It's hard to believe it's been been over a year since I released my first course on Deep Learning with NLP (natural language processing). A lot of cool stuff has happened since then, and I've been deep in the trenches learning, researching, and accumulating the best and most useful ideas to bring them back to you. So what is this course all about, and how have things changed since then? In previous courses, you learned about some of the fundamental building blocks of Deep NLP. We looked at RNNs (recurrent neural networks), CNNs (convolutional neural networks), and word embedding algorithms such as word2vec and GloVe.

The last few years have seen artificial intelligence (AI) as an ever-evolving and rapidly growing space. It has been vastly adopted across sectors and domains not just to study and analyse data or find hidden patterns but to also make meaningful real-life decisions. The global AI market was worth $35.92 billion in 2020. And according to Fortune Business Insights, the market is expected to grow at a CAGR of 33.6 per cent between 2020 and 2028, to reach a valuation of $360.36 billion in 2028. India itself is expected to invest $1 billion in the AI space by 2023.

The Black Friday Udemy sale begins. Shop to save on thousands of online courses. Welcome to the Course Introduction to Deep Learning with TensorFlow 2.0: In this course, you will learn advanced linear regression technique process and with this, you can be able to build any regression problem. Using this you can solve real-world problems like customer lifetime value, predictive analytics, etc. All the above-mentioned techniques are explained in TensorFlow.

IBM Artificial Intelligence Professional Certificate Program is designed to help those with knowledge and experience in creating AI applications, creating chatbots, and deploying AI-driven applications on the web. Our Applied AI Professional Certificate is designed to provide learners with a thorough knowledge of artificial intelligence (AI). By the end of this program, you will be familiar with the concepts of machine learning, deep learning, and neural networks. This program will help you develop the skills and knowledge in one of the hottest, in-demand sectors in technology today: artificial intelligence (AI). The program consists of six hands-on, comprehensive courses that prepare students to put theory into practice in actual development projects.

In recent years, we've seen a resurgence in AI, or artificial intelligence, and machine learning. Machine learning has led to some amazing results, like being able to analyze medical images and predict diseases on-par with human experts. Google's AlphaGo program was able to beat a world champion in the strategy game go using deep reinforcement learning. Machine learning is even being used to program self driving cars, which is going to change the automotive industry forever. Imagine a world with drastically reduced car accidents, simply by removing the element of human error.

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.