Stock Market Forecasting Neural Networks for Multi-Output Regression in Python
Multi-output time series regression can forecast several steps of a time series at once. The number of neurons in the … Read more
Neural Networks
Here you’ll find everything about neural networks (NNs), including Python tutorials.
A neural network is a machine learning algorithm inspired by the structure and function of the human brain. It has multiple layers of interconnected “neurons,” which process and transmit information. The connections between the neurons transform the data and extract useful features. The data then passes through multiple layers of neurons. Each neuron applies a different set of weights and biases to the data before the final layer gives out the predictions.
Different types of neural networks were designed to solve specific kinds of problems. Some common types include:
- Feedforward neural networks are the most basic type, consisting of a series of layers. Each layer receives input from the previous layer and passes it on to the next layer.
- Convolutional neural networks (CNNs): These NNs are designed to process and analyze visual data, such as images or videos. They use convolutions to extract features from the data.
- Recurrent neural networks (RNNs) are ideal for processing sequential data, such as time series data or natural language text. They use a type of memory called a “recurrent state” to remember past inputs.
- Autoencoders: These networks perform dimensionality reduction and data denoising. They consist of an encoder that transforms the input data into lower dimensions and a decoder that reconstructs the original data.
- A generative adversarial network (GAN) is a deep learning model for unsupervised learning. It consists of a generator, which generates new data samples, and a discriminator, which attempts to differentiate between the generations and actual examples.
Image Classification with Convolutional Neural Networks – Classifying Cats and Dogs in Python
This tutorial shows how to use Convolutional Neural Networks (CNNs) with Python for image classification. CNNs belong to the field … Read more
Mastering Multivariate Stock Market Prediction with Python: A Guide to Effective Feature Engineering Techniques
Are you interested in learning how multivariate forecasting models can enhance the accuracy of stock market predictions? Look no further! … Read more
Stock Market Prediction using Multivariate Time Series and Recurrent Neural Networks in Python
Regression models based on recurrent neural networks (RNN) can recognize patterns in time series data, making them an exciting technology … Read more
Measuring Regression Errors with Python
Evaluating performance is a crucial step in developing regression models. Because regression models return continuous outputs, such models allow for … Read more
Rolling Time Series Forecasting: Creating a Multi-Step Prediction for a Rising Sine Curve using Neural Networks in Python
Many time forecasting problems can be solved by predicting just one step into the future. However, some problems require a … Read more
Stock Market Prediction – Adjusting Time Series Prediction Intervals in Python
This tutorial shows how to adjust prediction intervals in time series forecasting using Keras recurrent neural networks and Python. We … Read more
Stock Market Prediction using Univariate Recurrent Neural Networks (RNN) with Python
Financial analysts have long been fascinated by the prospect of predicting the prices of financial assets. In recent years, there … Read more