In today’s world, machine learning has become one of the most powerful and influential technology. Machine learning and deep learning are the subsets of Artificial Intelligence, which uses statistical techniques to make predictions with the help of available resources.
Unknowingly every one of us is already interacting with the machine learning in our daily lives. For example, every time you google something, watch movies on Netflix, listen to songs on Spotify or watch youtube videos - machine learning works behind the scenes to give you the best user experience by presenting the results based on your interest.
So the best way to master the concepts of machine learning and deep learning is with a hands-on approach, therefore this post gives you innovative projects on deep learning through which you can sharpen your skills and knowledge on deep learning. Since deep learning is going to be the future if you are developing a mini-project or final year project on deep learning will make you unique from others and help you to build a strong career.
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Deep learning is a subset of machine learning, where it creates algorithms and interprets the data similar to machine learning. But the created algorithms has numerous layers- each providing a different solution to the input data. Unlike machine learning, Deep learning follows a non-linear way of approach to analyse the data. Deep learning uses a hierarchical level of artificial neural network to examine the data. The artificial neural network functions similar to our human brain by connecting all the information to get the desired output.
Below example gives you a clear understanding of the difference between deep learning and machine learning.
Consider there is a collection of images which contains a set of pictures of cats and dogs. You want to identify and separate the images of cats and dogs with the help of deep learning and machine learning.
Machine learning approach - Machine learning creates and modify algorithms without human intervention by serving itself structured data to get the desired output. You mark the pictures of cats and dogs in a manner that will define characteristics of both the animals. For the machine learning algorithm, this information will be enough to learn and then it will continue its work based on the learning and separates millions of images of both the animals with the help of structured data.
Deep learning approach - Deep learning will use a different approach to solve this problem. It uses artificial neural network that sends the input (image data) through various layers of the network, where the features of images are defined hierarchically in each network. It works similar to our brain where the query is passed through different neurons to find the solution.
Simply put machine learning algorithm requires structured to classify the images whereas Deep learning can able to classify images without the requirement of structured data by analysing the images within layers of the network. Deep learning depends on the different output from each layer to form a unique way of classifying images.
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This is the most interesting part of this article, here we will discuss some innovative deep learning projects based on real-time applications which you can implement in your day to day life to make the learning process more enjoyable.
1. Brain tumour detection using deep learning: Brain tumours become more harmful if they are not detected in the earlier stages. With the help of deep learning techniques, you can able to detect the brain tumour with more accuracy. In this deep learning project, you will use the MRI image of the brain to identify the brain tumour by creating deep neural networks. You will solve complex problems with the help of tools like Tensorflow and Keras. As the outcome of this project, you will be able to understand and build a neural network and to preprocess the data.
2. Predict Census income using Deep learning: In this Deep learning project, you will use adult census income dataset from Kaggle to find the earnings of an individual. This dataset contains data from the 1994 US census with which you can predict the income. As part of this machine learning project, you will test various models, K-nearest neighbor, classification tree, linear discriminant analysis, logistic regression, gradient boosting and random forest.
3. Sentiment analyser of social media: By understanding the sentiment and opinions of users in social media platforms like Twitter, Facebook, youtube, Instagram, Snapchat, etc. will help business firms for their digital marketing and branding purposes. In this deep learning project, you will develop one such model which uses twitter dataset to analyse the customer behaviour based on their posts and tweets.
4. Lung cancer detection using deep learning: Lung cancers are very difficult to detect for doctors through visual inspection of the CT scans due to this the cancer is detected very late or not detected at all. With the help of this deep learning project, you will provide a solution to help doctors in diagnosing cancer. You will train the model with the help of deep learning algorithms to analyze the CT scan images more efficiently.
5. Hand Gesture recognition using Deep learning: Hand gesture control is widely used in electronics like mobile camera, smart television, gaming devices, etc. In this deep learning project, you will train the model to recognise the hand gesture using static image datasets. You will use ConvNet machine learning algorithm to execute the classification tasks through sound, text, images and video.
6. Music Recommendation system: Everyone likes to listen to music. You want to know the secret behind the song suggestion system of music apps like Spotify and youtube music. Then this deep learning project is for you. In this machine learning project, you will train the model based on the user’s history to increase the accuracy of music recommendation.
7. Wine Quality prediction: Wine quality depends on the percentage of ingredients contained in it. In this deep learning project, you will predict the quality of the wine on a scale of 0-10 with the help of wine quality dataset. The dataset contains input variables like citric acid, volatile acid, density, pH, sulphates, alcohol, etc.
8. House price prediction using machine learning: Predicting house prices in an area will help both the sellers and buyers. In this machine learning project, you will predict the prices of the house in Boston based on the house location, number of rooms, size of the house, and some other properties. Boston dataset is used in this project to get the required data for the prediction.
9. Handwritten digit recognition using machine learning: Recognizing the handwritten documents or boards is used in self-driving cars to generate the text to identify the number plates and boards. In this machine learning project, you will use MNIST data to recognise the handwritten digits. By working on this project, you will learn about deep learning, TensorFlow, neural networks, etc.
10. Movie recommendation system using machine learning: Ever wondered how the Netflix and amazon prime suggests you the favourite movies. This machine learning project reveals the secret behind the system. This machine learning system is widely used in entertainment industries to provide a good user experience. As part of this project, you will train the model to recommend movies based on the user’s search history and watch history.
The below list consists of deep learning projects from basics to advanced level. Develop all the projects to enhance your knowledge and skills in deep learning.
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With this, we come to an end of this deep learning projects article. I hope you got some interesting projects from this article. For those who want to learn more about deep learning, they can sign up for our online course on deep learning where you will get an interactive learning experience and trained by a machine learning expert.
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