Neural networks and various other models of how the brain works have been around since people started talking about artificial intelligence. In this article, I’m going to introduce you to some very useful neural networks projects that you can use in your machine learning portfolio.
Neural Networks Projects
Predict Fuel Efficiency with Neural Networks
In these types of machine learning problems to predict fuel efficiency, we aim to predict the output of a continuous value, such as a price or a probability. In this project, I will take you through how we can predict Fuel Efficiency with Machine Learning. Here I will use one of the famous datasets among machine learning practitioners, Auto MPG dataset to create a model to predict fuel efficiency of vehicles in the late 1970s and early 1980s.
To do this, we will provide the model with a description of many automobiles from this period. This description includes attributes such as cylinders, displacement, horsepower and weight. See Full Project.
Text Classification with Neural Networks
I’ll walk you through the basic application of transfer learning with TensorFlow Hub and Keras. I will be using the IMDB dataset which contains the text of 50,000 movie reviews from the internet movie database.
These are divided into 25,000 assessments for training and 25,000 assessments for testing. The training and test sets are balanced in a way that they contain an equal number of positive and negative reviews. See Full Project.
Image Classification with Neural Networks
Here you will learn how we can train a neural network model for the task of image classification with TensorFlow. For those new to TensorFlow, TensorFlow is an end-to-end open-source platform for machine learning.
It has a comprehensive and flexible ecosystem of tools, libraries, and community resources that allow researchers to push cutting-edge advancements in ML, and developers to easily build and deploy machine learning-based applications. See Full Project.
Image Recognition with Neural Networks
Humans take no effort to distinguish a dog, cat, or flying saucer. But this process is quite difficult for a computer to emulate: it only looks easy because God designs our brains incredibly well to recognize images. One common example of image recognition with machine learning is optical character recognition.
In this project, I will take you through building an Image Recognition model with Machine Learning using PyTorch. See Full Project.
Fashion Recommendation System with Neural Networks
Unlike other areas, fashion recommendations shouldn’t be based solely on personal taste and past activity of the customer. There are many external factors (many of which are emotional) that make creating a fashion recommendation system all the more complex.
Public perceptions must be taken into account, as well as fashion rules, dress rules and current trends. See Full Project.
Gender Classification Model with Neural Networks
Indeed, two people can never have the same fingerprints, it is unique in every human. But using fingerprints we can classify gender, whether it’s male or female. In this project, I will take you through a Gender Classification Model which I will train using Deep Learning and Convolutional Neural Networks. See Full Project.
Face Landmarks Detection with Neural Networks
Have you ever thought about how Snapchat manages to apply amazing filters according to your face? It has been programmed to detect some marks on your face to project a filter according to those marks.
In Machine Learning those marks are known as Face Landmarks. In this project, I will guide you about how you can detect face Landmarks with Machine Learning. See Full Project.
Next Word Prediction Model with Neural Networks
Most of the keyboards in smartphones give next word prediction features; google also uses next word prediction based on our browsing history. So a preloaded data is also stored in the keyboard function of our smartphones to predict the next word correctly.
In this project, I will train a Deep Learning model for next word prediction using Python. I will use the Tensorflow and Keras library in Python for next word prediction model. See Full Project.
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