Udemy – Natural Language Processing with Deep Learning in Python 2019-12

Udemy – Natural Language Processing with Deep Learning in Python 2019-12 Downloadly IRSpace

Apr 17, 2025 - 10:11 Updated: Apr 20, 2025 - 18:15

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Udemy – Natural Language Processing with Deep Learning in Python 2019-12

Natural Language Processing with Deep Learning in Python published by Udemy Academy. A complete guide to mining and implementing word2vec, GloVe, word embedding and sentiment analysis with recurrent networks.

In this course we are going to explore NLP (Natural Language Processing) with deep learning. Earlier, you learned about some basics, like how many NLP problems are just typical machine learning and data science problems, and simple, practical methods like bag-of-word matrices and word documents. These allow us to do very interesting things, such as identifying spam emails, writing poetry, rotating articles, and grouping similar words. In this course I show you how to do more amazing things. In this course, we will learn not only 1, but 4 new architectures.

What you will learn in the Natural Language Processing with Deep Learning in Python course:

Understand and implement word2vec.
Understand the CBOW method in word2vec.
Understand the skip-gram method in word2vec.
Understand negative sampling optimization in word2vec.
Understand and implement GloVe using gradient descent and alternating least squares.
Use recurrent neural networks to label parts of speech.
Use recurrent neural networks to identify the named entity.
And …

Who is this course suitable for:

Any student who needs to take the Duolingo English test.
Don’t let the name fool you. This course is also for students who have weeks or months to prepare.

Course specifications

Publisher: Udemy
Instructor: Lazy Programmer Inc.
Language: English
Training level: Intermediate
Number of courses: 100
Training duration: 12 hours and 49 minutes

Course topics on 2022/12

Natural Language Processing with Deep Learning in Python

Course prerequisites

Install Numpy, Matplotlib, Sci-Kit Learn, and Theano or TensorFlow (should be extremely easy by now)
Understand backpropagation and gradient descent, be able to derive and code the equations on your own
Code a recurrent neural network from basic primitives in Theano (or Tensorflow), especially the scan function
Code a feedforward neural network in Theano (or Tensorflow)
Helpful to have experience with tree algorithms