Machine learning is a widely used technique, but deep learning is a more advanced version. Deep Learning is a machine learning subfield that uses Artificial Neural Networks. Artificial Neural Networks run on three or more layers, similar to the structure and function of the human brain. Deep learning is utilised for vast volumes of data, to put it simply. Deep learning handles complicated problems such as face recognition and natural language processing, computer vision, machine translation, sound, and so on. Machine learning employs computer algorithms to predict or make judgments. The top 5 deep learning projects for beginners will be discussed in this blog.

You must begin practising with projects if you want to become a deep learning master. Theoretical knowledge will never be enough to clear your deep-learning concepts, so concentrate on real applications. We’ve explored the top 5 beginner-friendly deep learning projects here.

Beginner Deep Learning Projects


  1. Dogs vs. Cats


Dogs vs. cats one of the most straightforward deep learning projects Identify the images of cats and dogs in this project. The topic of this project is cats vs. dogs.


  1. Classification of images using the CIFAR-10 dataset


For novices, Image Classification with the CIFAR-10 dataset is a simple deep learning project.


The CIFAR-10 dataset contains 60,000 colour images, grouped into ten classes of 6,000 images each. The training set has 50,000 photos, whereas the test set contains 10,000. The main purpose of this project is to create an image categorization system that can determine what class an image belongs to. Because it is utilised in so many applications, image classification is the greatest project to start with while learning deep learning.


TensorFlow with the matplotlib package can be used to generate an image classifier. GPU assistance, such as Kaggle or Google Collaboratory, is often encouraged.


  1. Face Recognition


For novices, face detection is a straightforward deep learning project. There are numerous facial recognition systems available. And, thanks to deep learning, the accuracy of these technologies has improved. This face detection project’s main purpose is to detect any item in an image.




Also see Statanalytica’s Different Types of Probability Distribution.




  1. Detection of Crop Disease


Plant illnesses can be found in the soil, plants, or fruits, which is known as crop disease. Fungal spores, bacteria, viruses, and worms can all cause it. You can create a classifier for a crop disease detected from an image in this project. Crop disease is detected using Convolutional Neural Networks (CNN). The crop Disease dataset is available for download on Kaggle.


  1. Identifying the Dog’s Breed


Dog Breed is a beginner-friendly deep learning project. Everyone adores dogs and is eager to learn about different dog breeds. You are aware that there are numerous dog breeds, the most of which are identical. The main purpose of this research is to use the dog breeds dataset to construct a model that can categorise different dog breeds from an image.