Deep Learning Application for Finance
|
This course will teach you the applications of deep learning models in the world of Stock Trading and Finance.
Stock Market is a bit volatile and to predict the future trend of the stock and predict a price of a stock in days to come would benefit the Investment Banking/Brokerage Firm to assist their end clients. Hence predicting the same using advanced Neural Network gives a huge advantage to the volatile Trading Market. In this course, Deep Learning Application for Finance, you’ll learn to understand the benefits deep learning offers to resolve problem statements in the Finance Industry such as Fraud, Stock Market Prediction or Portfolio Recommendations. First, you’ll explore the basic nuances of deep learning. Next, you’ll discover different types of neural net models that can resolve different types of case-studies. Finally, you’ll learn how to apply Stacked LSTM to predict the stock price of a company using Keras/Tensorflow. When you’re finished with this course, you’ll have the skills and knowledge of Deep Learning Application for Finance needed to resolve the problem statements connected to the Finance Industry.
Author Name: Niraj Joshi
Author Description:
Niraj is a AWS/Azure DevSecOps Cloud Specialist with over a decade of work experience into Data Modeling with Databases like Cassandra, MongoDB, SparkSQL, ElasticSearch and SQL Server. He has over 7 years of work ex into Computer Vision, Artificial Intelligence, DevOps, Machine Learning and Big Data Stack, he has been a consultant to companies like CISCO, ERICSSON, Dynamic Elements and JP Morgan He has excellent data visualization/ analytics skills and quite proficient in languages like Python ,… more
Table of Contents
- Course Overview
1min - Understanding the Applications of Deep Learning Algorithms in Finance World
12mins - Analyzing the Importance of Deep Learning Algorithm to Predict a Stock Price
12mins - Predicting Stock Price Using Stacked LSTM
9mins
User Reviews
Be the first to review “Deep Learning Application for Finance”
Related Products
Fine-Tuning BERT for Text Reconstruction with Hugging Face
Master the fine-tuning of BERT for text reconstruction tasks using Hugging Face. Learn to enhance BERT's performance on NLP tasks like text generation and sentence reconstruction with transfer learning techniques.
Waste Classification: Hands-on Guide to Transfer Learning
Learn to classify waste using transfer learning techniques. Understand how to leverage pre-trained models for efficient waste classification tasks using image data, and build a robust waste management system using machine learning.
Build an Image Style Transfer Tool using CycleGAN
Explore how to build an image style transfer tool using CycleGAN in PyTorch. Learn the fundamentals of generative adversarial networks (GANs) and how to apply them for transforming images into different artistic styles.
Comparing frozen versus trainable word embeddings in NLP
Dive into the concept of word embeddings in NLP. Learn the difference between frozen and trainable word embeddings, and understand when to use each approach for better model performance in text classification and other tasks.
Deep Learning Application for Healthcare
Deep learning is a powerful machine learning technique that has led to incredible innovations in artificial intelligence. This course will teach you the fundamentals of deep learning in healthcare through theory and applied case studies.
Recurrent Neural Networks (RNNs): Deep Learning for Sequences and Time Series
Recurrent Neural Networks (RNNs) excel at processing sequences, making them ideal for analyzing, and predicting time series data with temporal dependencies.
There are no reviews yet.