When we use deep neural architecture instead of classic machine learning algorithms, it’s nothing but deep learning. In this article, I’m going to introduce you to some of the best deep learning projects with Python that you should try as a deep learning beginner.
Deep Learning Projects using Python
Real-Time Face Mask Detection System:
Detecting whether a person is wearing a mask or not in real-time is an amazing use case where you can use neural networks. To train a facial mask detection model, I used a convolutional neural network with 3 dense layers. You can find this deep learning project solved and explained with Python from here.
Landmark Detection Model:
Landmark Detection is an amazing application that is used to detect popular man-made sculptures, structures and monuments. Google already has an amazing landmark detection model that is used in Google maps to show you landmarks. You can also build a landmark detection model using neural network architectures. You can find this complete deep learning project from here.
If you’ve never used the PyTorch library in Python for deep learning, you should get your hands on this project. In this project, I used the deep neural architectures provided by the PyTorch library in Python. Here you will learn how to recognize the breed of a dog present in the image instead of just detecting a dog. You can find this deep learning project from here.
Creating a Neural Network without using Libraries:
If you want to create a neural network without using any deep learning library in Python like TensorFlow and PyTorch then this project is for you. Here you will learn how to create a neural network with just Python code only without using any Python library. You can find this deep learning project from here.
So these were some of the best deep learning projects that you should try as a beginner in deep learning. All the projects mentioned above will train you enough to work on complex deep learning applications. I hope you liked this article on Deep Learning projects with Python. Feel free to ask your valuable questions in the comments section below.