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 Udemi Academy. Among the most important topics covered in this course are GRU architecture, short-term long-term memory (LSTM) architecture, 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 the RNN network are 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
Deep Learning Course Details: Recurrent Neural Networks in Python
Publisher: Udemi
Instructor: Lazy Programmer Inc
Language: English
Education Level: Introductory to Advanced
Number of Courses: 70
Training Duration: 11 hours and 54 minutes
Course topics on 2021/11
Prerequisites for Deep Learning: Recurrent Neural Networks in Python
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 pictures
Installation guide
After Extract, watch with your favorite Player.
English subtitle
Quality: 720p
download link
Download Part 1 – 1 GB
Download Part 2 – 1 GB
Download Section 3 – 816 MB
file password link
Follow On Tumblr
Follow On pinterest
Visit our blog
