with total page 278 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the First MICCAI Workshop on Deep Generative Models, DG4MICCAI 2021, and the First MICCAI Workshop on Data Augmentation, Labelling, and Imperfections, DALI 2021, held in conjunction with MICCAI 2021, in October 2021. The workshops were planned to take place in Strasbourg, France, but were held virtually due to the COVID-19 pandemic. DG4MICCAI 2021 accepted 12 papers from the 17 submissions received. The workshop focusses on recent algorithmic developments, new results, and promising future directions in Deep Generative Models. Deep generative models such as Generative Adversarial Network (GAN) and Variational Auto-Encoder (VAE) are currently receiving widespread attention from not only the computer vision and machine learning communities, but also in the MIC and CAI community. For DALI 2021, 15 papers from 32 submissions were accepted for publication. They focus on rigorous study of medical data related to machine learning systems.

Deep Learning

Download Deep Learning PDF Online Free

Author : Ian Goodfellow
Publisher : MIT Press
ISBN 13 : 0262337371
Total Pages : 801 pages
Book Rating : 4.2/5 (623 download)

Book Synopsis Deep Learning by : Ian Goodfellow

Download or read book Deep Learning written by Ian Goodfellow and published by MIT Press. This book was released on 2016-11-10 with total page 801 pages. Available in PDF, EPUB and Kindle. Book excerpt: An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

On the Evaluation of Deep Generative Models

Download On the Evaluation of Deep Generative Models PDF Online Free

Author : Sharon Zhou
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (125 download)

Book Synopsis On the Evaluation of Deep Generative Models by : Sharon Zhou

Download or read book On the Evaluation of Deep Generative Models written by Sharon Zhou and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Evaluation drives and tracks progress in every field. Metrics of evaluation are designed to assess important criteria in an area, and aid us in understanding the quantitative differences between one breakthrough and another. In machine learning, evaluation metrics have historically acted as north stars towards which researchers have optimized and organized their methods and findings. While evaluation metrics have been straightforward to construct and implement in some subfields of machine learning, they have been notoriously difficult to design in generative models. Several reasons emerge to explain this: (1) there are no gold standard outputs to compare against, unlike held-out test sets, (2) because of their diverse training methods and formulations, inherent model properties are difficult to measure consistently, and sampled outputs are often used for evaluation instead, (3) dependence on external (pretrained) models that add another layer of bias and uncertainty, and (4) inconsistent results without a large number of samples. As a result, generative models have suffered from noisy assessments that occupy a changing evaluation landscape, in contrast to the relative stability of their discriminative counterparts. In this manuscript, we examine several important criteria for generative models and introduce evaluation metrics to address each one while discussing the aforementioned issues in generative model evaluation. In particular, we examine the challenge of measuring the perceptual realism of generated outputs and introduce a human-in-the-loop evaluation system that leverages psychophysics theory to ground the method in human perception literature and crowdsourcing techniques to construct an efficient, reliable, and consistent method for comparing different models. In addition to this, we analyze disentanglement, an increasingly important property for assessing learned representations, by measuring an intrinsic property of a generative model's data manifold using persistent homology. The final work in this manuscript takes a step towards assessing a generative model and its different modes with a key application in mind, specifically the stylistic fidelity across different generated modes in a multimodal setting.

Generative Adversarial Networks with Python

Download Generative Adversarial Networks with Python PDF Online Free

Author : Jason Brownlee
Publisher : Machine Learning Mastery
ISBN 13 :
Total Pages : 655 pages
Book Rating : 4./5 ( download)

Book Synopsis Generative Adversarial Networks with Python by : Jason Brownlee

Download or read book Generative Adversarial Networks with Python written by Jason Brownlee and published by Machine Learning Mastery. This book was released on 2019-07-11 with total page 655 pages. Available in PDF, EPUB and Kindle. Book excerpt: Step-by-step tutorials on generative adversarial networks in python for image synthesis and image translation.

Deep Generative Models

Download Deep Generative Models PDF Online Free

Author : Anirban Mukhopadhyay
Publisher : Springer Nature
ISBN 13 : 3031727444
Total Pages : 235 pages
Book Rating : 4.0/5 (317 download)

Book Synopsis Deep Generative Models by : Anirban Mukhopadhyay

Download or read book Deep Generative Models written by Anirban Mukhopadhyay and published by Springer Nature. This book was released on with total page 235 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Advances in Deep Generative Models for Medical Artificial Intelligence

Download Advances in Deep Generative Models for Medical Artificial Intelligence PDF Online Free

Author : Hazrat Ali
Publisher : Springer Nature
ISBN 13 : 3031463412
Total Pages : 259 pages
Book Rating : 4.0/5 (314 download)

Book Synopsis Advances in Deep Generative Models for Medical Artificial Intelligence by : Hazrat Ali

Download or read book Advances in Deep Generative Models for Medical Artificial Intelligence written by Hazrat Ali and published by Springer Nature. This book was released on 2023-12-16 with total page 259 pages. Available in PDF, EPUB and Kindle. Book excerpt: Generative Artificial Intelligence is rapidly advancing with many state-of-the-art performances on computer vision, speech processing, and natural language processing tasks. Generative adversarial networks and neural diffusion models can generate high-quality synthetic images of human faces, artworks, and coherent essays on different topics. Generative models are also transforming Medical Artificial Intelligence, given their potential to learn complex features from medical imaging and healthcare data. Hence, computer-aided diagnosis and healthcare are benefiting from Medical Artificial Intelligence and Generative Artificial Intelligence. This book presents the recent advances in generative models for Medical Artificial Intelligence. It covers many applications of generative models for medical image data, including volumetric medical image segmentation, data augmentation, MRI reconstruction, and modeling of spatiotemporal medical data. This book highlights the recent advancements in Generative Artificial Intelligence for medical and healthcare applications, using medical imaging and clinical and electronic health records data. Furthermore, the book comprehensively presents the concepts and applications of deep learning-based artificial intelligence methods, such as generative adversarial networks, convolutional neural networks, and vision transformers. It also presents a quantitative and qualitative analysis of data augmentation and synthesis performances of Generative Artificial Intelligence models. This book is the result of the collaborative efforts and hard work of many minds who contributed to it and illuminated the vast landscape of Medical Artificial Intelligence. The book is suitable for reading by computer science researchers, medical professionals, healthcare informatics, and medical imaging researchers interested in understanding the potential of artificial intelligence in healthcare. It serves as a compass for navigating the artificial intelligence-driven healthcare landscape.

Learning to Sample from Noise with Deep Generative Models

Download Learning to Sample from Noise with Deep Generative Models PDF Online Free

Author : Florian Bordes
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (12 download)

Book Synopsis Learning to Sample from Noise with Deep Generative Models by : Florian Bordes

Download or read book Learning to Sample from Noise with Deep Generative Models written by Florian Bordes and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine learning and specifically deep learning has made significant breakthroughs in recent years concerning different tasks. One well known application of deep learning is computer vision. Tasks such as detection or classification are nearly considered solved by the community. However, training state-of-the-art models for such tasks requires to have labels associated to the data we want to classify. A more general goal is, similarly to animal brains, to be able to design algorithms that can extract meaningful features from data that aren't labeled. Unsupervised learning is one of the axes that try to solve this problem. In this thesis, I present a new way to train a neural network as a generative model capable of generating quality samples (a task akin to imagining). I explain how by starting from noise, it is possible to get samples which are close to the training data. This iterative procedure is called Infusion training and is a novel approach to learning the transition operator of a generative Markov chain. In the first chapter, I present some background about machine learning and probabilistic models. The second chapter presents generative models that inspired this work. The third and last chapter presents and investigates our novel approach to learn a generative model with Infusion training.

De novo Molecular Design

Download De novo Molecular Design PDF Online Free

Author : Gisbert Schneider
Publisher : Wiley-VCH
ISBN 13 : 9783527334612
Total Pages : 0 pages
Book Rating : 4.3/5 (346 download)

Book Synopsis De novo Molecular Design by : Gisbert Schneider

Download or read book De novo Molecular Design written by Gisbert Schneider and published by Wiley-VCH. This book was released on 2013-12-23 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Systematically examining current methods and strategies, this ready reference covers a wide range of molecular structures, from organic-chemical drugs to peptides, Proteins and nucleic acids, in line with emerging new drug classes derived from biomacromolecules. A leader in the field and one of the pioneers of this young discipline has assembled here the most prominent experts from across the world to provide first-hand knowledge. While most of their methods and examples come from the area of pharmaceutical discovery and development, the approaches are equally applicable for chemical probes and diagnostics, pesticides, and any other molecule designed to interact with a biological system. Numerous images and screenshots illustrate the many examples and method descriptions. With its broad and balanced coverage, this will be the firststop resource not only for medicinal chemists, biochemists and biotechnologists, but equally for bioinformaticians and molecular designers for many years to come. From the content: * Reaction-driven de novo design * Adaptive methods in molecular design * Design of ligands against multitarget profiles * Free energy methods in ligand design * Fragment-based de novo design * Automated design of focused and target family-oriented compound libraries * Molecular de novo design by nature-inspired computing * 3D QSAR approaches to de novo drug design * Bioisosteres in de novo design * De novo design of peptides, proteins and nucleic acid structures, including RNA aptamers and many more.

Deep Generative Models

Download Deep Generative Models PDF Online Free

Author : Anirban Mukhopadhyay
Publisher : Springer Nature
ISBN 13 : 303153767X
Total Pages : 256 pages
Book Rating : 4.0/5 (315 download)

Book Synopsis Deep Generative Models by : Anirban Mukhopadhyay

Download or read book Deep Generative Models written by Anirban Mukhopadhyay and published by Springer Nature. This book was released on with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Generative Adversarial Networks for Image-to-Image Translation

Download Generative Adversarial Networks for Image-to-Image Translation PDF Online Free

Author : Arun Solanki
Publisher : Academic Press
ISBN 13 : 0128236132
Total Pages : 446 pages
Book Rating : 4.1/5 (282 download)

Book Synopsis Generative Adversarial Networks for Image-to-Image Translation by : Arun Solanki

Download or read book Generative Adversarial Networks for Image-to-Image Translation written by Arun Solanki and published by Academic Press. This book was released on 2021-06-22 with total page 446 pages. Available in PDF, EPUB and Kindle. Book excerpt: Generative Adversarial Networks (GAN) have started a revolution in Deep Learning, and today GAN is one of the most researched topics in Artificial Intelligence. Generative Adversarial Networks for Image-to-Image Translation provides a comprehensive overview of the GAN (Generative Adversarial Network) concept starting from the original GAN network to various GAN-based systems such as Deep Convolutional GANs (DCGANs), Conditional GANs (cGANs), StackGAN, Wasserstein GANs (WGAN), cyclical GANs, and many more. The book also provides readers with detailed real-world applications and common projects built using the GAN system with respective Python code. A typical GAN system consists of two neural networks, i.e., generator and discriminator. Both of these networks contest with each other, similar to game theory. The generator is responsible for generating quality images that should resemble ground truth, and the discriminator is accountable for identifying whether the generated image is a real image or a fake image generated by the generator. Being one of the unsupervised learning-based architectures, GAN is a preferred method in cases where labeled data is not available. GAN can generate high-quality images, images of human faces developed from several sketches, convert images from one domain to another, enhance images, combine an image with the style of another image, change the appearance of a human face image to show the effects in the progression of aging, generate images from text, and many more applications. GAN is helpful in generating output very close to the output generated by humans in a fraction of second, and it can efficiently produce high-quality music, speech, and images. - Introduces the concept of Generative Adversarial Networks (GAN), including the basics of Generative Modelling, Deep Learning, Autoencoders, and advanced topics in GAN - Demonstrates GANs for a wide variety of applications, including image generation, Big Data and data analytics, cloud computing, digital transformation, E-Commerce, and Artistic Neural Networks - Includes a wide variety of biomedical and scientific applications, including unsupervised learning, natural language processing, pattern recognition, image and video processing, and disease diagnosis - Provides a robust set of methods that will help readers to appropriately and judiciously use the suitable GANs for their applications

Deep Generative Models for Medical Images and Beyond

Download Deep Generative Models for Medical Images and Beyond PDF Online Free

Author : Yuan Xue
Publisher :
ISBN 13 :
Total Pages : pages
Book Rating : 4.:/5 (127 download)

Book Synopsis Deep Generative Models for Medical Images and Beyond by : Yuan Xue

Download or read book Deep Generative Models for Medical Images and Beyond written by Yuan Xue and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep generative models such as generative adversarial networks (GANs) or variational autoencoders (VAEs) have been extensively studied in unsupervised image generation tasks where given training data, the goal is to try and generate new samples from the same distribution. Despite such research efforts, components of such generative models, especially GANs, have rarely been integrated into supervised learning scenarios such as in classification, segmentation, and regression tasks in the literature. To improve the generality and applicability of deep generative models, in this dissertation, we show the potential of integrating components of generative models into a variety of supervised learning tasks for improved performance. We propose several advanced deep learning based generative methods that are complementary to traditional supervised learning methods, for different medical image analysis applications as well as architecture design applications which achieve state-of-the-art performances in our experiments. First, we present an image segmentation method that consists of a segmentor (i.e., generator) network and a critic (i.e., discriminator) network, which is trained in an adversarial learning fashion, so that feedback from the critic network can help the segmentor generate accurate and realistic segmentations. We also discuss other segmentation tasks, including 3D organ segmentation and infant video segmentation. Then, we present a multimodal recurrent model with attention for the automatic generation of medical reports given X-Rays images. We also propose several deep generative model architectures with structural integrity for indoor wireframe scene rendering and automated floor plan generation. Finally, we propose a series of synthetic augmentation models that generate synthetic images and then selectively choose high-quality synthetic images to augment training sets and improve histopathology image classification results.

Generative Deep Learning

Download Generative Deep Learning PDF Online Free

Author : David Foster
Publisher : "O'Reilly Media, Inc."
ISBN 13 : 109813415X
Total Pages : 456 pages
Book Rating : 4.0/5 (981 download)

Book Synopsis Generative Deep Learning by : David Foster

Download or read book Generative Deep Learning written by David Foster and published by "O'Reilly Media, Inc.". This book was released on 2022-06-28 with total page 456 pages. Available in PDF, EPUB and Kindle. Book excerpt: Generative AI is the hottest topic in tech. This practical book teaches machine learning engineers and data scientists how to use TensorFlow and Keras to create impressive generative deep learning models from scratch, including variational autoencoders (VAEs), generative adversarial networks (GANs), Transformers, normalizing flows, energy-based models, and denoising diffusion models. The book starts with the basics of deep learning and progresses to cutting-edge architectures. Through tips and tricks, you'll understand how to make your models learn more efficiently and become more creative. Discover how VAEs can change facial expressions in photos Train GANs to generate images based on your own dataset Build diffusion models to produce new varieties of flowers Train your own GPT for text generation Learn how large language models like ChatGPT are trained Explore state-of-the-art architectures such as StyleGAN2 and ViT-VQGAN Compose polyphonic music using Transformers and MuseGAN Understand how generative world models can solve reinforcement learning tasks Dive into multimodal models such as DALL.E 2, Imagen, and Stable Diffusion This book also explores the future of generative AI and how individuals and companies can proactively begin to leverage this remarkable new technology to create competitive advantage.

Variational Methods for Machine Learning with Applications to Deep Networks

Download Variational Methods for Machine Learning with Applications to Deep Networks PDF Online Free

Author : Lucas Pinheiro Cinelli
Publisher : Springer Nature
ISBN 13 : 3030706796
Total Pages : 173 pages
Book Rating : 4.0/5 (37 download)

Book Synopsis Variational Methods for Machine Learning with Applications to Deep Networks by : Lucas Pinheiro Cinelli

Download or read book Variational Methods for Machine Learning with Applications to Deep Networks written by Lucas Pinheiro Cinelli and published by Springer Nature. This book was released on 2021-05-10 with total page 173 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a straightforward look at the concepts, algorithms and advantages of Bayesian Deep Learning and Deep Generative Models. Starting from the model-based approach to Machine Learning, the authors motivate Probabilistic Graphical Models and show how Bayesian inference naturally lends itself to this framework. The authors present detailed explanations of the main modern algorithms on variational approximations for Bayesian inference in neural networks. Each algorithm of this selected set develops a distinct aspect of the theory. The book builds from the ground-up well-known deep generative models, such as Variational Autoencoder and subsequent theoretical developments. By also exposing the main issues of the algorithms together with different methods to mitigate such issues, the book supplies the necessary knowledge on generative models for the reader to handle a wide range of data types: sequential or not, continuous or not, labelled or not. The book is self-contained, promptly covering all necessary theory so that the reader does not have to search for additional information elsewhere. Offers a concise self-contained resource, covering the basic concepts to the algorithms for Bayesian Deep Learning; Presents Statistical Inference concepts, offering a set of elucidative examples, practical aspects, and pseudo-codes; Every chapter includes hands-on examples and exercises and a website features lecture slides, additional examples, and other support material.

Deep Generative Modeling

Download Deep Generative Modeling PDF Online Free

Author : Jakub M. Tomczak
Publisher : Springer Nature
ISBN 13 : 3030931587
Total Pages : 210 pages
Book Rating : 4.0/5 (39 download)

Book Synopsis Deep Generative Modeling by : Jakub M. Tomczak

Download or read book Deep Generative Modeling written by Jakub M. Tomczak and published by Springer Nature. This book was released on 2022-02-18 with total page 210 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook tackles the problem of formulating AI systems by combining probabilistic modeling and deep learning. Moreover, it goes beyond typical predictive modeling and brings together supervised learning and unsupervised learning. The resulting paradigm, called deep generative modeling, utilizes the generative perspective on perceiving the surrounding world. It assumes that each phenomenon is driven by an underlying generative process that defines a joint distribution over random variables and their stochastic interactions, i.e., how events occur and in what order. The adjective "deep" comes from the fact that the distribution is parameterized using deep neural networks. There are two distinct traits of deep generative modeling. First, the application of deep neural networks allows rich and flexible parameterization of distributions. Second, the principled manner of modeling stochastic dependencies using probability theory ensures rigorous formulation and prevents potential flaws in reasoning. Moreover, probability theory provides a unified framework where the likelihood function plays a crucial role in quantifying uncertainty and defining objective functions. Deep Generative Modeling is designed to appeal to curious students, engineers, and researchers with a modest mathematical background in undergraduate calculus, linear algebra, probability theory, and the basics in machine learning, deep learning, and programming in Python and PyTorch (or other deep learning libraries). It will appeal to students and researchers from a variety of backgrounds, including computer science, engineering, data science, physics, and bioinformatics, who wish to become familiar with deep generative modeling. To engage the reader, the book introduces fundamental concepts with specific examples and code snippets. The full code accompanying the book is available on github. The ultimate aim of the book is to outline the most important techniques in deep generative modeling and, eventually, enable readers to formulate new models and implement them.

Deep Generative Models and Applications

Download Deep Generative Models and Applications PDF Online Free

Author : Tatjana Chavdarova
Publisher :
ISBN 13 :
Total Pages : 149 pages
Book Rating : 4.:/5 (119 download)

Book Synopsis Deep Generative Models and Applications by : Tatjana Chavdarova

Download or read book Deep Generative Models and Applications written by Tatjana Chavdarova and published by . This book was released on 2020 with total page 149 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mots-clés de l'auteur: Deep Learning ; Deep Generative Models ; Generative Adversarial Networks ; Variance Reduced Gradient.