We all know that many car companies had developed automatic cars. But have you thought how they have done it or what is the technology they had used behind it? It is all with the help of deep learning. The automatic cars will get the senses of traffic lights and traffic signals and even pedestrians which help the car to do fewer accidents. Now people will have questions “what is deep learning”. Actually, Deep Learning is a part of Machine Learning which is a part of Artificial Intelligence. We will discuss it in details, its advantages and some latest deep learning mini-projects.
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Deep Learning is an algorithm which is a subpart of the Machine Learning of Artificial Neural Network. Deep Learning is an algorithm which is inspired by the human brain, at which it learns from a huge amount of data. From the present data, it analyses the current given input and detects the output on the basis of the facts present in the given data. It also helps the machine to predict or solve the complex problems even when the present data which is very different, unstructured and interlinked. The more data it collects or generates the better and efficient to perform its task. In simple words, it resembles the neural connections that exist in the human brain.
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Machine Learning and Deep Learning are both subpart of Artificial Intelligence (AI), which is currently the highest demanding skill in today’s technical fields. It has been attracting attention for several years and will be in huge demand in the coming world too.
Machine Learning (ML) is a subset of artificial intelligence which deals with creating the algorithms that can modify themselves as well as improve themselves without any human intervention to get the required output or result by analysing the present as well as acquired structured data.
Deep Learning is a subset of machine learning as above mentioned, it is similar to Machine Learning, but there are many levels of these algorithms, each supplying with a different understanding or arrangements of data it conveys. This chain network of the algorithm is also called Artificial Neural Network.
The main difference between deep learning and machine learning is due to the way data is presented is the way of presenting data in a machine, device or a system. Machine learning algorithm almost always requires structure data while in case of deep learning, networks rely on artificial neural network (ANN). Deep learning networks don’t need human support, as multilevel layers in neural networks placed in a hierarchy of different ideas of concept, which results in learning from their own mistakes.
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One of the most important growth of Artificial Intelligence (AI) is totally dependent on deep learning. As it has given the systems the power or the ability to do a lot of work itself. Such as the ability to recognize human speech, the ability to detect any medical diagnosis disease, predict the outcomes of particular research, etc. The advantages of deep learning are-
Maximum utilization of unstructured data: For most of the machine learning algorithm its quite hard to analyse unstructured data, i.e. it remains unutilized and that is where deep learning comes in handy. For instance, deep learning algorithms help to reveal any present relation between different unstructured data.
Ability to present the best quality result: Deep learning algorithm or model has the ability to perform thousands of routines, repetitive tasks within a small period of time, compared to what human beings can do. In addition, the quality of work never degrades unless it contains raw data. Even with a failed result it analyses it and corrects it.
Elimination of unnecessary costs: With the help of deep learning, the defects which are hard to train like minor products labelling errors can be detected. Deep learning models can easily identify defects which would be difficult to detect
Here we will discuss some of the latest mini projects of deep learning which will help you to know how deep learning had played a great role in making many things easier and time-saving.
1. Brain tumour detection using deep learning project – Brain tumours are curable if it is detected in early stages. But due to the lack of proper types of equipment brain tumours are detected at their final stage. This deep learning project helps you to develop a deep-learning model that detects the brain tumour by examining the CT scan images of the brain. By working on this mini-project you will learn the following things - working with TensorFlow and Keras, preprocessing and exploration of data, understanding and building a neural network.
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2. Machine learning using python – In this machine learning mini-project, you will develop a machine learning model that predicts the house pricing of a particular area with the help of dataset. As part of this mini-project, you will use Boston housing dataset to predict the house prices of areas in Boston.
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3. Fraud detection using Machine learning – This machine learning projects teaches you to develop a machine learning model that detects credit cards by tracking past banking transactions.
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4. Lung cancer detection: Lung cancer has been seen as one of the most difficult diseases for doctors to deal with it. Since doctors use their eyes to detect, so it’s harder to spot the nodules and hence it takes a long time to detect cancer and sometimes even it’s difficult to detect cancer. 12 sigma uses deep learning to train an Al algorithm that helps the doctor to analyze the CT scan images more perfectly. This model helps hospitals to get results in below 10 minutes which saves 4-5 hours of doctors hard work. This deep learning mini project will help you to detect cancer with the help of deep learning.
5. Wave Glow
Deep learning is Aldo playing a major in audio processing and it’s not generating music. WaveGlow is a flow-based Generative Network for Speech Synthesis by NVIDIA. WaveGlow is applied only using a single cost function, which helps in making training procedure simple and stable. This deep learning project helps you to generate high-quality speech from Mel-spectrograms.
6. Image Enlarging: Google has launched some advanced software that can make detailed images from just a tiny, pixelated source image. So it’s very helpful in times of investigation when they get a blur or tiny photos to know the real face. This deep learning projects help you in making a high-resolution image from low-resolution input.
7. Image Out painting: Suppose you want a full scenery from a photo which has half scenery, then it can be done through Image Out painting. This is really an awesome thing. A deep learning student should try out this. This deep learning project helps you to recover a full scenery photo from a half photo.
8. Human face detection - Idea behind this deep learning projects- The face detection is a good idea to deal it with the help of deep learning techniques. Boundary boxes are used to build the model with more accuracy. This deep learning project will help you to learn how to detect any object from an image.
9. Computer Vision-Based Mouse Project
You might be wondering why people would use computer vision to build a mouse. However, you can find the answer for the same if you think about the physically handicapped. In this Deep learning mini-project, not only will you be learning new concepts related to Computer Vision, but you will also be putting them to use to build something that celebrates inclusion. The mouse you build can be moved by just pointing your fingers, rather than through manual control. You will also learn how to implement the object tracking algorithm and Canny edge detection method. Therefore, this deep learning project will help you make the computer mouse a lot more accessible to people with disabilities.
10. Computer Vision-Based Smart Selfie Project
Nowadays everyone is obsessed with selfies. So, what if I told you that you can use Computer vision to take better selfies? Well, the technology that helps robots to see the world the way humans do can also help you look fantastic in every selfie you take. In this neural networking project, students will build a smart selfie that takes photos automatically when you smile by making use of a facial feature recognition algorithm. Click away, and instead of taking a 100 and deleting 98, get that perfect shot the first time! Also, it isn’t just great photos you will gain, but rather a knowledge of how deep learning algorithms work.
11. Computer vision-based Text Scanner Project
Computer vision powers everything from autonomous vehicles to textual scanning. This Deep learning mini-project is a great way to get started in the field of Computer Vision. In this Computer Vision project, you will build a CV text scanner that can detect text in images. The main principle used in this DIY Deep Learning project is that of the optical character recognition algorithm, and the other things you will learn include thresholding and perspective transformation
12. Movie Recommendation using Machine learning
There are very few who use the internet and are unaware of Netflix. Most of us visit this website in our free time and then spend a few hours there because of the great content. One of the best things about Netflix is their movie recommendation system that appears to know just what you will like. Have you ever wondered how this system works? Well, in this Machine Learning project for beginners you will learn how to build such a system using the basics of segmentation and feature extraction.
13. Brain Tumor Detection Neural Network Project
This deep learning project-based course will serve as the perfect introduction to deep learning. You will learn how to create deep neural networks that help in detecting brain tumours. Students will also learn about neural networking algorithms that will be useful while working on neural network projects. The device will perform data exploration to try and understand brain scan images which are necessary to make decisions. Through this deep learning mini-project, students will learn to work with TensorFlow and Keras, which are important deep learning tools.
14. Handwritten Digits Recognition using ML Project
Computer technology and AI rely on Machine learning when it comes to processing data. One major use of ML comes into play in the field of image recognition. Machine learning can be used to recognize handwritten digits and can also be used to identify number plates of vehicles, read cheques and even convert hand-written documents into PDFs. In this Machine Learning project for beginners, you will learn how to build a recognition algorithm based on MNIST data. This project will serve as a great introduction to the field of neural networking and deep learning.
Here is the list of other deep learning projects:
These were some of the deep learning projects for engineering students. To master the deep learning concepts developing projects is the only way.
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As we can see, deep learning is a technology for the future that will have a huge impact on our lives. Almost every field will in the future have something to do with deep learning and machine learning. Therefore, as a young graduate, the best thing to do would be to start working with this innovative technology as soon as possible. With our DIY deep-learning projects, neural network projects and ML projects, you will be able to gain hands-on experience to build a better life for yourself!
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