FDPS & Workshops

Theory: Deep Learning, Perceptron Model, Activation Function, Loss Function, Building Neural Networks, Cross Entropy Loss, Loss Optimization, Gradient Descent, Learning Rate, Overfitting, Dropouts,   Convolution Neural Networks: Feature Extraction, Feature Representation, Fully Connected Network, Convolution Layer & Feature Maps, Pooling, Activation Function, Transfer Learning and Fine-tuning, Popular CNN architectures such as AlexNet, VGG-Net, ResNet, GoogleNet, Introduction to RNNs and their applications, Long Short-Term Memory (LSTM) networks and Gated Recurrent Units (GRUs), Sequence-to-sequence models for natural language processing, Attention mechanism. Transformers.  

Hands-on Session:  Installation of software and IDE, Programming on Python, Intro to Training Resources, Import Packages and Loading of Datasets, Creating CNN Models from Scratch, Compiling of CNN Models, Training and Testing, Plotting of Curves,  Deep Learning Training and Architecture, Feature Extraction, Models training with some pre-trained models,   Text data handling, Train Recurrent Neural Networks (RNNs) on the dataset for sentiment analysis, Training an Attention Network for classification. Implementation of convolution attention block module.

Hands-on Training to configure edge devices (Like Raspberry Pi/Jetson Nano) and deploy AI models for real-world AI applications.

For Students who has experience to train Deep Learning models - Learn to deploy it in edge devices.

Theory: Machine Learning, Supervised Learning, Perceptron model, Building Neural Networks, Cross Entropy loss, loss optimization, Gradient decent, Learning rate, Overfitting, Dropouts, CNN, Case study of use of Business analytics, Machine learning for Business analytics, Case study of use of Deep Learning based AI models for Business analytics

Hands-on Session: Basic Python Programming, Intro to Training resources, Introduction to packages and loading of datasets, Creating CNN models from scratch for Business analytics, Compiling of models, Training and Testing of Models, Training with pertained models for business analytics.

Theory: Machine Learning, Supervised Learning, Perceptron model, Activation function, Loss Function, Building Neural Networks, Cross Entropy loss, loss optimization, Gradient decent, Learning rate, Overfitting, Dropouts, CNN, Case study of use of Machine learning for Biological / Clinical Applications, Case study of use of Deep Learning based CNN models for Biological / Clinical Applications. 

Hands-on Session: Basic Python Programming, Intro to Training resources, Introduction to packages and loading of datasets, Creating CNN models from scratch for Biological Application, Compiling of models, Training and Testing of Models, Training with pertained models for Biological / Medical Applications. 

Theory: Machine Learning, Supervised Learning, Perceptron model, Activation function, Loss Function, Building Neural Networks, Cross Entropy loss, loss optimization, Gradient decent, Learning rate, Overfitting, Dropouts. Features Extraction, Feature Representation, Fully Connected Neural Network, CNN, pooling, stride, padding, Transfer Learning and Fine-tuning.

Hands-on Session: Basic Python Programming, Intro to Training resources, Import packages and loading of datasets, Creating CNN models from scratch, Compiling of models, Training and Testing of Models, Training with pertained models.

Theory: Perceptron model, Activation function, Loss Function, Building Neural Networks, Cross Entropy loss, loss optimization, Gradient decent, Learning rate, Over fitting, Dropouts. Features Extraction, Feature Representation, Fully Connected Neural Network, CNN, pooling, stride, padding, Transfer Learning and Fine-tuning.Recurrent Neural Networks (RNNs), Sequential models for solving Long-term dependency issues. LSTM models, Introduction to Autoencoders and Variational Autoencoders, Generative Adversarial Networks (GANs), Applications of GANs.

Hands-on Session: Basic Python Programming, Intro to Training resources, Import packages and loading of datasets, Creating ANN & CNN models from scratch, Compiling of models, Training and Testing, Plotting of curves, feature extraction, Models training with some pertained models, Image Augmentation, Implementation of RNN, GAN architecture implementation.

2 weeks research project to build a Deep Neural Network for solving a problem. 

Theory: Perceptron model, Activation function, Loss Function, Building Neural Networks, Cross Entropy loss, loss optimization, Gradient decent, Learning rate, Over fitting, Dropouts. Features Extraction, Feature Representation, Fully Connected Neural Network, CNN, pooling, stride, padding, Transfer Learning and Fine-tuning.

Recurrent Neural Networks (RNNs), Sequential models for solving Long-term dependency issues. LSTM models, Introduction to Autoencoders and Variational Autoencoders, Generative Adversarial Networks (GANs), Applications of GANs.

Hands-on Session: Basic Python Programming, Intro to Training resources, Import packages and loading of datasets, Creating ANN & CNN models from scratch, Compiling of models, Training and Testing, Plotting of curves, feature extraction, Models training with some pertained models, Image Augmentation, Implementation of RNN, GAN architecture implementation. 

Theory: Perceptron model, Activation function, Loss Function, Building Neural Networks, Cross Entropy loss, loss optimization, Gradient decent, Learning rate, Overfitting, Dropouts. Features Extraction, Feature Representation, Fully Connected Neural Network, CNN, pooling, stride, padding, Transfer Learning and Fine-tuning.

Hands-on Session: Basic Python Programming, Intro to Training resources, Import packages and loading of datasets, Creating CNN models from scratch, Compiling of models, Training and Testing of Models, Training with pertained models.