Deep Learning for Natural Language Processing - Applications of Deep Neural Networks to Machine Learning Tasks

Colleagues, the Deep Learning for Natural Language Processing  - Applications of Deep Neural Networks to Machine Learning program bring intuitive explanations of essential theory to life with interactive, hands-on Jupyter notebook demos. Examples feature Python and Keras, the high-level API for TensorFlow. Training modules include:  1) The Power and Elegance of Deep Learning for NLP -  linguistics section that introduces the elements of natural language and breaks down how these elements are represented both by Deep Learning and by traditional machine learning approaches. This is followed up with a tantalizing overview of the broad natural language applications in which Deep Learning has emerged as state-of-the-art. The lesson then reviews how to run the code in these LiveLessons on your own machine, as well as the foundational Deep Learning theory that is essential for building an NLP specialization upon. The lesson wraps up by taking a sneak peek at the capabilities you’ll develop over the course of all five lessons, 2) Word Vectors - word vectors are as well as how the beautiful word2vec algorithm creates them. Subsequently, the lesson arms you with a rich set of natural language data sets that you can train powerful Deep Learning models, and then swiftly moves along to leveraging those data to generate word vectors of your own, 3) Modeling Natural Language Data - calculate a concise and broadly useful summary metric called the Area Under the Curve of the Receiver Operator Characteristic. Calculate that summary metric in practice by building and evaluating a dense neural network for classifying documents, and add convolutional layers into your deep neural network as well, 4) Recurrent Neural Networks - essential RNN theory, a Deep Learning family that’s ideally suited to handling data that occur in a sequence like languages, apply this theory by incorporating an RNN into your document classification model, and high-level theoretical overview of especially powerful RNN variants—the Long Short-Term Memory Unit and the Gated Recurrent Unit,before incorporating these into your Deep Learning models as well, and 5) Advanced Models - LSTM, namely the Bi-Directional and Stacked varieties. - non-sequential network architectures—where instead of only stacking neural layers on top of each other as we’ve always done and run layers side-by-side in parallel.

Enroll today (eams & execs welcome):  https://tinyurl.com/yks4wu2h 

Much career success, Lawrence E. Wilson - Artificial Intelligence Academy