Natural Language Processing, or NLP for short, is the study of computational methods for working with speech and text data. The field is dominated by the statistical paradigm and machine learning methods are used for developing predictive models. In this post, you will discover the top books that you can read to get started with natural language processing. As practitioners, we do not always have to grab for a textbook when getting started on a new topic. Code examples in the book are in the Python programming language.

AWS offers a family of intelligent services that provide cloud-native machine learning and deep learning technologies to address your different use cases and needs. For developers looking to add managed AI services to their applications, AWS brings natural language understanding (NLU) and automatic speech recognition (ASR) with Amazon Lex, visual search and image recognition with Amazon Rekognition, text-to-speech (TTS) with Amazon Polly, and developer-focused machine learning with Amazon Machine Learning. For more in-depth deep learning applications, the AWS Deep Learning AMI lets you run deep learning in the cloud, at any scale. Launch instances of the AMI, pre-installed with open source deep learning engines (Apache MXNet, TensorFlow, Caffe, Theano, Torch and Keras), to train sophisticated, custom AI models, experiment with new algorithms, and learn new deep learning skills and techniques; all backed by auto-scaling clusters of GPU-based instances. Whether you're just getting started with AI or you're a deep learning expert, this session will provide a meaningful overview of how to improve scale and efficiency with the AWS Cloud.

Some in-video questions and practice quizzes will help you to review the material, with no effect on the final grade. A mandatory peer-graded assignment is also present, for weeks from 1 to 9, and it will expand on the lectures' topics, letting you reach a deeper understanding. The nine peer-graded assignments will make up for 50% of the grade, while the other half will come from a final exam, after the last lecture. In the tutorial we will use the 3x3 pebble game to understand the essential concepts of Monte Carlo techniques (detailed balance, irreducibility, and a-periodicity), and meet the celebrated Metropolis algorithm. Finally, the homework session will let you understand some useful aspects of Markov-chain Monte Carlo, related to convergence and error estimations.

Four members of our research team spent the past week at the Conference on Empirical Methods in Natural Language Processing (EMNLP 2017) in Copenhagen, Denmark. The conference handbook can be found here and the proceedings can be found here. The program consisted of two days of workshops and tutorials and three days of main conference. Videos of the conference talks and presentations can be found here.The conference was superbly organized, had a great venue, and a social event with fireworks. With 225 long papers, 107 papers, and 9 TACL papers accepted, there was a clear uptick of submissions compared to last year.

Today's blog post was inspired by PyImageSearch reader, Emmanuel. Emmanuel emailed me after last week's tutorial on object detection with deep learning OpenCV and asked: I really enjoyed last week's blog post on object detection with deep learning and OpenCV, thanks for putting it together and for making deep learning with OpenCV so accessible. I want to apply the same technique to real-time video. What is the best way to do this? How can I achieve the most efficiency?

This is the 3rd part in my Data Science and Machine Learning series on Deep Learning in Python. At this point, you already know a lot about neural networks and deep learning, including not just the basics like backpropagation, but how to improve it using modern techniques like momentum and adaptive learning rates. You've already written deep neural networks in Theano and TensorFlow, and you know how to run code using the GPU. This course is all about how to use deep learning for computer vision using convolutional neural networks. These are the state of the art when it comes to image classification and they beat vanilla deep networks at tasks like MNIST.

Everyone wants to minimize losses and maximize profits. AI and Deep Learning are transforming the way we understand software, making computers more intelligent than we could even imagine just a decade ago. Thanks to Deep Learning and improved methodologies to analyze data, Data Analysts and Data Scientists are increasingly using data to make informed decisions. Deep Learning algorithms are being used across a broad range of industries – as the fundamental driver of AI, being able to tackle Deep Learning is going to a vital and valuable skill not only within the tech world but also for the wider global economy that depends upon knowledge and insight for growth and success. It's something that's moving beyond the realm of data science – if you're a developer, this course gives you a great opportunity to expand your skillset.

Artificial intelligence is growing exponentially. There is no doubt about that. Self-driving cars are clocking up millions of miles, IBM Watson is diagnosing patients better than armies of doctors and Google Deepmind's AlphaGo beat the World champion at Go - a game where intuition plays a key role. But the further AI advances, the more complex become the problems it needs to solve. And only Deep Learning can solve such complex problems and that's why it's at the heart of Artificial intelligence.

Nothing reveals as much about a society, and its future, as its high schools. Yet amid accelerating change -- widening inequality, unprecedented globalization and technological advances -- they've woefully lagged behind. There are, of course, exceptions. Follow OZY's special series High School, Disrupted to find out about the global leaders, cutting-edge trends and big ideas reimagining secondary education -- for the better. From Siri handling our schedules to smart cars driving themselves, artificial intelligence (AI) has turned our world upside down -- except in education.