Udemy – Deep Learning: Recurrent Neural Networks in Python 2021-6

Published on: 2021-12-03 14:46:13

Categories: 28

Description

Deep Learning: Recurrent Neural Networks in Python is a Deep Learning and Artificial Intelligence training course focusing on the development of recursive neural networks (RNNs) published by Udemy Academy. Among the most important topics covered in this course are GRU architecture, short-term long-term memory architecture (LSTM), time series forecasting, stock price forecasting, natural language processing (NLP) with artificial intelligence and. .. Cited. At the beginning of this training course, you will get acquainted with the famous deep learning architectures in a brief and at the same time practical way. Recursive neural networks, or RNNs for short, are one of the most popular classes in the development of artificial intelligence-based systems used in modeling operations sequences.

Among the most important applications of RNN network, we can mention time series forecasting of various events, stock price forecasting, natural language processing, and so on. RNN-based algorithms are very powerful, and the resulting data is much more accurate than older hidden machine learning algorithms such as the Markov model. Your main tool in this training course is Python programming language, which is one of the most widely used and popular programming languages in the field of data science, artificial intelligence, machine learning and deep learning. Along with Python, you will also use a number of powerful and unfamiliar Python-based frameworks such as Numpy, Matplotlib and Tensorflow, each of which has a unique application.

What you will learn in Deep Learning: Recurrent Neural Networks in Python

Use the RNN neural network to predict the sequence of events and time series
Develop a powerful project to predict future stock prices
Utilization of RNN in video classification projects
Work with Numpy, Matplotlib and Tensorflow libraries
Develop an intelligent tool to classify texts and automatically detect spam
Full familiarity with the Natural Language Processing process
Familiarity with other existing architectures and comparison of the advantages and disadvantages of each
The basics of machine learning and neurons
Development of neural networks for classification and regression
Sequence data modeling
Time series data modeling
Textual data modeling for natural language processing
Build recursive neural networks with the Tensorflow 2 library

Course specifications

Publisher: Udemy
Instructors: Lazy Programmer Inc
Language: English
Level: Introductory to Advanced
Number of Lessons: 70
Duration: 11 hours and 54 minutes