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What would you learn in Deep Learning: Recurrent Neural Networks in Python course?
Its Recurrent Neural Network (RNN) has been employed to produce the latest results in sequence modeling.
The latter includes time analysis of series, forecasting, and natural language processing (NLP).
Find out why RNNs beat traditional machine-learning algorithms such as hidden Markov models.
This course will train you to:
The fundamentals in machine learning and neuron (just an overview to warm you up!)
Neural networks to help with classifying and regression (just a quick review to get you accustomed!)
How can I model sequence data
How do you make a time series model?
How do you create a model of text data for NLP (including the steps to preprocess text)
How do I construct an RNN with the help of TensorFlow 2?
How do I use the GRU and LSTM Tensorflow 2?
How do you perform time series forecasting using Tensorflow 2?
How can you forecast stock prices and return on stocks with LSTMs in Tensorflow 2. (hint it's not as you believe!)
How do you use Embeddings within Tensorflow 2 to perform NLP
How do you build an RNN to classify text to use for NLP (examples include sentiment analysis, detection of spam, and tagging of speech parts as well as name-entity recognition)
The materials needed for this course are downloaded and installed at no cost. The majority of our work is with Numpy Matplotlib along with Tensorflow. I'm available to answer any questions you may have and guide you through your journey to data science.
Utilize RNNs for Time Series Forecasting (tackle the common "Stock Prediction" problem)
Utilize RNNs in Natural Language Processing (NLP) and Text Classification (Spam Detection)
Make use of RNNs for Image Classification.
Learn about the Recurrent unit (Elman unit) GRU, GRU, as well as LSTM (long small-term memory unit)
Write different recurrent networks into Tensorflow 2.
Learn how to reduce the problem of the gradient disappearing
Download Deep Learning: Recurrent Neural Networks in Python from below links NOW!
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