Neural Network Classification model in hebel

Neural Network Classification model in hebel

Recipe Objective - Neural Network Classification model in Hebel?

A neural network is a circuit of neurons or in a broader sense, an Artificial Neural Network composes of nodes that are the basis of deep learning inspired by the human brain in the same way biologically created neurons signal each other, used in solving artificial intelligence problems. Neural networks comprise node layers, with an input layer, one or more hidden layers, and finally an output layer. Node assigns a number to its incoming connection known as 'weight'. It also contains an associated threshold value. If the output of the individual node is above the specified threshold value then that node is activated thereby sending data to the next layer of the network. No data is passed to the next layer of the network if the output of the individual node is below the specified threshold value.

class hebel.models.NeuralNet(layers, top_layer=None, activation_function='sigmoid', dropout=0.0,
input_dropout=0.0, n_in=None, n_out=None, l1_penalty_weight=0.0, l2_penalty_weight=0.0)

A neural network for classification using the cross-entropy loss function.

 

For more related projects:-

https://www.dezyre.com/projects/data-science-projects/data-science-projects-in-python
https://www.dezyre.com/projects/data-science-projects/machine-learning-projects-in-python

Example:-

# Simple form
model = NeuralNet(layers=[1000, 1000],
     activation_function='relu',
     dropout=0.5,
     n_in=784, n_out=10,
     l1_penalty_weight=.1)

# Extended form, initializing with "HiddenLayer" and "TopLayer" objects
hidden_layers = [HiddenLayer(784, 1000, 'relu', dropout=0.5,
     l1_penalty_weight=.2),
    HiddenLayer(1000, 1000, 'relu', dropout=0.5,
     l1_penalty_weight=.1)]
softmax_layer = LogisticLayer(1000, 10, l1_penalty_weight=.1)

model = NeuralNet(hidden_layers, softmax_layer)

What Users are saying..

profile image

Ameeruddin Mohammed

ETL (Abintio) developer at IBM
linkedin profile url

I come from a background in Marketing and Analytics and when I developed an interest in Machine Learning algorithms, I did multiple in-class courses from reputed institutions though I got good... Read More

Relevant Projects

Recommender System Machine Learning Project for Beginners-1
Recommender System Machine Learning Project for Beginners - Learn how to design, implement and train a rule-based recommender system in Python

Loan Eligibility Prediction using Gradient Boosting Classifier
This data science in python project predicts if a loan should be given to an applicant or not. We predict if the customer is eligible for loan based on several factors like credit score and past history.

Deploying Machine Learning Models with Flask for Beginners
In this MLOps on GCP project you will learn to deploy a sales forecasting ML Model using Flask.

Stock Price Prediction Project using LSTM and RNN
Learn how to predict stock prices using RNN and LSTM models. Understand deep learning concepts and apply them to real-world financial data for accurate forecasting.

Build a Review Classification Model using Gated Recurrent Unit
In this Machine Learning project, you will build a classification model in python to classify the reviews of an app on a scale of 1 to 5 using Gated Recurrent Unit.

Learn to Build an End-to-End Machine Learning Pipeline - Part 2
In this Machine Learning Project, you will learn how to build an end-to-end machine learning pipeline for predicting truck delays, incorporating Hopsworks' feature store and Weights and Biases for model experimentation.

Demand prediction of driver availability using multistep time series analysis
In this supervised learning machine learning project, you will predict the availability of a driver in a specific area by using multi step time series analysis.

Azure Text Analytics for Medical Search Engine Deployment
Microsoft Azure Project - Use Azure text analytics cognitive service to deploy a machine learning model into Azure Databricks

MLOps Project to Deploy Resume Parser Model on Paperspace
In this MLOps project, you will learn how to deploy a Resume Parser Streamlit Application on Paperspace Private Cloud.

Learn Object Tracking (SOT, MOT) using OpenCV and Python
Get Started with Object Tracking using OpenCV and Python - Learn to implement Multiple Instance Learning Tracker (MIL) algorithm, Generic Object Tracking Using Regression Networks Tracker (GOTURN) algorithm, Kernelized Correlation Filters Tracker (KCF) algorithm, Tracking, Learning, Detection Tracker (TLD) algorithm for single and multiple object tracking from various video clips.