Link: Natural Language Processing(NLP) with Deep Learning in Keras Natural Language Processing (NLP) is a hot topic into Machine Learning field. This course is an advanced course of NLP using Deep Learning approach. BESTSELLER 4.1 (44 ratings) 418 students enrolled Created by CARLOS QUIROS What you'll learn Upgrade the knowledge of Natural Language Processing using Deep Learning models Requirements Machine Learning, NLP basics, Linear Algebra, Python, Tensor Flow, Keras Description Natural Language Processing (NLP) is a hot topic into Machine Learning field. This course is an advanced course of NLP using Deep Learning approach. Before starting this course please read the guidelines of the lesson 2 to have the best experience in this course.

Overview of how Python is used for Data Science and Data Analytics projects.

Zhou Yi was terrible at math. He risked never getting into college. Then a company called Squirrel AI came to his middle school in Hangzhou, China, promising personalized tutoring. He had tried tutoring services before, but this one was different: instead of a human teacher, an AI algorithm would curate his lessons. The 13-year-old decided to give it a try. By the end of the semester, his test scores had risen from 50% to 62.5%. Two years later, he scored an 85% on his final middle school exam. "I used to think math was terrifying," he says. "But through tutoring, I realized it really isn't that hard. It helped me take the first step down a different path."

GANs are the new hottest topic in the ML arena; however, they present a challenge for the researchers and the engineers alike. Their design, and most importantly, the code implementation has been causing headaches to the ML practitioners, especially when moving to production. The workshop aims at providing a complete understanding of both the theory and the practical know-how to code and deploy this family of models in production. By the end of it, the attendees should be able to apply the concepts learned to other models without any issues. We will be showcasing all the shiny new APIs introduced by TensorFlow 2.0 by showing how to build a GAN from scratch and how to "productionize" it by leveraging the AshPy Python package that allows to easily design, prototype, train and export Machine Learning models defined in TensorFlow 2.0.

Not the Terminator kind that will take over the world. But definitely, the kind that are taking over our jobs! We live in an automated world, where our Uber rides are booked by Alexa and Siri gives us driving directions. PwC estimates that 30% of jobs will be impacted by automation by 2030. You and I may become irrelevant.

In this tutorial, you will learn how to build a scalable image hashing search engine using OpenCV, Python, and VP-Trees. Back in 2017, I wrote a tutorial on image hashing with OpenCV and Python (which is required reading for this tutorial). That guide showed you how to find identical/duplicate images in a given dataset. However, there was a scalability problem with that original tutorial -- namely that it did not scale! To find near-duplicate images, our original image hashing method would require us to perform a linear search, comparing the query hash to each individual image hash in our dataset. In a practical, real-world application that's far too slow -- we need to find a way to reduce that search to sub-linear time complexity. But how can we reduce search time so dramatically?

Generative adversarial networks, or GANs for short, are an effective deep learning approach for developing generative models. Unlike other deep learning neural network models that are trained with a loss function until convergence, a GAN generator model is trained using a second model called a discriminator that learns to classify images as real or generated. Both the generator and discriminator model are trained together to maintain an equilibrium. As such, there is no objective loss function used to train the GAN generator models and no way to objectively assess the progress of the training and the relative or absolute quality of the model from loss alone. Instead, a suite of qualitative and quantitative techniques have been developed to assess the performance of a GAN model based on the quality and diversity of the generated synthetic images.

Hello learners, Welcome to MAKERDEMY's "Learn Alexa the Fun Way" course. If you are looking for that one course that will help you gain the confidence to build and publish Amazon Alexa skills, you have come to the right place. With numerous custom made illustrations and animations, we have set the standard in terms of production quality. All so that you can have a terrific learning experience. This course is meant for anyone who wants to build many fun-filled Alexa skills and publish them for the whole world to use.

Computational Intelligence (CI) paradigms have become a key factor in the resurgence of Artificial Intelligence which is now part of the daily life. Therefore, basic and applied CI research have substantially grown and more spaces for discussion on these topics are required. The aim of this workshop is to put together researchers, practitioners, students and those interested in presenting novel findings and applications related to computational intelligence techniques. The workshop also looks to be a mean to establish possible collaborations among the attendants. Interested authors are invited to submit original papers including novel CI research and findings.

Understanding how Artificial Intelligence (AI) applies to real-life business challenges can be elusive. The power of AI lies in its ability to glean relevant insights from huge datasets, such as call data, that just a few years ago, would have been impossible to process.