Top 10 Deep Learning Projects For Beginners

By Author: Learnbay
Total Articles: 1
Comment this article

MNIST
MNIST stands for ‘Modified National Institute of Standards and Technology database’. It is a database consisting of handwritten digits. The objective is to identify the correct number. This project is straightforward; it should familiarise you with your deep learning framework and teach you how to build and train your first Artificial Neural Network. It also shows you how to solve multiclass classification problems rather than just binary ones.

TensorFlow and PyTorch both can load MNIST.
CIFAR-10
CIFAR-10 stands for ‘Canadian Institute For Advanced Research’. This dataset comprises 60000 colour images of size 32x32 in ten classes, each with 6000 photos. This project is similar to the previous, although it is a little more complex. It includes colour photos of aeroplanes, birds, dogs, and other objects from ten different classes. It’s a little more challenging to come up with a good classification model here. Instead of utilising a simple neural network, you should use a Convolutional Neural Network and discover how it works.

TensorFlow and PyTorch both can load CIFAR-10.
Image ...
... Recognition
The task of finding objects of interest within an image and determining which category they belong to is known as image recognition. We naturally recognise items as separate instances and associate them with specific definitions when we view them visually. Visual recognition, on the other hand, is a challenging assignment for machines to do.

In the realm of computer vision, image recognition using artificial intelligence has been a long-standing research challenge. While numerous methods have evolved, image recognition’s unifying purpose is to classify observed objects into multiple categories. Hence, they are also called object recognition.
Diagnosis of a disease
For decades, the disease diagnosis process has remained the same: a clinician would examine symptoms, conduct lab testing, and consult medical diagnostic standards. Recent breakthroughs in AI/machine learning/deep learning, on the other hand, have enabled computers to diagnose and identify diseases with human precision. The Deep Learning technology is successfully used in the diagnosis of the following:
Cancer prognosis and detection.
Organ Failure.
Autistic disorder.
Diabetic retinopathy disease diagnosis.
Psoriasis disease diagnosis.
Alzheimer’s disease.
Parkinson’s disease.
Recognition/ Classification of Tweets
This project falls under the category of Natural Language Processing. Another subset of NLP is Sentiment Analysis, where we find the sentiment of the given text. Text classification is a machine learning technique for categorising open-ended text into a collection of predetermined categories. Text classifiers can organise, arrange, and organise almost any type of text, including documents, medical research, and files, as well as text found on the internet.

Unstructured data accounts for over 80% of all data, with text being one of the most common categories. Because analysing, comprehending, organising, and sifting through text data is difficult and time-consuming due to its messy nature, most businesses do not exploit it to its full potential.

Text classifiers enable businesses to rapidly and cost-effectively arrange various forms of relevant text, including email messages, legal documentation, media platforms, chatbots, surveys, and more. This has allowed companies to save time studying text data, automate business processes, and make data-driven business choices.
Recommendation System
Major music and video streaming services such as Spotify and Netflix use Deep learning models to curate a playlist/ watchlist for you. Using data obtained from their interactions, such as impressions, clicks, likes, and purchases, recommender systems are trained to comprehend individuals’ preferences, previous decisions, and characteristics.

Recommender systems aid in the reduction of information overload by assisting consumers in locating relevant items from a large number of options by delivering customised content. Since their ability to predict customer interests and desires on a highly customised level, recommender systems are a favourite of content and product suppliers because they push consumers to just about any product or service that interests them, from books to movies.
Time Series Forecasting
Time series forecasting is a critical application of machine learning. While the time component provides more information, time-series issues are more difficult to anticipate than many other tasks. As the name implies, time-series data differ from different data types in that the temporal aspect is significant. On the plus side, this provides us with more information that we can use when creating our machine learning model. The input features and the changes in input/output over time include helpful information.
Chatbot
Through human-to-human dialogue, a deep learning chatbot learns everything from data. The more data you feed it, the more effective it will become at learning. Chatbots will enhance their accuracy if they are trained extensively. Retrieval-based and generative deep learning chatbots are the two primary forms. Retrieval-based chatbots have a ‘repository’ of responses to questions, whereas generative chatbots don’t.

Existing interactions between customers and support workers can train deep learning chatbots, which should be as thorough and varied as feasible. Data reshaping (generating message-response combinations that the machine will recognise) and pre-processing are also part of the training process (adding grammar so that the chatbot can understand errors correctly).
Object Detection
A subset of computer vision, object detection is an automated method for detecting necessary details in an image with respect to the background.
Placing a tight bounding box around these things and linking the relevant object category with each bounding box is the key to solving the object detection challenge. Deep learning, like other computer vision tasks, is the most advanced way of detecting objects.

The number of things in the foreground can change across photos, which complicates object recognition. Consider restricting the object detection problem by assuming that each image has only one thing to understand better how it works. When there is only one object per image, determine a bounding box and categorise the object. Because the bounding box comprises four values, knowing its position makes it a regression issue. The object is then classified, which is a classification problem.

The convolutional neural network (CNN) solves the regression and classification difficulties for our constrained object detection task. Unlike other traditional computer vision tasks like image recognition, keypoint detection, and semantic segmentation, our constrained object identification issue has a set number of targets. Modelling the targets as a fixed number of classification or regression tasks can be used to fit them.
Style Transfer
Neural style transfer is a technique for blending two images—a content image and a style reference image (such as a famous painter’s work)—so that the output image appears like the content image but is ‘painted’ in the manner of the style reference image.

This is accomplished by adjusting the output image’s content statistics to match the content image’s content statistics and the style reference image’s style statistics. A convolutional network extracts these data from the pictures.