Description
Deep Learning: Recurrent Neural Networks in Python is a deep learning and artificial intelligence course focusing on the development of recurrent neural networks (RNN) published by Yudemy Academy. Among the most important topics covered in this training course are GRU architecture, long-short-term memory (LSTM) architecture, time series forecasting, stock price forecasting, natural language processing (NLP) with artificial intelligence, etc. Cited. At the beginning of this training course, you will get to know the famous deep learning architectures in a brief yet practical way. Recurrent Neural Networks or RNN for short are one of the most famous classes of artificial intelligence-based systems development that are used in modeling the sequence of operations.
Among the most important applications of the RNN network, we can mention the prediction of the time series of various events, stock price prediction, natural language processing, etc. Algorithms based on RNN are very powerful and the resulting data are very accurate compared to old machine learning algorithms such as the hidden Markov model. Your main tool in this educational course is the Python programming language, which is one of the most used and popular programming languages in the field of data science, artificial intelligence, machine learning, and deep learning. In addition to Python, you will also benefit from a series 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 course:
- Using neural network RNN In order to predict the sequence of events and time series
- Development of a powerful project to predict future stock prices
- Operation of RNN In image classification projects
- Working with Numpy, Matplotlib and Tensorflow libraries
- Development of an intelligent tool to classify texts and automatically identify spams
- Familiarity with the process Natural Language Processing
- Getting to know other existing architectures and comparing the advantages and disadvantages of each
- Basic principles of machine learning and neurons
- Development of neural networks for classification and regression
- Modeling sequence data
- Time series data modeling
- Textual data modeling for natural language processing
- Building recurrent neural networks with Tensorflow 2 library
Details of the course Deep Learning: Recurrent Neural Networks in Python
Publisher: Yodmi
Instructor: Lazy Programmer Inc
English language
Training level: introductory to advanced
Number of courses: 70
Training duration: 11 hours and 54 minutes
Course topics on 11/2021
Deep Learning: Recurrent Neural Networks in Python course prerequisites
Basic math (taking derivatives, matrix arithmetic, probability) is helpful
Python, Numpy, Matplotlib
Suggested Prerequisites:
- matrix addition, multiplication
- basic probability (conditional and joint distributions)
- Python coding: if/else, loops, lists, dicts, sets
- Numpy coding: matrix and vector operations, loading a CSV file
Course images
Deep Learning: Recurrent Neural Networks in Python course introduction video
Installation guide
After Extract, view with your favorite Player.
English subtitle
Quality: 720p
download link
Password file(s): www.downloadly.ir
Size
2.8 GB
Be the first to comment