Intermediate
122 Lessons
15h
Certificate of Completion
Deep learning has revolutionized NLP and problems that require a large amount of work in terms of new features. Tuning can now be efficiently solved using NLP.
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This course includes
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Course Overview
Deep learning has revolutionized natural language processing (NLP) and NLP problems that require a large amount of work in terms of designing new features. Tuning models can now be efficiently solved using NLP. In this course, you will learn the fundamentals of TensorFlow and Keras, which is a Python-based interface for TensorFlow. Next, you will build embeddings and other vector representations, including the skip-gram model, continuous bag-of-words, and Global Vector representations. You will then learn ...
What You'll Learn
Familiarity with word embeddings, including the skip-gram model, continuous bag-of-words (CBOW), and Global Vector representations (GloVe)
Understanding of deep models like CNNs, RNNs, LSTMs, and GRUs
Hands-on experience building NLP tools using TensorFlow, Keras, and Hugging Face libraries
Working knowledge of transformers and their variants in sequence-to-sequence modeling
Course Content
Introduction to Natural Language Processing
Understanding TensorFlow 2
Word2vec: Learning Word Embeddings
Advanced Word Vector Algorithms
Sentence Classification with Convolutional Neural Networks
Recurrent Neural Networks
10 Lessons
Understanding Long Short-Term Memory Networks
7 Lessons
Applications of LSTM: Generating Text
9 Lessons
Sequence-to-Sequence Learning: Neural Machine Translation
13 Lessons
Transformers
10 Lessons
Image Captioning with Transformers
10 Lessons
Final Remarks
1 Lesson
Appendix: Mathematical Foundations and Advanced TensorFlow
6 Lessons
How You'll Learn
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