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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. In this new highly-focused course - written by developers, for developers – we’ll cut 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.

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Unlimited Duration

Last Updated

March 4, 2021

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This “skills-centric” 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.In this course you learn about:· 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 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 Curriculum

    • Natural Language Processing 00:00:00
    • Deep Learning 00:00:00
    • Promise of Deep Learning for Natural Language 00:00:00
    • How to Clean Text Manually and with NLTK 00:00:00
    • How to Prepare Text Data with scikit-learn 00:00:00
    • How to Prepare Text Data with Keras 00:00:00
    • Bag-of-Words 00:00:00
    • The Bag-of-Words Model 00:00:00
    • Prepare Movie Review Data for Sentiment Analysis 00:00:00
    • Neural Bag-of-Words Model for Sentiment Analysis 00:00:00
    • The Word Embedding Model 00:00:00
    • How to Develop Word Embeddings with Gensim 00:00:00
    • How to Learn and Load Word Embeddings in Keras 00:00:00
    • Neural Models for Document Classification 00:00:00
    • Develop an Embedding + CNN Model 00:00:00
    • Develop an n-gram CNN Model for Sentiment Analysis 00:00:00
    • Neural Language Modeling 00:00:00
    • Develop a Character-Based Neural Language Model 00:00:00
    • How to Develop a Word-Based Neural Language Model 00:00:00
    • Develop a Neural Language Model for Text Generation 00:00:00
    • Neural Image Caption Generation 00:00:00
    • Neural Network Models for Caption Generation 00:00:00
    • Load and Use a Pre-Trained Object Recognition Model 00:00:00
    • How to Evaluate Generated Text with the BLEU Score 00:00:00
    • How to Prepare a Photo Caption Dataset For Modeling 00:00:00
    • Develop a Neural Image Caption Generation Model 00:00:00
    • Neural Machine Translation 00:00:00
    • Encoder-Decoder Models for NMT 00:00:00
    • Configure Encoder-Decoder Models for NMT 00:00:00
    • How to Develop a Neural Machine Translation Model 00:00:00

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