Like human learning, machine learning also needs to be upgraded from time to time. An ImageNet competition took place in 2012, and it was won by a student named Alex, and that was the birth of alexnet. An alexnet architecture consists of convolutional layers, normalization layers, fully connected layers, softmax layers, and max-pooling layers. Thus, before understanding what is Alexnet we need to understand what do you mean by the convolutional layer. A convolutional layer is an artificial neural network that is designed to process pixel data. In other words, it is basically a powerful artificial intelligence than traditional neural networks used for image recognition.
CNN or Convolutional neural network can be seen in different architectures like LeNet-5, but another CNN architecture, alexnet, outperformed this. Now, alexnet architecture won the competition of annual Image Net in 2012 by producing an error rate of 15.3%, which was almost 10%lower if compared to the second place architecture error rate of 26.2%. This almost more than 10% error rate different rate speaks for itself how much a revolution was needed in this architecture. This is the reason alexnet became one of the leading architectures to understand the deep learning of computer visions.
Image net is one of the biggest image set containing more than 15 million images of more than 20000 different categories and having subcategories which again contain a minimum of 500 images. Image net is also one of the highest competitions which are organized annually, and it was won by alexanet in 2012. In this global annual contest, different software programs compete against each other for image classification and detection of images through testing of data from million training, testing, validation images.
The top 5 error rates are chosen, and the best algorithm with the least error rate among these 5 is chosen as a winner. In 2017, SENet was able to achieve a record-dropping error rate of 2.251%. This artificial algorithm error rate was almost half of the top 5 error rates of humans, which showed how far machine learning has come in all these years.
Alexnet has revolutionized the field of machine learning. AlexNet is constituted of 5 convolutional layers and 3 fully connected layers. That’s the reason it was better than Lenet, as it contains more filters per layer and stacked convolutional layers. Each such filter is further connected with the activation function.
Now, after reading the above article, we have a basic knowledge of what is alexnet. Initially, proper data sets were absent to run deep machine learning algorithms, and that was the reason it was never popular in the business world or even the real world. But now it is possible with alexnet as it comprises 8 layers with more than 62 million learnable parameters. It is a leading architecture that has not only opened a new era of research in machine learning but has also provided deep learning modules for easy implementation of the same.
If you are interested in making a career in the Data Science domain, our 11-month in-person Postgraduate Certificate Diploma in Data Science course can help you immensely in becoming a successful Data Science professional.