This is a tutorial and survey paper on Generative Adversarial Network (GAN), adversarial autoencoders, and their variants. We start with explaining adversarial learning and the vanilla GAN. Then, we explain the conditional GAN and DCGAN. The mode collapse problem is introduced and various methods, including minibatch GAN, unrolled GAN, BourGAN, mixture GAN, D2GAN, and Wasserstein GAN, are introduced for resolving this problem. Then, maximum likelihood estimation in GAN are explained along with f-GAN, adversarial variational Bayes, and Bayesian GAN. Then, we cover feature matching in GAN, InfoGAN, GRAN, LSGAN, energy-based GAN, CatGAN, MMD GAN, LapGAN, progressive GAN, triple GAN, LAG, GMAN, AdaGAN, CoGAN, inverse GAN, BiGAN, ALI, SAGAN, Few-shot GAN, SinGAN, and interpolation and evaluation of GAN. Then, we introduce some applications of GAN such as image-to-image translation (including PatchGAN, CycleGAN, DeepFaceDrawing, simulated GAN, interactive GAN), text-to-image translation (including StackGAN), and mixing image characteristics (including FineGAN and MixNMatch). Finally, we explain the autoencoders based on adversarial learning including adversarial autoencoder, PixelGAN, and implicit autoencoder.

Python based YOLO Object Detection using Pre-trained Dataset Models as well as Custom Trained Dataset Models. Python based YOLO Object Detection using Pre-trained Dataset Models as well as Custom Trained Dataset Models. This is the fourth course from my Computer Vision series. As you know Object Detection is the most used applications of Computer Vision, in which the computer will be able to recognize and classify objects inside an image. This course is equally divided into two halves.

Thanks to advancements in speech and natural language processing, there is hope that one day you may be able to ask your virtual assistant what the best salad ingredients are. Currently, it is possible to ask your home gadget to play music, or open on voice command, which is a feature already found in some many devices. If you speak Moroccan, Algerian, Egyptian, Sudanese, or any of the other dialects of the Arabic language, which are immensely varied from region to region, where some of them are mutually unintelligible, it is a different story. If your native tongue is Arabic, Finnish, Mongolian, Navajo, or any other language with high level of morphological complexity, you may feel left out. These complex constructs intrigued Ahmed Ali to find a solution.

Are you ready to master Machine Learning techniques and Kick-off your career as a Data Scientist?! You came to the right place! Machine Learning skill is one of the top skills to acquire in 2019 with an average salary of over $114,000 in the United States according to PayScale! The total number of ML jobs over the past two years has grown around 600 percent and expected to grow even more by 2020. In this course, we are going to provide students with knowledge of key aspects of state-of-the-art classification techniques.

An adjunction is a pair of functors related by a pair of natural transformations, and relating a pair of categories. It displays how a structure, or a concept, projects from each category to the other, and back. Adjunctions are the common denominator of Galois connections, representation theories, spectra, and generalized quantifiers. We call an adjunction nuclear when its categories determine each other. We show that every adjunction can be resolved into a nuclear adjunction. This resolution is idempotent in a strong sense. The nucleus of an adjunction displays its conceptual core, just as the singular value decomposition of an adjoint pair of linear operators displays their canonical bases. The two composites of an adjoint pair of functors induce a monad and a comonad. Monads and comonads generalize the closure and the interior operators from topology, or modalities from logic, while providing a saturated view of algebraic structures and compositions on one side, and of coalgebraic dynamics and decompositions on the other. They are resolved back into adjunctions over the induced categories of algebras and of coalgebras. The nucleus of an adjunction is an adjunction between the induced categories of algebras and coalgebras. It provides new presentations for both, revealing the meaning of constructing algebras for a comonad and coalgebras for a monad. In his seminal early work, Ross Street described an adjunction between monads and comonads in 2-categories. Lifting the nucleus construction, we show that the resulting Street monad on monads is strongly idempotent, and extracts the nucleus of a monad. A dual treatment achieves the same for comonads. Applying a notable fragment of pure 2-category theory on an acute practical problem of data analysis thus led to new theoretical result.

Learn how embedded machine learning can reduce costs and improve efficiency in this Webinar hosted by Avnet and Microchip Technology. Discover the benefits of machine learning for applications such as smart embedded vision, smart predictive maintenance, smart human machine interfaces and more. Thanks to modern lightweight machine learning models, you can now implement these capabilities into microcontroller-based devices.

In this section, we will introduce the deep learning framework we'll be using through this course, which is PyTorch. We will show you how to install it, how it works and why it's special, and then we will code some PyTorch tensors and show you some operations on tensors, as well as show you Autograd in code!

End-to-End Data Science and Machine Learning (Right from learning the basics to building the models to deploying the models) Learn Through More Than 20 Projects and Assignments Master Machine Learning With Python Learn How to Deploy Machine Learning Models Practical Hands-On Data Science Projects Mastery Build And Deploy Machine Learning models On Flask, Heroku, Streamlit, AWS,Google Cloud, Microsoft Azure Create Robust Machine Learning Models with CatBoost,XGBoost, LightGbm Learn Different Machine Learning Algorithms such as Linear And Logistic regression, Naive Bayes,KNN,SVM,K-means, etc. Deal With Data Imbalance (Upsampling/Downsampling/SMOTE) Gain Confidence In Performing Exploratory Data Analysis (EDA) Choose The Right Machine Learning Model For Your Problem Statement Access To Exclusive Community To Learn With Others And Answer Your Queries Learn The Necessary Statistics Master Data Analysis This course is a beginner to advance level course with all the tutorials on the lessons covered in the projects included If you are a complete beginner, you have all the lessons from introduction to python to building projects and deployment. If you already have have the basics, we have more than 20 projects and deployment for you to practice. If you are a complete beginner, you have all the lessons from introduction to python to building projects and deployment. If you already have have the basics, we have more than 20 projects and deployment for you to practice. Then this course is for you!!

Deep Learning: Advanced Computer Vision (GANs, SSD, +More!), VGG, ResNet, Inception, SSD, Neural Style Transfer, GANs +More Using CNNs in Tensorflow, Keras, and Python Created by Lazy Programmer Inc. Preview this Course  - GET COUPON CODE 100% Off Udemy Coupon . Free Udemy Courses . Online Classes

The Black Friday Udemy sale begins. Shop to save on thousands of online courses. This Course is complete Guide to both supervised & unsupervised learning using python.This means,this course covers all the main aspects of practical data science and if you take this course,you can do away with taking other courses or buying books on python based data science. In this age of big data,companies across the globe use python to sift through the avalanche of information at their disposal.. By becoming proficient in unsupervised & supervised learning in python,you can give your company a competitive edge and boost your career to the next level.