Category: Implementation
Keras library wrapper classesÂ
KerasClassifier is a wrapper class provided by the Keras library that allows you to use a Keras neural network model as an estimator in scikit-learn workflows. This wrapper enables you to leverage the extensive functionality of scikit-learn, such as cross-validation, grid search, and pipelines, with Keras models seamlessly. Here’s how KerasClassifier works: Here’s a simple…
LabelEncoder of scikit-learn library
LabelEncoder is a utility class provided by the scikit-learn library in Python, specifically in the sklearn.preprocessing module. It is commonly used for encoding categorical labels into numerical labels. Here’s what LabelEncoder does: Here’s an example of how to use LabelEncoder: Keep in mind that LabelEncoder is suitable for encoding target labels (dependent variables) in supervised…
What is Deep Learning
Deep learning is a subset of machine learning that utilizes artificial neural networks with multiple layers (hence “deep”) to learn and extract features from data. It has gained significant attention and popularity due to its ability to automatically learn hierarchical representations of data, which allows for more effective feature extraction and modeling of complex relationships…
Neural Network model building
In neural networks, there are so many hyper-parameters that you can play around with and tune the network to get the best results. Some of them are: Here’s an example of building a neural network model with two hidden layers using the Sequential API in TensorFlow/Keras: Once we are done with the model architecture, we…
Gradient Descent Optimization
Gradient Descent is an optimization algorithm commonly used in machine learning and deep learning to minimize the loss function and find the optimal parameters (weights and biases) of a model. It’s based on the principle of iteratively moving in the direction of the steepest descent of the loss function with respect to the model parameters.…
TensorFlow
TensorFlow is an open-source machine learning library developed by Google Brain team. It is one of the most popular frameworks for building and training machine learning and deep learning models. TensorFlow provides a comprehensive ecosystem of tools, libraries, and community resources to facilitate the development and deployment of various types of machine learning models. Key…
MNIST dataset in artificial neural network
In the context of artificial neural networks (ANNs), MNIST refers to the MNIST dataset, which is often used as a benchmark for training and testing ANN models, particularly for image classification tasks. The MNIST dataset consists of a large collection of grayscale images of handwritten digits from 0 to 9. Each image is a 28×28…
Multi-Layer Perceptron (MLP) in artificial neural network
A Multi-Layer Perceptron (MLP) is a type of artificial neural network that consists of multiple layers of nodes (perceptrons). Unlike a single-layer perceptron, an MLP has one or more hidden layers between the input and output layers. Each node in a layer is connected to every node in the subsequent layer. Here’s a basic overview…
Perceptron in artificial neural network
A perceptron is one of the simplest forms of artificial neural networks. It’s a binary classifier that takes multiple binary inputs and produces a single binary output. Here’s how it works: