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This book is my attempt to provide a brief but comprehensive introduction to graph representation learning, including methods for embedding graph data, graph neural networks, and deep generative models of graphs.
As you can see the book packs a block with the explanation of the main concepts behind generative deep learning (deep learning, VAEs, etc..) and another block with explanations and examples of the areas where it is widely used.
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.
Data-driven design approaches based on deep learning have been introduced in nanophotonics to reduce time-consuming iterative simulations, which have been a major challenge. Here, we report the first use of conditional deep convolutional generative adversarial networks to design nanophotonic antennae that are not constrained to predefined shapes. For given input reflection spectra, the network generates desirable designs in the form of images; this allows suggestions of new structures that cannot be represented by structural parameters. Simulation results obtained from the generated designs agree well with the input reflection spectrum. This method opens new avenues toward the development of nanophotonics by providing a fast and convenient approach to the design of complex nanophotonic structures that have desired optical properties.
In this article, we provide the first use of a conditional deep convolutional generative adversarial network (cDCGAN)  to design nanophotonic structures. cDCGAN is a recently developed algorithm to solve the instability problem of GAN, and provides a very stable Nash equilibrium solution. The generated designs are presented as images, so they provide essentially any arbitrary possible design for the desired optical properties that are not limited to specific structures. Our research provides designs of a 64×64 pixel probability distribution function (PDF) in a domain size of 500 nm×500 nm, which allows 264×64 degrees of freedom of design.
We optimized ρ to make the GN generate high-quality realistic designs. For a low ρ, a competition effect cannot be expected, whereas a high ρ can cause confusion in the learning process. Therefore, an appropriate value of ρ=0.5 was chosen to maximize the ability of GN to produce convincing structural designs. During each training step, the network is trained to optimize the weights to describe the mapping between the input spectrum and the PDF (see Supporting Information for details about deep-learning procedure and network optimization).
This book is a legend among all the books on deep learning. The book not just only tells about the concepts of deep learning, it first brushes your knowledge and concepts of applied maths(Linear Algebra, Probability and Information Theory, Numerical Computations) and machine learning basics in terms of maths (The lowest building blocks of A.I.).
Saving the best full package resource for the end. This book is definitely among my top-3 favourite books, absolutely beautiful not just in terms of deep learning but all other very important factors that are related to deep learning in practice like Model to production, Data ethics and Your deep learning journey (a map to follow). These three things are really really important if you are hoping to become a deep learning engineer or something even remotely similar to it in practice.
A report (of about 4 to 8 pages) presenting the project and the obtained results (for applicative projects), to be given by December 19th, 2018 on Studium. The report has to be written in such a way that any student who has followed the class can understand (no need to introduce graphical model concepts). The report has to clearly present (in French or English) the studied problem and the existing approaches. You will be more evaluated on the clarity of the report rather than on its length. To train you to write professional research papers, you should use LaTeX in the ICML 2016 template format (download the template here). You may use appendices for additional details beyond 8 pages if you want, but be aware that as in standard conference reviewing, I might only read the first 8 pages (so the main content has to be there), and also, succinctness is more valued here than length! 2b1af7f3a8