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The Complete Recurrent Neural Network with Python Course

The Complete Recurrent Neural Network with Python Course

 Interested in the field of Machine Learning, Deep Learning, and Artificial Intelligence? Then this course is for you!

Category : DevelopmentData Science, Deep Learning

Instructor : Hoang Quy La

Redeem On Udemy


What you'll learn

  • Text analysis
  • Image analysis
  • Embedding layers
  • Word embedding
  • Long short-term memory models
  • Sequence-to-vector models
  • Vector-to-sequence models
  • Bi-directional LSTM
  • Sequence-to-sequence models
  • Transforming words into feature vectors
  • frequency-inverse document frequency
  • Cleaning text data
  • Processing documents into tokens
  • Topic modeling with latent Dirichlet allocation
  • Decomposing text documents with LDA
  • Autoencoder
  • Numpy
  • Pandas
  • Tensorflow
  • Sentiment Analysis
  • Matplotlib
  • out-of-core learning

Description

Interested in the field of Machine Learning, Deep Learning, and Artificial Intelligence? Then this course is for you!

This course has been designed by a software engineer. I hope with the experience and knowledge I did gain throughout the years, I can share my knowledge and help you learn complex theories, algorithms, and coding libraries in a simple way.

I will walk you step-by-step into Deep Learning. With every tutorial, you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.

This course is fun and exciting, but at the same time, we dive deep into Recurrent Neural Network. Throughout the brand new version of the course, we cover tons of tools and technologies including:

  • Deep Learning.
  • Google Colab
  • Keras.
  • Matplotlib.
  • Splitting Data into Training Set and Test Set.
  • Training Neural Network.
  • Model building.
  • Analyzing Results.
  • Model compilation.
  • Make a Prediction.
  • Testing Accuracy.
  • Confusion Matrix.
  • ROC Curve.
  • Text analysis.
  • Image analysis.
  • Embedding layers.
  • Word embedding.
  • Long short-term memory (LSTM) models.
  • Sequence-to-vector models.
  • Vector-to-sequence models.
  • Bi-directional LSTM.
  • Sequence-to-sequence models.
  • Transforming words into feature vectors.
  • frequency-inverse document frequency.
  • Cleaning text data.
  • Processing documents into tokens.
  • Topic modelling with latent Dirichlet allocation
  • Decomposing text documents with LDA.
  • Autoencoder.
  • Numpy.
  • Pandas.
  • Tensorflow.
  • Sentiment Analysis.
  • Matplotlib.
  • out-of-core learning.
  • Bi-directional LSTM.

Moreover, the course is packed with practical exercises that are based on real-life examples. So not only will you learn the theory, but you will also get some hands-on practice building your own models. There are several projects for you to practice and build up your knowledge. These projects are listed below:

  • Bitcoin Prediction
  • Stock Price Prediction
  • Movie Review sentiment
  • IMDB Project.
  • MNIST Project.


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