Deep Learning With Pytorch: Predicting Global Gold Price

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Published 4/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 464.59 MB | Duration: 1h 4m



Sequential Data Prediction Using LSTM Model with PyTorch
[b]What you'll learn[/b]
perform comprehensive data exploratory analysis, including data preprocessing, visualization, and feature engineering using Python
apply deep learning methods to predict global gold prices, utilizing Python and PyTorch.
evaluate the performance of deep learning models using appropriate metrics
extend acquired skills to other sequential prediction scenarios
[b]Requirements[/b]
Familiarity with Python syntax, data types, and basic operations
[b]Description[/b]
The global precious metal market (e.g., gold, silver, etc.) has significantly grown over the past decades and is expected to grow in future. Specifically, gold, a highly liquid asset, plays a crucial role in preventing individuals and organizations from the adverse effects of a declining dollar. Accurately predicting gold prices not only allows us to uncover evolving patterns in asset prices but also offers opportunities to make strategic investment decisions. Such knowledge is important for both investors and those new to the financial markets.This course introduce deep learning predictive analytics focusing on the global gold market. It provides detailed guidelines for analyzing and forecasting future gold prices using advanced deep learning models, such as Long Short-Term Memory (LSTM) network. Throughout the course, students will gain hands-on experience in conducting exploratory data analysis, mastering feature engineering, and building robust deep learning models using Python/PyTorch. This practical approach ensures students understand the theoretical underpinnings and apply knowledge effectively in real-world scenarios.At the end of this class, students are expected to be proficient in utilizing deep learning models (e.g., LSTM) for time series analysis and extend these applications to other domains (e.g., stock market prediction, trend analysis, temperature forecast, etc.)
[b]Overview[/b]
Section 1: Explore and Visualize Gold Price Data
Lecture 1 Explore and Visualize Gold Price Data
Section 2: Feature Engineering
Lecture 2 Feature Engineering
Section 3: Tensor Data Preparation and LSTM Model Construction
Lecture 3 Tensor Data Preparation & Model Construction
Section 4: Predictive Model Training and Evaluation
Lecture 4 Predictive Model Training and Evaluation
Section 5: Model Performance Improvement
Lecture 5 Model Performance Improvement
python developers,people who love data science and predictive analytics
Screenshots

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