Deep Learning
Face detection is a computer vision problem that involves finding faces in photos. This problem has been solved quite well by classical feature-based techniques, such as the cascade classifier. Recently deep learning methods have achieved state-of-the-art results on standard face detection datasets. One such method is the Multi-task Cascade Convolutional Neural Network, or popularly called […]
#CellStratAILab #disrupt4.0 #WeCreateAISuperstars Minutes from Saturday 7th Dec AI Lab meetup at BLR :- Last Saturday we had fabulous presentations by AI Lab members in BLR. Recommender Systems :- In our Hebbal AI Lab, the day started with a deep session on Recommender Systems by Gurumoorthy Loganathan. Recommender system is a subclass of information filtering […]
Transfer Learning is a good and popular approach in deep learning in which pre-trained models are used as starting point on computer vision (CNN) and natural language processing (NLP) tasks. It is popular in deep learning given the enormous resources required to train deep learning models or the large and challenging datasets on which deep […]
#CellStratAILab #disrupt4.0 #WeCreateAISuperstars CellStrat AI Lab is engaged in incredible AI innovations and product development activity. The sophistication of our AI Lab members’ presentations continues to rise week after week. Last Saturday AI Lab started with an interesting session on Image Descriptors, Feature Descriptors & Feature Vectors by Sonal Kukreja. In computer vision, image descriptors are descriptions of the visual […]
Artificial Intelligence applications are taking over the world in almost all the possible areas known and to for artificial intelligence to be developed, machine learning lrequires lots and lots of training data, more the better. So large data sets are though useful but on the flip side, using a large data set has its own […]
#CellStratAILab #disrupt4.0 #WeCreateAISupertars Last Saturday, CellStrat AI Lab Team Lead Niraj Kale presented an intuitive hands-on workshop on Face Recognition with MTCNN and FaceNet algorithms. The session included a theory presentation along with an extensive hands-on code workshop. This model has two networks at play. First, the MTCNN localizes the face by creating a bounding […]