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Top 10 Deep Learning Projects Ideas for Beginners and Professionals

Deep Learning has successfully created hype among students and researchers. Most of the research fields require a lot of funding and well-equipped labs. However, you will only need a computer to work with DL at the initial levels. You don’t even have to worry about the computation power of your computer. Many cloud platforms are available where you can run your model. All these privileges have allowed many students to choose DL as their university project. There are many Deep Learning projects to choose from. You may be a beginner or professional; suitable projects are available for all.

Top Deep Learning Projects

Everyone has projects in their university life. The project may be small or revolutionary. It is very natural for one to work on Deep Learning as it is an age of Artificial Intelligence and Machine Learning. But one may get confused by a lot of options. So, we have listed the top Deep Learning projects you should take a look at before going for the final one.

01.  Building Neural Network from Scratch

The neural network is actually the very base of DL. To understand DL properly, you need to have a clear idea about neural nets. Although several libraries are available to implement them in Deep Learning algorithms, you should build them once to have a better understanding. Many may find it as a silly Deep Learning project. However, you will get its importance once you finish building it. This project is, after all, an excellent project for beginners.

neural_network_from_scratch-Deep Learning projects

Highlights of the project

  • A typical DL model generally has three layers such as input, hidden layer, and output. Each layer is comprised of several neurons.
  • The neurons are connected in a manner to give a definite output. This model formed with this connection is the neural network.
  • The input layer takes the input. These are basic neurons with not-so-special characteristics.
  • The connection between the neurons is called weights. Each neuron of the hidden layer is associated with a weight and a bias. An input is multiplied with the corresponding weight and added with the bias.
  • The data from weights and biases then goes through an activation function. A loss function in the output measures the error and back-propagates the info to change the weights and ultimately decrease the loss.
  • The process continues until the loss is minimum. The speed of the process depends on some hyper-parameters, such as the learning rate. It takes a lot of time to build it from scratch. However, you can finally understand how DL works.

02. Traffic Sign Classification

Self-driving cars are a rising AI and DL trend. Big car manufacturing companies like Tesla, Toyota, Mercedes-Benz, Ford, etc., are investing a lot to advance technologies in their self-driving vehicles. An autonomous car needs to understand and work according to traffic rules.

As a result, to achieve precision with this innovation, the cars must comprehend road markings and make appropriate decisions. Analyzing the importance of this technology, students should try to do the traffic sign classification project.

Highlights of the project

  • The project may seem complicated. However, you can do a prototype of the project quite easily with your computer. You will only need to know the basics of coding and some theoretical knowledge.
  • At first, you need to teach the model different traffic signs. The learning will be done using a dataset. “Traffic Sign Recognition” available in Kaggle has more than fifty thousand images with labels.
  • After downloading the dataset, explore the dataset. You can use the Python PIL library to open the images. Clean the dataset if required.
  • Then take all the images into a list along with their labels. Convert the images into NumPy arrays as CNN cannot work with raw images. Split the data into train and test set before training the model
  • Since it is an image processing project, there should be a CNN involved. Create the CNN according to your requirements. Flatten the NumPy array of data before inputting.
  • At last, train the model and validate it. Observe the loss and accuracy graphs. Then test the model on the test set. If the test set shows satisfactory results, you can move on to adding other things to your project.

03. Breast Cancer Classification

If you wish to grasp Deep Learning, you must complete Deep Learning projects. The breast cancer classification project is yet another straightforward but practical project to do. This is also an image processing project. A significant number of women worldwide die every year only due to breast cancer.

However, the death rate could decrease if cancer could be detected at an early stage. Many research papers and projects have been published regarding breast cancer detection. You should recreate the project to enhance your knowledge of DL as well as Python programming.

Histological-classification-of-breast-cancer-deep learning projects

Highlights of the project

  • You will have to use the basic Python libraries like Tensorflow, Keras, Theano, CNTK, etc., to create the model. Both CPU and GPU version of Tensorflow is available. You can use either one. However, Tensorflow-GPU is the fastest.
  • Use the IDC breast histopathology dataset. It contains almost three hundred thousand images with labels. Each image has the size 50*50. The whole dataset will take three GB of space.
  • If you are a beginner, you should use OpenCV in the project. Read the data using the OS library. Then split them into train and test sets.
  • Then build the CNN, which is also called the CancerNet. Use three by three convolution filters. Stack the filters and add the necessary max-pooling layer.
  • Use sequential API to pack up the whole CancerNet. The input layer takes four parameters. Then set the hyper-parameters of the model. Start training with the training set along with the validation set.
  • Finally, find the confusion matrix to determine the accuracy of the model. Use the test set in this case. In case of unsatisfactory results, change the hyper-parameters and run the model again.

04. Gender Recognition using Voice

Gender recognition by their respective voices is an intermediate project. You have to process the audio signal here to classify between genders. It’s a binary classification. You have to differentiate between males and females based on their voices. Males have a deep voice, and females have a sharp voice. You can understand by analyzing and exploring the signals. Tensorflow will be the best to do the Deep Learning project.

Highlights of the project

  • Use the “Gender Recognition by Voice” dataset of Kaggle. The dataset contains more than three thousand audio samples of both males and females.
  • You cannot input the raw audio data into the model. Clean the data and do some feature extraction. Decrease the noises as much as possible.
  • Make the number of males and females equal to reduce overfitting possibilities. You can use the Mel Spectrogram process for data extraction. It turns the data into vectors of size 128.
  • Take the processed audio data into a single array and divide them into test and train sets. Next, build the model. Using a feed-forward neural network will be suitable for this case.
  • Use at least five layers in the model. You can increase the layers according to your necessities. Use “relu” activation for the hidden layers and “sigmoid” for the output layer.
  • Finally, run the model with suitable hyper-parameters. Use 100 as the epoch. After training, test it with the test set.

05. Image Caption Generator

Adding captions to the images is an advanced project. So, you should start it after finishing the above projects. In this age of social networks, pictures and videos are everywhere. Most people prefer an image to a paragraph. Moreover, you can easily make a person understand a matter with an image than with writing.

All these images need captions. When we see a picture, automatically, a caption comes to our mind. The same thing has to be done with a computer. In this project, the computer will learn to produce image captions without any human help.

image_caption_generator-deep learning projects

Highlights of the project

  • This is actually a complex project. Nevertheless, the networks used here are also problematic. You have to create a model using both the CNN and LSTM, i.e., RNN.
  • Use the Flicker8K dataset in this case. As the name suggests, it has eight thousand images taking one GB of space. Moreover, download the “Flicker 8K text” dataset containing the image names and caption.
  • You have to use a lot of python libraries here, such as pandas, TensorFlow, Keras, NumPy, Jupyterlab, Tqdm, Pillow, etc. Make sure all of them are available on your computer.
  • The caption generator model is basically a CNN-RNN model. CNN extracts features, and LSTM helps to create a suitable caption. A pre-trained model named Xception can be used to make the process easier.
  • Then train the model. Try to gain maximum accuracy. In case the results are not satisfactory, clean the data and run the model again.
  • Use separate images to test the model. You will see the model is giving proper captions to the images. For example, a bird’s image will get the caption “bird.”

06. Music Genre Classification

People hear music every day. Different people have different music tastes. You can easily build a music recommendation system using Machine Learning. However, classifying music into different genres is a different thing. One has to use DL techniques to make this Deep Learning project. Moreover, you can get a very good idea of audio signal classification through this project. It is almost like the gender classification problem with a few differences.

Highlights of the project

  • You can use several methods to solve the problem, such as CNN, support vector machines, K- nearest neighbor, and K-means clustering. You can use any of them according to your preferences.
  • Use the GTZAN dataset in the project. It contains different songs up to 2000-200. Each song is 30 seconds long. Ten genres are available. Each song has been labeled properly.
  • In addition, you have to go through feature extraction. Divide the music into smaller frames of each 20-40 ms. Then determine the noise and make the data noise-free. Use the DCT method to do the process.
  • Import necessary libraries for the project. After extraction of features, analyze the frequencies of each data. The frequencies will help to determine the genre.
  • Use a suitable algorithm to build the model. You can use KNN to do it as it is the most convenient. However, to gain knowledge, try to do it using CNN or RNN.
  • After running the model, test the accuracy. You have successfully built a music genre classification system.

07. Colorizing Old B&W Images

Nowadays, everywhere we see are colored images. However, there was a time when only monochrome cameras were available. Images, along with movies, were all black and white. But with the advancement of technology, you can now add RGB color to black and white images.

Deep Learning has made it quite easy for us to do these tasks. You just have to know basic Python programming. You just have to build the model, and if you want, you can also make a GUI for the project. The project can be quite helpful for beginners.

Highlights of the project

  • Use OpenCV DNN architecture as the main model. The neural network is trained using picture data from the L channel as source and signals from the a, b streams as the goal.
  • Furthermore, use the pre-trained Caffe model for extra convenience. Make a separate directory and add every necessary module and library there.
  • Read the black and white images and then load the Caffe model. If required, clean the images according to your project and to get more accuracy.
  • Then manipulate the pre-trained model. Add layers to it as necessary. Moreover, process the L-channel to deploy into the model.
  • Run the model with the training set. Observe the accuracy and precision. Try to make the model as accurate as possible.
  • At last, make predictions with the ab channel. Observe the results again and save the model for later usage.

08. Driver Drowsiness Detection

Numerous people use the freeway at all hours of the day and overnight. Cab drivers, truck drivers, bus drivers, and long-distance travelers all suffer from sleep deprivation. As a result, driving when sleepy is highly hazardous. The majority of accidents occur as a result of the driver’s tiredness. So, to avoid these collisions, we’ll use Python, Keras, and OpenCV to create a model that will inform the operator when he gets tired.

Highlights of the project

  • This introductory Deep Learning project aims to create a sleepiness monitoring sensor that monitors when a man’s eyes are shut for a few moments. When sleepiness is recognized, this model will notify the driver.
  • You will be using OpenCV in this Python project to collect photos from a camera and put them into a Deep Learning model to determine if the person’s eyes are wide open or shut.
  • The dataset used in this project has several images of persons with closed and open eyes. Each image has been labeled. It contains more than seven thousand images.
  • Then build the model with CNN. Use Keras in this case. After completing, it will have a total of 128 fully connected nodes.
  • Now run the code and check the precision. Tune the hyper-parameters if it needs to. Use PyGame to build a GUI.
  • Use OpenCV to receive video, or you can use a webcam instead. Test on yourselves. Close your eyes for 5 seconds, and you will see the model is warning you.

09. Image Classification with CIFAR-10 Dataset

A noteworthy Deep Learning project is image classification. This is a beginner-level project. Previously, we have done various types of image classification. However, this one is a special one as the images of the CIFAR dataset fall under a variety of categories. You should do this project before working with any other advanced projects. The very basics of classification can be understood from this. As usual, you will use python and Keras.

Highlights of the project

  • The categorization challenge is sorting every one of the elements in a digital image into one of several categories. It is actually very important in image analysis.
  • The CIFAR-10 dataset is a widely used computer vision dataset. The dataset has been used in a variety of deep learning computer vision studies.
  • This dataset is made up of 60,000 photos separated into ten class labels, each including 6000 photos of the size 32*32. This dataset provides low-resolution photos (32*32), allowing researchers to experiment with new techniques.
  • Use Keras and Tensorflow to build the model and Matplotlib to visualize the whole process. Load the dataset directly from keras.datasets. Observe some of the images among them.
  • The CIFAR dataset is almost clean. You don’t have to give extra time to process the data. Just create the required layers for the model. Use SGD as the optimizer.
  • Train the model with the data and calculate the precision. Then you can build a GUI to sum up the whole project and test it on random images other than the dataset.

10. Age Detection

Age detection is an important intermediate-level project. Computer vision is the investigation of how computers can see and recognize electronic pictures and videos in the same way that humans perceive. The difficulties it confronts are primarily due to a lack of understanding of biological sight.

However, if you have enough data, this lack of biological sight can be abolished. This project will do the same. A model will be built and trained based on the data. Thus the age of people can be determined.

Highlights of the project

  • You shall utilize DL in this project to reliably recognize an individual’s age from a single photograph of their appearance.
  • Because of elements such as cosmetics, illumination, obstacles, and facial expressions, determining an exact age from a digital photo is extremely hard. As a result, rather than calling this a regression task, you make it a categorization task.
  • Use the Adience dataset in this case. It has more than 25 thousand images, each one labeled properly. The total space is nearly 1GB.
  • Make the CNN layer with three convolution layers with a total of 512 connected layers. Train this model with the dataset.
  • Write necessary Python code to detect the face and draw a square box around the face. Take steps to show the age on top of the box.
  • If everything goes well, build a GUI and test it with random pictures with human faces.

Finally, Insights

In this age of technology, anyone can learn anything from the internet. Moreover, the best way to learn a new skill is to do more and more projects. The same tip goes to experts too. If someone wants to become an expert in a field, he has to do projects as much as possible. AI is a very significant and rising skill now. Its importance is increasing day by day. Deep Leaning is an essential subset of AI dealing with computer vision problems.

If you are a beginner, you may feel confused about which projects to start with. So, we have listed some of the Deep Learning projects you should take a look at. This article contains both beginner and intermediate-level projects. Hopefully, the article will be beneficial to you. So, stop wasting time and start doing new projects.

Mehedi Hasan
Mehedi Hasan
Mehedi Hasan is a passionate enthusiast for technology. He admires all things tech and loves to help others understand the fundamentals of Linux, servers, networking, and computer security in an understandable way without overwhelming beginners. His articles are carefully crafted with this goal in mind - making complex topics more accessible.


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