Deep Learning for Natural Language Processing

Colleagues, the Deep Learning for Natural Language Processing program equips you to build natural language models with deep learning. These lessons 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 2, the most popular Deep Learning library. In early lessons, specifics of working with natural language data are covered, including how to convert natural language into numerical representations that can be readily processed by machine learning approaches. In later lessons, state-of-the art Deep Learning architectures are leveraged to make predictions with natural language data. Skill-based lessons include: 1) The Power and Elegance of Deep Learning for NLP - how it has been revolutionized in recent years by Deep Learning approaches, run the code, and theory that is essential for building an NLP specialization upon; 2) Word Vectors - linguistics section that introduces computational representations of natural language elements and an illustrating what word vectors are as well as how the beautiful word2vec algorithm creates them; 3) Modeling Natural Language Data - vector-space embeddings and creating word vectors with word2vec. Whiteboard how to calculate a concise and broadly useful summary metric called the Area Under the Curve of the Receiver Operator Characteristic then calculate that summary metric in practice by building and evaluating a dense neural network for classifying documents. The lesson then goes a step further by showing you how to add convolutional layers into your deep neural network as well; 4) Recurrent Neural Networks - theory, apply this theory by incorporating an RNN into your document classification model. Jon then provides a high-level theoretical overview of especially powerful RNN variants--the Long Short-Term Memory Unit and the Gated Recurrent Unit--then incorporate these variants into your deep learning models; and 5) Advanced Models - LSTM  special cases, namely the Bi-Directional and Stacked varieties plus data sets that you can use to train powerful Deep Learning models and other advanced approaches, including sequence generation, seq2seq models, attention, transfer learning, non-sequential network architectures, and financial time series applications.

Enroll today (eams & execs welcome): https://tinyurl.com/4dp57tck 

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