Deep Learning for NLP with TensorFlow

Word2Vec, Glove, FastText, Universal Sentence Encoder, GRU, LSTM, Conv1D, Seq2Seq, MachineTranslation, ChatBot, and more

Bullstrack Bullstrack
Feb 24, 2023 - 15:03 Updated: Feb 23, 2023 - 21:07
4.1 0 5
Deep Learning for NLP with TensorFlow
Deep Learning for NLP with TensorFlow

Deep Learning for NLP with TensorFlow udemy course

Word2Vec, Glove, FastText, Universal Sentence Encoder, GRU, LSTM, Conv1D, Seq2Seq, MachineTranslation, ChatBot, and more

What you'll learn:

  • Understand and implement word2vec
  • Understand the CBOW method in word2vec
  • Understand the skip-gram method in word2vec
  • Understand the negative sampling optimization in word2vec
  • Understand and implement GLoVe using gradient descent and alternating least squares
  • Use recurrent neural networks for parts-of-speech tagging
  • Use recurrent neural networks for named entity recognition
  • Understand and implement recursive neural networks for sentiment analysis
  • Understand and implement recursive neural tensor networks for sentiment analysis

Requirements:

  • Install Numpy, Matplotlib, Sci-Kit Learn, Theano, and TensorFlow (should be extremely easy by now)
  • Understand backpropagation and gradient descent, be able to do it on your own.
  • Code a recurrent neural network in Theano
  • Code a feedforward neural network in Theano

Description:

Natural Language Processing (NLP) is a hot topic into Machine Learning field. 

This course is an advanced course of NLP using Deep Learning approach.

Before starting this course please read the guidelines of the lesson 2 to have the best experience in this course.

This course starts with the configuration and the installation of all resources needed including the installation of Tensor Flow 1.X CPU/GPU, Cuda and Keras. You will be able to use your GPU card if you have one, to accelate fastly the training processes of your models. However if you dont have a GPU card you can follow the instructions using Google Colab.

After that we are going to review the main concepts of Deep Learning in the Chapter 2 for applying them into the Natural Language Processing field offering you a solid background for the main chapter.

In the main Chapter 3 we are going to study the main Deep Learning libraries and models for NLP such as:

- Word Embeddings,

- Word2Vec,

- Glove,

- FastText,

- Universal Sentence Encoder,

- RNN,

- GRU,

- LSTM,

- Convolutions in 1D,

- Seq2Seq,

- Memory Networks,

- and the Attention mechanism.

This course offers you many examples, with different datasets suchs as:

- Google News,

- Yelp comments,

- Amazon reviews,

- IMDB reviews,

- the Bible corpus, etc and different text corpus.

At the final in Chapter 4 you will put in practice your knowledge with practical applications such as:

- Multiclass Sentiment Analysis,

- Text Generation,

- Machine Translation,

- Developing a ChatBot and more. 

For coding we are going to use TensorFlow, Keras, Google Colab and many Python libraries.


If you need a previous background in Natural Language Processing or in Machine Learning I recommend you my courses:

  • Python for Machine Learning and Data Mining  or 

  • Natural Language Processing with Python and NLTK


The student has the opportunity to get a feedback from the instructor through Q&A forums, by email: machine.learning.eirl@gmail.com or by Twitter: @AILearningCQ

Who this course is for:

Course Details:

  • 8.5 hours on-demand video
  • 2 articles
  • 7 downloadable resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of completion

Deep Learning for NLP with TensorFlow udemy free download

Word2Vec, Glove, FastText, Universal Sentence Encoder, GRU, LSTM, Conv1D, Seq2Seq, MachineTranslation, ChatBot, and more

Demo Link: https://www.udemy.com/course/mastering-nlp-with-deep-learning-in-keras/

Login to Download  
Tags: