Deep Learning for NLP with TensorFlow Udemy Free Download
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:
- Professionals looking for an advanced course of Natural Language Processing using Deep Learning approach
Course Details:
- 8.5 hours on-demand video
- 2 articles
- 7 downloadable resources
- Full lifetime access
- Access on mobile and TV
- Certificate of completion