Deep Learning (DL) is an aspect of artificial intelligence (AI) that stimulates the activity of a human brain specifically, pattern recognition by passing input through various layers of the neural network.
Deep learning (DL) is a subset of machine learning, but it is capable of achieving tremendous power and flexibility.
Deep learning (DL) contributes heavily toward making our daily life easier and more convenient. In the coming years, this trend will grow even faster.
Examples of deep learning;
According to the Investopedia.com;
Deep learning (DL): ‘is an artificial intelligence (AI) function that imitates the work of the workings of the human brain in processing data and creating patterns to use in decision making.’
Deep learning has evolved rapidly with the digital era; the explosion of data is simply known as big data. The sources of big data include e-commerce platforms internet search engines, social media, online cinemas, and scores of others.
This vast data however is unstructured, though this data is readily available it would take humans too long to extract relevant data.
Here is the list of deep learning (DL) projects using python;
The main reason for road accidents is the drop in alertness level of drivers. During long journeys, it is natural for drivers to doze off or microsleep when behind the steering wheel lack of sleep and stress are two factors that can cause drowsiness while driving resulting in accidents.
A drowsiness detection system prevents and reduces such accidents, in this project you will use python, Keras, and OpenCV to design a system that can detect whether the eyes of drivers are closed and alert them if they were to fall asleep while driving.
This system will immediately alert the driver if they were to doze off and their eyes were to close, even for a few seconds thereby preventing an accident.
Social distancing is an effective way to check the spread of infectious diseases such as COVID19, people are requested to restrict their interactions with others thus physical or close contact is reduced, decreasing the chances of the spread of the disease.
A deep learning algorithm analyzes social distancing in a public place and carries out the required actions to deal with pandemics better.
A social distance analyzer can automate this task.
You will use IBM Watson’s API to model a chat in this project. The chatbot will be capable of engaging with humans like another human being, a chatbot is extremely intelligent and can quickly answer questions or requests by humans in real-time.
This is the reason a lot of companies in various domains are integrating chatbox in the customer support service.
A recent study states that we can create a deep learning model capable of hallucinating colors within a black and white photograph by training a neural network. We will need to use a voluminous and rich data set for this.
In this project, you will use OpenCV DNN architecture trained on an imagenet data set. The aim is to create a colored version of grayscale images. You will use a pre-trained Caffe model, a Numpy file, and a Prototxt file.
Convolutional neural networks (CNNs) and LSTM (Long Short-term Memory) a type of recurrent neural network (RNN) are leveraged to build a model that is capable of generating captions for an image.
Both computer vision (CV) and natural language processing (NLP) techniques are combined by an image caption generator.
In conclusion, there are four main components of the learning process, whether by a human or a machine, namely, data storage, abstraction, generalization, and evaluation.