New computational algorithms make it possible to build neural networks with many input nodes and many layers, and distinguish "deep learning" of these networks from previous work on artificial neural nets.
Recommender Systems and Deep Learning in Python 4.6 (1,635 ratings) Course Ratings are calculated from individual students' ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately. What do I mean by "recommender systems", and why are they useful? Let's look at the top 3 websites on the Internet, according to Alexa: Google, YouTube, and Facebook. Recommender systems form the very foundation of these technologies. They are why Google is the most successful technology company today.
Natural language processing (NLP) technologies are widely deployed to process rich natural language text data for search and recommender systems. Achieving high-quality search and recommendation results requires that information, such as user queries and documents, be processed and understood in an efficient and effective manner. In recent years, the rapid development of deep learning models has been proven successful for improving various NLP tasks, indicating the vast potential for further improving the accuracy of search and recommender systems. Deep learning-based NLP technologies like BERT (Bidirectional Encoder Representations from Transformers) have recently made headlines for showing significant improvements in areas such as semantic understanding when contrasted with prior NLP techniques. However, exploiting the power of BERT in search and recommender systems is a non-trivial task, due to the heavy computation cost of BERT models. In this blog post, we will introduce DeText, a state-of-the-art open source NLP framework for text understanding.
Half of this crazy year is behind us and summer is here. Over the years, we machine learning engineers at Ximilar have gathered a lot of interesting ML/AI material from which we draw. I have chosen the best ones from podcasts to online courses that I recommend to listen to, read, and check. Some of them are introductory, others more advanced. However, all of them are high-quality ones made by the best people in the field and they are worth checking.
Created by Lazy Programmer Inc. English [Auto-generated], Portuguese [Auto-generated], 1 more Created by Lazy Programmer Inc. This course is a lead-in to deep learning and neural networks - it covers a popular and fundamental technique used in machine learning, data science and statistics: logistic regression. We cover the theory from the ground up: derivation of the solution, and applications to real-world problems. We show you how one might code their own logistic regression module in Python. This course does not require any external materials.
Hi All - This event was originally going to be held during GDC week back in March but had to be postponed. Excited to be hosting this event virtually during GDC Summer on Aug 4th. Games have always been at the forefront of AI & they serve as a good testing bed for AI before we put it to use in the real world. Therefore, its natural to look into gaming to peek into new techniques being discovered in AI. What started with self-learning AI in games has now translated into solving real-world problems in computer vision, natural language processing, & self-driving cars.
Let me begin this article with a question -- Which of the following sentence makes sense? Its obvious that the second one makes sense as the sequence of the sentence is preserved. So, whenever the sequence is important we use RNN. RNNs in general and LSTMs, in particular, have received the most success when working with sequences of words and paragraphs, generally called natural language processing. Some of the famous technologies using RNN are Google Assistance, Google Translate, Stock Prediction, Image Captioning, and similarly many more.
Real-time motion prediction of a vessel or a floating platform can help to improve the performance of motion compensation systems. It can also provide useful early-warning information for offshore operations that are critical with regard to motion. In this study, a long short-term memory (LSTM) -based machine learning model was developed to predict heave and surge motions of a semi-submersible. The training and test data came from a model test carried out in the deep-water ocean basin, at Shanghai Jiao Tong University, China. The motion and measured waves were fed into LSTM cells and then went through serval fully connected (FC) layers to obtain the prediction.