Deep Learning for Omics — CNN, RNN & Transformers Training | Biostatistics & ML for Omics

Apply deep learning to omics and biomedical data using MLPs, CNNs, RNNs and Transformer architectures. Learn data pipelines, regularization, training tricks and evaluation for real world research projects in R & Python.

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Deep Learning for Omics — CNN, RNN & Transformers — Hands-on

Learn how to design and train deep learning models tailored to omics, clinical and biomedical data. This module covers data pipelines, core architectures (MLP, CNN, RNN, Transformers) , regularization, training best practices and evaluation, implemented in R and Python with reproducible notebooks.

Deep Learning for Omics — CNN, RNN, Transformers

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Session Index

Session 1 — Deep Learning Foundations for Omics Session 2 — CNN Architectures for Omics & Images Session 3 — RNNs & Sequence Models Session 4 — Transformers, Attention & End-to-End Pipeline

Session 1

Fee: Rs 8800

Deep Learning Foundations for Omics

Neural network basics and terminology

perceptrons and MLPs activation functions losses and optimizers

Data preparation for deep learning

train/validation/test splits tensor shapes and batching GPU vs CPU considerations

Overfitting, regularization and monitoring

dropout and weight decay early stopping and learning rate schedules training and validation curves

Session 2

Fee: Rs 11800

CNN Architectures for Omics & Images

Convolutional building blocks

convolutions and receptive fields pooling and padding batch normalization

CNNs for omics and biomedical signals

1D CNNs for sequences and profiles 2D CNNs for contact maps or images data augmentation ideas

Transfer learning and fine tuning

pretrained backbones freezing vs unfreezing layers small sample strategies

Session 3

Fee: Rs 14800

RNNs & Sequence Models

Recurrent architectures

vanilla RNNs LSTM and GRU cells bidirectional variants

Modeling biological and clinical sequences

DNA / protein sequence encodings time ordered lab and visit data sequence to label tasks

Training stability and sequence length issues

vanishing and exploding gradients truncated backpropagation padding and masking

Session 4

Fee: Rs 18800

Transformers, Attention & End-to-End Pipeline

Attention and Transformer basics

self attention mechanism multi head attention blocks positional encodings

Using pretrained models and embeddings

bio specific Transformers (concepts) feature extraction vs fine tuning integration with classical ML

Deliverables: end to end deep learning pipeline

training notebook with metrics saved model and config inference script or function