Udemy Deep Learning Course: Advanced NLP and RNNs

Udemy Deep Learning Course: Advanced NLP and RNNs

In this course, you’ll learn about advanced NLP techniques, including how to build recurrent neural networks (RNNs) for text data.

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Introduction to Udemy’s Deep Learning Course: Advanced NLP and RNNs

This course is designed to give you a solid understanding of advanced natural language processing (NLP) and recurrent neural networks (RNNs). You’ll learn how to build models for text classification, machine translation, Named Entity Recognition (NER), and sentiment analysis. You’ll also discover how to train your models on massive datasets using GPUs, and deploy them in production using TensorFlow Serving.

Why you should take this course

This course is designed to give you a comprehensive understanding of advanced natural language processing techniques using recurrent neural networks. You will learn how to build powerful models that can handle long-range dependencies and produce state-of-the-art results on a variety of tasks.

The course starts with an overview of the fundamental concepts in natural language processing, including tokenization, word embeddings, and sequence modeling. We then dive into more advanced topics, such as building neural machine translation models and modeling relationships between entities in text. Finally, we’ll discuss recent advances in NLP and recurrent neural networks, such as memory networks and sequence-to-sequence models.

By the end of this course, you will have a solid understanding of advanced NLP techniques and how to apply them to real-world tasks.

What you will learn in this course

In this course you will learn about advanced NLP topics such as word embeddings, sentencepiece tokenization, and how to use RNNs and LSTMs for modeling text data. This course is taught by Sebastian Ruder, who is a research scientist at Google Brain.

The instructor of this course

The instructor of this course is Sebastian Raschka, who is a full stack deep learning engineer. Sebastian holds a PhD in machine learning and has been working on developing machine learning & deep learning solutions for industry since 2012.

Course Outline

I. Introduction to NLP
A. What is NLP?
B. The Benefits of NLP
C. The Different Fields of NLP
D. The Different Applications of NLP
E. The Evolution of NLP
F. The Fundamentals of Language
G. The Structure of Language
H. Parts of Speech Basics
I. Syntax Basics
J. Semantics Basics
K. Pragmatics Basics
L. Discourse Analysis Basics
M. Statistical Methods in NLP
N. Textual Inference Methods in NLP
O. Text Mining Methods in NLP
PQ) Overview of the Course
QR) Course Resources

II. bag-of-words model and vector space model

A) bag-of-words model

1) Introduction to the bag-of-words model

a) What is the bag-of-words model?

b) How does the bag-of-words model work?

c) What are the benefits of the bag-of words model?

d) What are the limitations of the bag-of words model?

2) Implementation of the bag-of words model in Python

a) Preprocessing text data for the bag -of -words model

b) Building a vocabulary for the bag -of -words model

c) Encoding texts as vectors using the bag -of -words model

d) Decoding encoded vectors back into English using the opposite map

e) Comparing two different texts using cosine similarity with scikit -learn

f )Visualizing vector space models with matplotlib

g ) Applying LSI to Wikipedia articles

h ) Applying LSI to quora questions

i ) Applying LSA to product reviews B ) vector space models 1 ) Introduction to vector space models a ) What are vector space models? b )How do vector space models work ? c )What are some common applications for vector space models ? 2 Implementation of vector space models in Python with scikit learn tfidfVectorizer class a strokes, piano music ,etc preprocessing text data for building a vocabulary and encoding as vectors 8 building a vocabulary and encoding as vectors : spaCy 9 Plotting results with matplotlib 10 Clustering documents using KMeans 11 Discovering latent themes using Non negative Matrix Factorization 12Discovering latent themes using Latent Dirichlet Allocation III topic modeling 13 what is topic modeling ? 14 how does topic modeling work ? 15 what are some common applications for topic modeling ? 16 introduction to gensim 17 Topic Modeling with Gensim :ldaModel 18 Training an ldaModel on Wikipedia Data 19 Exploring and Interpreting Topics 20 ApplyingLDAto Quora Questions 21 improveTopicModelingwith Mallet IV word embeddings 22 what are word embeddings 23 how do word embeddings work 24 how are word embeddings used ? 25 intro to word2vec 26 training own word2vec Embeddings from scratch with gensim 27 visualizing word2vec embeddings with tSNE 28 importan ce word embeddings : practical applications 29 introto glove 30 training gloveembeddingsfrom scratch 31 visualizing gloveembeddingwith PCA 32 conclusion

Course Highlights

Udemy Deep Learning Course: Advanced NLP and RNNs covers how to build advanced systems for Natural Language Processing, using Recurrent Neural Networks (RNNs) in Python.

You’ll learn how to:

– Understand and implement advanced RNN architectures, such as LSTMs and GRUs
– Build a real-world NLP project with PyTorch
– Use TorchText to process and build datasets
– Implement a seq2seq model with attention mechanism
– Train your models on a GPU for extremely fast training times

This course is designed for students who already have some experience with machine learning and natural language processing concepts. If you are new to these topics, we recommend taking our other course, Udemy Machine Learning Course: Supervised and Unsupervised Learning, before starting this one.


Q: Will I get a certificate after completing the course?
A: Yes, all students who complete the course will receive a certificate of completion.

Q: What is the format of the course?
A: The course is self-paced and you can access it on any device. It is divided into modules with videos, quizzes and assignments.

Q: What will I learn in this course?
A: This course will teach you how to build advanced neural networks and natural language processing models.

Q: What are the requirements for taking this course?
A: You should have some experience with deep learning and programming.

Student Success Stories

Preview of the course

In this course you will learn about some of the most advanced Natural Language Processing techniques currently available. We will be using a combination of both traditional NLP methods as well as cutting edge RNN and Sequence to Sequence models.

You will also get experience building and training these models from scratch on real world data. By the end of this course you will have a solid understanding of how to apply these techniques to your own projects.


We hope you enjoyed this Udemy course on deep learning and advanced NLP! In this course, we covered a lot of ground, from theory to practical application. We started with an overview of deep learning, then moved on to covering different types of neural networks, such as RNNs. We also looked at how to implement these networks in code, using TensorFlow and Keras. Finally, we applied our knowledge to real-world data sets, such as natural language processing and sentiment analysis.

Keyword: Udemy Deep Learning Course: Advanced NLP and RNNs

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