The Complete Recurrent Neural Network with Python Course

Rating 4.0 out of 5 (2 ratings in 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 …

Rating 4.0 out of 5 (2 ratings in 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.

Duration 6 Hours 58 Minutes

Paid

Self paced

Intermediate Level

English (US)

Rating 4.0 out of 5 (2 ratings in Udemy)

Go to the Course
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