Recurrent Neural Network (RNN) has been used to obtain state-of-the-art results in sequence modeling . This includes time series analysis, forecasting and natural language processing . Learn about why RNNs beat old-school machine learning algorithms like Hidden . Use the simple recurrent unit (Elman unit), GRU, and LSTM (long short-term memory unit) to write various recurrent networks in Tensorflow 2 and PYTHON 3 . Read the description of how to mitigate the vanishing gradient problem . Use it to help you learn about one of the most powerful Deep Learning architectures yet! Use it in TENSORFLOW 2 and
What you’ll discover in Deep Knowing: Recurrent Neural Networks in Python
- Apply RNNs to Time Series Projecting (take on the common “Supply Prediction” issue)
- Use RNNs to All-natural Language Handling (NLP) and also Text Classification (Spam Discovery)
- Use RNNs to Picture Category
- Understand the basic reoccurring system (Elman system), GRU, as well as LSTM (lengthy temporary memory unit)
- Write numerous frequent networks in Tensorflow 2
- Understand how to minimize the vanishing gradient trouble
Description
*** NOW IN TENSORFLOW 2 as well as PYTHON 3 ***
Discover one of the most effective Deep Discovering designs yet!
The Recurrent Neural Network (RNN) has actually been utilized to acquire modern cause series modeling.
This includes time series evaluation, projecting as well as all-natural language processing (NLP).
Discover why RNNs beat traditional equipment finding out formulas like Hidden Markov Versions.
This program will instruct you:
Who this course is for:
- Students, professionals, and anyone else interested in Deep Learning, Time Series Forecasting, Sequence Data, or NLP
- Software Engineers and Data Scientists who want to level up their career
| File Name : | Deep Learning: Recurrent Neural Networks in Python free download |
| Content Source: | udemy |
| Genre / Category: | Data Science |
| File Size : | 1.66 gb |
| Publisher : | Lazy Programmer Inc. |
| Updated and Published: | 07 Jul,2022 |
