explanation
Deep Learning: Recurrent Neural Networks in Python is a deep learning and artificial intelligence course focused on developing recurrent neural networks (RNNs) published by Udemy Academy. The most important topics covered in this course include GRU architecture, short-term long-term memory architecture (LSTM), time series forecasting, stock price forecasting, and natural language processing (NLP) using artificial intelligence. .. Quotation. As you begin this course, you will learn popular deep learning architectures in a concise and practical way. Recursive Neural Networks (RNNs for short) are one of the most popular classes in the development of artificial intelligence-based systems used for modeling task sequences.
The most important application areas of RNN networks include time series prediction of various events, stock price prediction, and natural language processing. RNN-based algorithms are very powerful and the resulting data is much more accurate than older hidden machine learning algorithms such as Markov models. The main tool of this course is the Python programming language, one of the most popular and widely used programming languages in the fields of data science, artificial intelligence, machine learning, and deep learning. Along with Python, you’ll use several powerful and unfamiliar Python-based frameworks, such as Numpy, Matplotlib, and Tensorflow, each with their own applications.
What you will learn in Deep Learning: Recurrent Neural Networks in Python
- Predict sequences and time series of events using RNN neural networks.
- Develop powerful projects to predict future stock prices
- Utilizing RNNs in video classification projects
- Working with Numpy, Matplotlib and Tensorflow libraries
- Develop intelligent tools to classify text and automatically detect spam
- Complete knowledge of natural language processing processes
- Knowledge of other existing architectures and comparison of the pros and cons of each.
- Basics of machine learning and neurons
- Developing neural networks for classification and regression
- Sequence data modeling
- Time series data modeling
- Text data modeling for natural language processing
- Building a recursive neural network using the Tensorflow 2 library
Course specifications
Publisher: Udemy
teacher: Lazy Programmers Inc
Language:English
Level: Beginner~Advanced
Number of classes: 70
Duration: 11 hours 54 minutes
2021/11 Course Topics
Deep Learning: Recurrent Neural Networks in Python Prerequisites
Basic math (differential calculus, matrix operations, probability) is helpful.
Python, Numpy, Matplotlib
Suggested prerequisites:
- Matrix addition and multiplication
- Basic probability (conditional and joint distribution)
- Python coding: if/else, loops, lists, dictionaries, sets
- Numpy coding: matrix and vector operations, loading CSV files
movie
Deep Learning: Introduction to Recurrent Neural Networks in Python Video
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