Go Beginner to Pro in Computer Vision in Pytorch / Python with Expert Tips Convolutional Neural Network Deep Learning 4.2 (101 ratings) Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
What you’ll learn
- Master how to Perform Computer Vision Task with Deep Learning
- Learn to Work with PyTorch
- Convolutional Neural Networks with Torch Library
- Build Intuition on Convolution Operation on Images
- Learn to Implement LeNet Architecture on CIFAR10 dataset which has 60000 images
With the Deep learning making the breakthrough in all the fields of science and technology, Deep Learning Computer Vision is the field which is picking up at the faster rate where we see the applications in most of the applications out there.
Be it, Facebook’s image tagging feature, Google Photo’s People Recognition along with Scenery detection, Fraud detection, Facial Recognition, We are seeing the Deep Learning Computer Vision Applications out there.
A typical task in Deep Learning Computer vision task will include the methods for acquiring, processing, analyzing and understanding digital images, and extraction of these high-dimensional data from the real world in order to produce numerical or symbolic information, with which we can form decisions.
A typical & basic operation we perform is – Convolution Operations on Images, where we try to learn the representations of the image so that the computer can learn the most of the data from the input images.
In this course,
We will be learning one of the widely used Deep Learning Framework, i.e PyTorch
It is said as,
PyTorchto be Goto Tool for DeepLearning for Product Prototypes as well as Academia.
signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately. Udemy