New Tutorial: Neural Machine Translation

This week you'll learn about Neural Machine Translation. Every day millions of articles are posted on the internet from all over the world. Sometimes they are in English, sometimes in a different language, yet all of it is accessible to anyone sitting at any corner of this planet. How? How does the internet transcend geographic and linguistic boundaries to deliver information (and also memes) to anyone, anywhere? The big picture: Translation, especially automatic translation powered by artificial neural networks, plays a huge role in this process. The task of translating between two languages using an Artificial Neural Network is known as Neural Machine Translation (NMT). How it works: Neural Machine Translation models consist of encoders and decoders. The encoder encodes the source language, while the decoder decodes it into the target language. Our thoughts: NMT is fairly easy to code, but this tutorial helps you build an intuition for it. The math behind the task is ingenious; once you tap into that, coding the algorithm will feel more organic. Yes, but: We just don't stop with the intuitions here. Our next blog post will show you how to code an NMT in TensorFlow and Keras. Stay smart: Strong foundations lead to a strong building. Having a grip on the intuitions for NMT will help you in the journey of attention and transformers :). Click here to read the full tutorial Solve Your CV/DL problem this week (or weekend) with our Working Code You can instantly access all of the code for Neural Machine Translation by joining PyImageSearch University. Get working code to Finish your project this weekend with our code Solve your thorniest coding problems at work this week to show off your expertise Publish groundbreaking research without multiple tries at coding the hard parts Guaranteed Results: If you haven't accomplished your CV/DL goals, let us know within 30 days and get a full refund. I want the code Note: You may have missed this, but last Wednesday, we published a new post on Computer Vision and Deep Learning for Agriculture.   In Case You Missed It We were live last Friday.  Did you miss it?   No problem, watch the replay and learn NMT Your PyImageSearch Team To ensure you keep getting these emails, please add adrian@pyimagesearch.com and success@pyimagesearch.com to your address book or allowlist us. Want out of the loop? Unsubscribe. Our postal address: 317 Commercial St. NE, Ste A, PMB 530, Albuquerque, NM 87102