Deep Learning: Recurrent Neural Networks in Python – Udemy
This course is available through Udemy. The course is designed to teach students about deep learning architectures, particularly RNN. Learners will understand how RNN is used for sequence modeling as well as its applications in time series analysis, forecasting, and NLP. The learners will learn the principles of machine learning, neural network topologies, and essential ideas of neural networks for classification and regression in this course. In addition, the students will learn about sequential data, time-series data, and how to develop text data models for an NLP problem.
The learners will also learn about the steps of RNN construction using TensorFlow, as well as the applications of GRU and LSTM. The students will next learn how to use TensorFlow to develop a model for time series forecasting, which will include tasks such as stock price prediction and text classification using RNN with features such as spam detection, sentiment analysis, and parts-of-speech tagging. Finally, the students will learn how to leverage TensorFlow's embedding for NLP.
The course contents include:
- Introduction
- Google Colab
- Machine Learning and Neurons
- Feedforward ANN
- RNN, Time Series, and Sequence Data
- NLP
- In-Depth: Loss Functions
- In-Depth: Gradient Descent
- Extras: Setting up the Environment/ Extra Help with Python Coding/ Effective Learning Strategies for Machine
- Learning
- Summary
Instructor: Lazy Programmers Inc.
Level: Intermediate
Duration: 11 hours and 49 minutes
User Review: 4.6/5
No. of Reviews: 3447
Price: $47.6
Website: udemy.com/course/deep-learning-recurrent-neural-networks-in-python/
