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Most research fields require large amounts of funding and well-equipped laboratories. But to work with DL at an early level, all you need is a system. You don't even have to worry about the computing power of your computer. Many cloud platforms on which the model can run are also available. These advantages have made DL a college project for many students. There are many deep learning project to choose from. You may be a beginner or an expert. In this post, I will introduce deep learning project that are  is suitable for everyone.
Deep learning project worth doing
Everyone will experience projects while in college. These deep learning project can be small or innovative. In the age of artificial intelligence and machine learning , it is very natural to focus on deep learning. But with so many options, it can  be confusing. We have listed the top deep learning project you should look at before moving on to the final project.

01.Deep learning project:  Building a Neural Network in Scratch

Neural networks are actually the most basic of DL. To properly understand DL, you need a clear idea of ​​what neural networks are. Several libraries are available to implement deep learning algorithms , but you should write the library once to better understand it. Many may consider this a useless deep learning project. However, once you are done with this project, you will realize its importance. This deep learning project will eventually be among  great deep learning project for beginners.
Building a Neural Network in Scratch

A typical DL model typically has three layers: an input, a hidden layer, and an output. Each layer consists of several neurons.
Neurons are connected to provide a definite output. The model formed by these connections is a neural network.
The input layer receives input. They are basic neurons with not very special properties.
The connections between neurons are called weights. Each neuron in the hidden layer is associated with a weight and bias. The input is multiplied by the corresponding weight and the bias is added.
Then, the data of weights and biases go through an activation function. The loss function of the output measures the error and passes the information back to change the weights and ultimately reduce the loss.
The process continues until losses are minimized. The speed of the process depends on some hyperparameter, such as the learning rate. It will take a lot of time to make, but eventually you will understand how DL works.

02.Deep learning project:  Classification of traffic signs

Self-driving cars are emerging as a recent artificial intelligence (AI) and DL trend . Large automakers such as Tesla, Toyota, Mercedes-Benz and Ford are investing heavily in the development of autonomous vehicle technology. Self-driving cars need to understand and operate on traffic rules.

Consequently, to achieve precision, automobiles must understand road markings and make appropriate decisions. Analyzing the importance of this skill, students can try a traffic sign classification project.

This deep learning project can seem complicated. However, it is very easy to prototype a project with a computer. You only need to know the basics and theoretical knowledge of coding.
First, you need to teach the model various traffic signs. Learning is performed using data sets. “Traffic Sign Recognition” available at Kaggle has over 50,000 images with labels.
After downloading the data set, explore the data set. You can use the Python PIL library to open the image. Clean the data set if necessary.
Then all the images are brought into the list with their labels. CNNs cannot work with raw images, so they convert the images to NumPy arrays. Before training the model, we split the data into a training set and a test set.
Since it is an image processing project, CNN should be involved. Let's flatten the NumPy array data before typing.
Finally, we train and validate the model. Observe the loss and accuracy graphs. The model is then tested on the test set. When the test set yields satisfactory results, other items can be added to this deep learning project.

03.Deep learning project: Breast cancer classification

The breast cancer classification project is also a simple but practical deep learning project. This is also an image processing project. A significant number of women worldwide die each year from breast cancer alone.

However, if cancer is detected early, the mortality rate can be reduced. Many research papers and projects have been published on breast cancer detection. Let's start a deep learning project to improve our knowledge of DL as well as Python programming.
Breast cancer classification

To build a model, you need to use native Python libraries such as Tensorflow, Keras, Theano, CNTK, etc. Both CPU and GPU versions of TensorFlow are available. You can use both, but TensorFlow-GPU is the fastest.
Use the IDC breast histopathology data set. There are 300,000 labeled images alone. The size of each image is 50*50. The entire dataset occupies 3 GB of space.
If you are a beginner, you should use OpenCV for your deep learning project. Read data using OS library. And let's split it into a training set and a test set.
It then builds a CNN, also called CancerNet. A 3 x 3 convolution filter is used. Stack filters and add the maximum pooling layer you need.
Package the entire CancerNet using the sequential API. The input layer takes 4 parameters. Then we set the hyperparameters of the model. Start training with a training set along with a validation set.
Finally, we check the model accuracy by finding the chaos matrix. In this case, let's use the test set. If the results are not satisfactory, change the hyperparameters and run the model again.

04.Deep learning project:  Gender recognition using voice

Gender recognition by voice is a medium difficulty deep learning project. In this deep learning project, we need to process the audio signal to be able to differentiate between genders. It is a binary classification. You have to listen to the voices and distinguish between men and women. Men have a thick voice, and women have a sharp voice. You can understand by analyzing and exploring the signals. The deep learning project will be best done with TensorFlow.

Let's use Kaggle's “Gender Recognition by Voice” data set. The data set contains more than 3,000 audio samples of men and women.
Raw audio data cannot be input into the model, so data is cleaned up and feature extraction is performed. Reduce the noise as much as possible.
Equal numbers of men and women to reduce the chance of overfitting. The data can be extracted using the Mel Spectrogram process. Convert the data to a vector of size 128.
We take the processed audio data as a single array and divide it into a test set and a training set. Next, let's create a model. Using a feed-forward neural network would be appropriate in this case.
Try using at least 5 layers in your model. You can increase the layers as needed. Use “relu” activation for hidden layers and “sigmoid” for output layers.
Finally, we run the model with the appropriate hyperparameters. Use 100 as the epoch. After training, it is verified with the test set.

05.Deep learning project:  Image caption generator

Adding captions to images is an advanced deep learning project. Therefore, you should start after completing the above projects. In today's age of social networks, photos and videos are everywhere. Most people prefer images to paragraphs of text. Also, it is easier to understand with images rather than text.

All these images need captions. When we look at a photo, a caption automatically comes to mind. The same goes for computers. In this deep learning project, a computer will learn to create image captions without human assistance.
Image caption generator

This is actually a complex deep learning project. Nevertheless, the network used here is also problematic. Models should be built using both CNNs and LSTMs (i.e. RNNs).
In this case, we use the Flicker8K data set. As the name suggests, it has 8,000 images occupying 1 GB of space. Also download the “Flicker 8K Text” dataset with image names and captions.
You will need to use many Python libraries such as pandas, TensorFlow, Keras, NumPy, Jupyterlab, Tqdm, Pillow, etc. Make sure all files are available on your computer.
The caption generator model is basically a CNN-RNN model. CNNs extract features and LSTMs help create appropriate captions. We can make the process easier by using a pre-trained model named Xception.
Then train the model. Ensure maximum accuracy. If the results are not satisfactory, clean the data and run the model again.
The model was tested using separate images. You can see that the model is giving the image a proper caption. For example, an image of a bird would be captioned “Bird”.

06.Deep learning project:  Music Genre Classification

People listen to music every day. Everyone has different tastes in music. It is easy to build a music recommendation system using machine learning. However, classifying music into various genres is different. To create a deep learning project, you need to use DL technology. Also, this deep learning project can give you a good idea about audio signal classification. 

Several methods can be used to solve the problem, such as CNNs, support vector machines, K-nearest neighbors, and K-means clustering. You can use anything you like.
The deep learning project uses the GTZAN data set. It contains various songs from 2000-200. Each song is 30 seconds long. Ten genres are available, and each song is properly labeled.
Next, you have to go through the feature extraction process. Break the music into smaller frames every 20-40ms. Then check the noise to make the data noise-free. The process is performed using the DCT method.
Import the necessary libraries for the deep learning project. After feature extraction, the frequency of each data is analyzed. The frequency will also help determine the genre.
Build the model using the appropriate algorithm. It is most convenient to use KNN. But to get knowledge, let's try using CNN or RNN.
After running the model, test its accuracy. 

07.Deep learning project:  Old B&W Image coloring page Free Printable Coloring Pages

These days, everything we see is a color image. There was a time when only black and white cameras were available. All images, of course, were in black and white. However, advances in technology have made it possible to add RGB color to black and white images.

Deep learning has made it pretty easy for us to do these things. You only need to know basic Python programming. All you have to do is create a model, and you can create a GUI for your deep learning project if you wish. This deep learning project can be quite helpful for beginners.
Old B&W Image coloring page Free Printable Coloring Pages

We use the OpenCV DNN architecture as the base model. The neural network is trained using the photo data of the L channel as the source and the signals of the a and b streams as targets.
For convenience, let's use the pretrained Caffe model. Create a separate directory and add all necessary modules and libraries.
Let's read the black and white image and load the Caffe model. If necessary, organize your images according to your deep learning project and increase their accuracy.
Then we modify the pre-trained model. Add layers as needed and process the L-channels to be placed in the model.
Run the model on the training set. Let's make the model as accurate as possible by adhering to accuracy and precision.
Finally, let's make a prediction with the ab channel. Observe the results again and save the model for later use.

08.Deep learning project:  Driver drowsiness detection

Thousands of people use the highway all day, night and day. Taxi drivers, truck drivers, bus drivers, and long-distance travelers all suffer from sleep deprivation. As a result, driving when drowsy is very dangerous. Most accidents are caused by driver fatigue. So, to avoid these crashes, we'll use Python, Keras and OpenCV to create a model that notifies us when our workers are tired.

The purpose of this deep learning project is to create a drowsiness monitoring sensor that monitors when a person's eyes are temporarily closed. When drowsiness is recognized, the model notifies the driver.
In this Python deep learning project, I am using OpenCV to collect pictures from a camera and put them into a deep learning model to determine whether a person's eyes are open or closed.
The data set used in this deep learning project contains several images of people with their eyes open and closed. Each image is labeled. It contains more than 7,000 images.
Then, the model is built with CNN. In this case, use Keras. Upon completion, a total of 128 nodes will be fully connected.
Now run the code and check the precision. Adjust hyperparameters if necessary. I use PyGame to write the GUI.
You can use OpenCV to receive the video or use a webcam instead. Let's test ourselves. Close your eyes for 5 seconds and you will see the model warn you.

09.Deep learning project: Image Classification Using the CIFAR-10 Dataset

A notable deep learning project is image classification. This is a beginner level project. Previously, we did various types of image classification. However, the CIFAR data set is special because it has a wide range of images. You should do this deep learning project before working on other advanced projects. The basics of classification can be understood from this. Let's use python and Keras as usual.

The classification task is to sort all elements of a digital image into one of several categories. In fact, it is very important in image analysis.
The CIFAR-10 data set is a widely used computer vision data set. The data set has been used in various deep learning computer vision studies.
This data set consists of 60,000 pictures separated by 10 class labels, including 6000 pictures of size 32*32. This data set provides low-resolution photos (32*32) so that researchers can experiment with new techniques.
Build models using Keras and Tensorflow and visualize the entire process using Matplotlib. Load data sets directly from keras.datasets. 
The CIFAR data set is almost clean. There is no need to give extra time to process the data. You only need to create the layers you need for your model. Use SGD as the optimizer.
Let's train a model with the data and compute the precision. You can then write a GUI to summarize the entire  deep learning project and test it on arbitrary images rather than data sets.

10.Deep learning project:  Age Detection

Age detection is an important mid-level deep learning project. Computer vision is the study of how computers can see and recognize photos and videos in the same way humans perceive. First, the lack of understanding of the biological perspective is the biggest difficulty.

However, with sufficient data, this lack of biological visual understanding can be eliminated. The same goes for this  deep learning project. Based on that data, a model will be built and trained. That way you can determine the age of people.
Age Detection

In this deep learning project, we need to reliably recognize the age from a single photo of a face using DL.
Due to factors such as cosmetics, lighting, obstacles, and facial expressions, it is very difficult to determine an exact age from a digital photo. Therefore, we make this task a classification task rather than a regression task.
In this case, we use the Adience data set. It has over 25,000 images, each one properly labeled. The total space is almost 1 GB.
Let's make the CNN layer with 3 convolutional layers, a total of 512 connected layers. We train this model using the data set.
Let's write the necessary Python code to detect a face and draw a square box around the face. Follow the steps to mark the age above the box.
Let's create a GUI and test it with a random picture with a human face.
In today's technological age, anyone can learn anything from the Internet. Besides, the best way to learn a new skill is to do more projects. The same goes for experts. If he wants to become an expert in any field, he has to do as many projects as possible. Artificial intelligence is a very important and emerging technology right now. The importance of AI is increasing day by day. 

If you're a beginner, it can be confusing which deep learning project to start with. So, I've listed a few deep learning project you should check out. This article includes both beginner and intermediate level projects. I hope this post is useful to you. So, don't waste your time and start a new deep learning project right now.