Deep learning is a form of machine learning that allows computers to learn complicated concepts through a hierarchy of simple ones. In other words, deep learning algorithms
seek to exploit the unknown structure in the input distribution in order to discover good representations, often at multiple levels, with higher-level learned features defined in terms of lower-level features. It has brought dramatic performance
advances on numerous difficult machine learning tasks such as image and object recognition, speech recognition, natural language processing and many other domains such as drug discovery and genomics.
This workshop is specifically designed to provide the right knowledge to the aspiring data scientists who wish to understand and apply Deep Learning techniques to a variety of problems.
|Date/Time||9:30 am-10:45 am; 11:00 am - 12:30 pm||1:30 pm - 2:45 pm; 3:00 pm - 4:30 pm|
• Machine Learning overview
• Introduction to Colab and Scientific Python
• Introduction to Deep Learning
• Regression using Deep Neural Network to predict sales based on advertisement in different media
• Recurrent Neural Networks
• Time Series Forecasting using Simple-RNN, LSTM and GRU on daily minimum temperature prediction
• Convolutional Neural Networks
• Image Classification using CNN on the Flower-classification dataset maintained by Google-TensorFlow
• Transfer-Learning and Fine-tuning pre-trained models
• Methods to visualize the model, callbacks to save best model, evaluation metrics for classification models
• Discussions about image segmentation and object detections
• ML Project perspectives and directions
UG students (BCA/BSc. Computer Science/ BTech.) / PG students/ Research Scholars/ Academicians / Anyone with interest and enthusiasm to learn Note: Proficiency in python programming is desirable.