Activation functions are a critical part of the design of a neural network. The choice of activation function in the […]

# Archive | Deep Learning

## Autoencoder Feature Extraction for Regression

Autoencoder is a type of neural network that can be used to learn a compressed representation of raw data. An […]

## Autoencoder Feature Extraction for Classification

Autoencoder is a type of neural network that can be used to learn a compressed representation of raw data. An […]

## Softmax Activation Function with Python

Softmax is a mathematical function that converts a vector of numbers into a vector of probabilities, where the probabilities of […]

## How to Use AutoKeras for Classification and Regression

AutoML refers to techniques for automatically discovering the best-performing model for a given dataset. When applied to neural networks, this […]

## Multi-Label Classification with Deep Learning

Multi-label classification involves predicting zero or more class labels. Unlike normal classification tasks where class labels are mutually exclusive, multi-label […]

## Deep Learning Models for Multi-Output Regression

Multi-output regression involves predicting two or more numerical variables. Unlike normal regression where a single value is predicted for each […]

## PyTorch Tutorial: How to Develop Deep Learning Models with Python

Predictive modeling with deep learning is a skill that modern developers need to know. PyTorch is the premier open-source deep […]

## Neural Networks are Function Approximation Algorithms

Supervised learning in machine learning can be described in terms of function approximation. Given a dataset comprised of inputs and […]

## TensorFlow 2 Tutorial: Get Started in Deep Learning With tf.keras

Predictive modeling with deep learning is a skill that modern developers need to know. TensorFlow is the premier open-source deep […]