Files Included : [200 WebRips (MP4) + Project Files (PDF, TXT, JPG, PY)] Torrent Conatins : 1,199 Files, 70 Folders Course Source : https://eu.udacity.com/course/natural-language-processing-nanodegree--nd892
About :
Master the skills to get computers to understand, process, and manipulate human language. Build models on real data, and get hands-on experience with sentiment analysis, machine translation, and more.
Content :
Part 01 : Introduction to Natural Language Processing Module 01: Intro to NLP Lesson 01: Intro to Natural Language Processing Find out how Natural Language Processing is being used in the industry, why it is challenging, and learn to design an NLP solution using IBM Watson's cloud-based services.
Lesson 02: Bookworm Learn how to build a simple question-answering agent using IBM Watson.
Part 02 : Introduction to Deep Learning Module 01: Intro to Deep Learning Lesson 01: Deep Neural Networks Luis will give you solid foundations on Deep Learning, and teach you how to apply Neural Networks to analyze real data!
Module 02: Convolutional Neural Networks Lesson 01: Convolutional Neural Networks Module 03: TensorFlow Lesson 01: Intro to TensorFlow In this section you'll get a hands-on introduction to deep learning and Tensorflow, Google's deep learning framework, and you'll be able to apply it on an image dataset.
Module 04: Intro to Recurrent Networks Lesson 01: Recurrent Neural Networks Jeremy explains Recurrent Neural Networks, and their cutting edge applications to text-based sequence generation
Lesson 02: Long Short-Term Memory Networks (LSTM) Luis explains Long Short-Term Memory Networks (LSTM), and similar architectures which have the benefits of preserving long term memory.
Lesson 03: Implementing RNNs and LSTMs In this lesson, Mat will review the concepts of RNNs and LSTMs, and then you'll see how a character-wise recurrent network is implemented in TensorFlow.
Lesson 04: Hyperparameters In this section, Jay will teach you about some important hyperparameters used for our deep learning work, including those used for Recurrent Neural Networks.
Lesson 05: Sentiment Prediction with RNN In this lesson you'll implement a sentiment prediction RNN
Part 03 : NLP Fundamentals Module 01: NLP Fundamentals Lesson 01: Natural Language Processing An overview of how to build an end-to-end Natural Language Processing pipeline.
Lesson 02: Text Processing Learn to prepare text obtained from different sources for further processing, by cleaning, normalizing and splitting it into individual words or tokens.
Part 04 : Feature Extraction Module 01: Feature Extraction Lesson 01: Feature Extraction Transform text using methods like Bag-of-Words, TF-IDF, Word2Vec and GloVe to extract features that you can use in machine learning models.
Part 05 : Modeling Module 01: Modeling in NLP Lesson 01: Modeling A selection of different NLP tasks and how to build models that accomplish them.
Module 02: Project: Machine Translation Lesson 01: Machine Translation Apply the skills you've learnt in Natural Language Processing to the challenging and extremely rewarding task of Machine Translation. Bonne chance!
Project Description - Machine Translation
Project Rubric - Machine Translation
Part 06 (Elective): NLP Supplementary Module 01: NLP: Supplementary Lesson 01: Embeddings and Word2Vec In this lesson, you'll learn about embeddings in neural networks by implementing the word2vec model.
Lesson 02: Sequence to Sequence Here you'll learn about a specific architecture of RNNs for generating one sequence from another sequence. These RNNs are useful for chatbots, machine translation, and more!
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