Deep learning methods are achieving state-of-the-art results on challenging machine learning problems, such as describing photos and translating text from one language to another. Introduction to Natural Language Processing (NLP) is a highly-focused, hands-on deep learning course - written by developers, for developers – that cuts through the excess math, research papers and patchwork descriptions about natural language processing to deep dive into the technology in a meaningful, practical way to gain real world skills to leverage on the job right after the training ends. Working in a hands-on learning environment led by our expert Deep Learning practitioner, using clear explanations and standard Python libraries, students will explore step-by-step what natural language processing is, the promise of deep learning in the field, how to clean and prepare text data for modeling and how to develop deep learning models for your own natural language processing projects.

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Learning Objectives

This course is about 50% hands-on lab and 50% lecture, with extensive practical exercises designed to reinforce fundamental skills, concepts and best practices taught throughout the course. Our engaging instructors and mentors are highly-experienced practitioners who bring years of current, modern "on-the-job" modern deep learning experience into every classroom and hands-on project. In this course Students will explore:
Hands-on Projects
Neural Text Classification. Develop a deep learning model to classify the sentiment of movie reviews as either positive or negative.
Neural Language Modeling. Develop a neural language model on the text of Plato in order to generate new tracts of text with the same style and flavor as the original.
Neural Photo Captioning. Develop a model to automatically generate a concise description of ad hoc photographs.
Neural Machine Translation. Develop a model to translate sentences of text in German to English.
Neural Network Models
Neural Bag-of-Words. Develop neural network models that model text as a bag-of-words where word order is ignored.
Neural Word Embedding. Develop neural network models that model text using a distributed representation.
Embedding + CNN. Develop deep learning models that combine word embedding representations with convolutional neural networks.
Encoder-Decoder RNN. Develop recurrent neural networks that use the encoder-decoder architecture.

Course Info

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Cost: $ 2,495

Length: 4.0 days (32 hours)

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