👨‍⚕️

Dr. Daya Shankar

Dean, Woxsen University | Founder, VaidyaAI | Applying AI to Solve Clinical Challenges in Diagnostics & Documentation | HealthTech Innovator

Leading expert in AI applications for healthcare, with extensive experience in deep learning, clinical diagnostics, and educational innovation. Passionate about bridging the gap between cutting-edge AI research and practical healthcare solutions.

🌐 Personal Website 🏥 VaidyaAI 🎓 Woxsen University

🧠 Deep Learning Mastery

🏆 Dean

Prof. Dr. Daya Shankar

Dean, School of Sciences | Woxsen University

Founder & AI Innovator | VaidyaAI Healthcare Solutions

🧠 AI Research Excellence

Deep Learning • Computer Vision • NLP

🏥 HealthTech Innovation

Medical AI • Clinical Decision Support

🎓 Academic Leadership

Leading 5 Departments • 500+ Students

🌟 About the Instructor

Prof. Dr. Daya Shankar serves as the Dean of School of Sciences at Woxsen University, overseeing five departments: Physics, Chemistry, Mathematics, Biotechnology, Computer Science, and AI & Data Science. With a Ph.D. in Mechanical Engineering, he specializes in applying rigorous quantitative analysis to solve complex data challenges.

As the founder of VaidyaAI, he's revolutionizing healthcare through artificial intelligence, creating medical intelligence systems that transform complex medical data into actionable insights for clinicians and patients. His work bridges the gap between cutting-edge AI research and practical healthcare applications.

Dr. Shankar's educational philosophy centers on making complex AI concepts accessible through interactive, hands-on learning experiences. This course represents months of careful preparation, combining theoretical rigor with practical application to create the ultimate deep learning educational resource.

🌐 Connect & Explore

🌐 Personal Website 🏥 VaidyaAI Platform 🎓 Woxsen University 💼 LinkedIn Profile

💭 Philosophy

"Transforming complex AI concepts into intuitive, interactive learning experiences that empower the next generation of practitioners to solve humanity's greatest challenges."

Comprehensive 45-Lecture Interactive Course

Master deep neural networks from fundamental concepts to cutting-edge applications. This comprehensive course covers theory, implementation, and real-world applications with hands-on interactive lectures designed for undergraduate and graduate students.

Interactive Lectures

45+

Hours Content

Lab Experiments

Credits

100+

Interactive Demos

📚 Module I: Deep Learning Foundations

12 Lectures • 12 Hours

Master the fundamental concepts, mathematics, and core techniques that form the backbone of deep learning

Progress: 7/12 lectures available 58% Complete

✅ Available

Lecture 1

🏛️ History & Evolution of AI

Journey through AI history from early concepts to modern breakthroughs. Understand key milestones and the evolution of neural networks.

AI Timeline Neural Network History Key Breakthroughs

📐 Mathematical Foundations

Essential mathematics for deep learning: linear algebra, calculus, probability, and statistics with interactive visualizations.

Linear Algebra Calculus Probability Statistics

🧮 Perceptron & Neural Basics

Understanding the building blocks: perceptrons, neurons, weights, biases, and basic neural network architecture.

Perceptron Weights & Biases Activation Functions

➡️ Forward Propagation

Deep dive into how neural networks process information through layers with step-by-step matrix operations.

Matrix Operations Layer Computation Information Flow

⬅️ Backward Propagation

Master the heart of learning: gradient computation, chain rule, and how networks learn from mistakes.

Chain Rule Gradients Weight Updates

⬇️ Gradient Descent Fundamentals

Understand optimization basics: vanilla gradient descent, learning rates, and convergence properties.

Optimization Learning Rate Convergence

⚡ Mini-batch & Stochastic GD

Efficient training with mini-batch gradient descent and stochastic optimization techniques.

Mini-batch SGD Efficiency

🚀 Advanced Optimizers

Modern optimization algorithms: Momentum, RMSprop, Adam, and adaptive learning rate methods.

Momentum Adam RMSprop

🎯 Hyperparameter Tuning

Systematic approaches to configure networks: learning rates, architectures, and optimization strategies.

Parameter Tuning Grid Search Validation

🛡️ Regularization Techniques – Part 1

Foundations of Prevent overfitting with dropout, L1/L2 regularization, early stopping, and data augmentation

Regularization Techniques L1/L2 regularization

🛡️ Regularization Techniques – Part 2

Advanced and Mathematical details of Prevent overfitting with dropout, L1/L2 regularization, early stopping, and data augmentation

early stopping data augmentation

🎲 Normalization Techniques – Part 1

Foundations of Internal covariate shift, normalization techniques.

normalization Internal covariate shift

🎯 Normalization Techniques – Part 2

Advanced and Mathematical details of Internal covariate shift, normalization techniques.

Batch Normalization Covariate Shift

🎲 Loss Functions – Part 1

Foundations of loss functions, intuition, and basic error metrics.

Loss Intuition Error Metrics

🎯 Loss Functions – Part 2

Advanced metrics, implementation, and evaluation strategies.

Cross-Entropy Evaluation

⏱️ 30 minutes

🚀 Continue Part 2

🖼️ Module II: Convolutional Neural Networks

12 Lectures • 12 Hours

Master computer vision with CNNs, from basic convolutions to state-of-the-art architectures

Progress: 3/12 lectures available 25% Complete

✅ Available

Lecture 13

🔍 Introduction to Computer Vision

Foundations of computer vision: image representation, pixels, channels, and basic image processing. Interactive storytelling approach with Sarah's photography shop.

Pixels & RGB Image Processing Convolution Basics Histograms

🌊 Convolution Operations

Deep dive into convolution: kernels, filters, feature maps, and convolution mathematics. Interactive detective story approach with step-by-step examples.

Kernels & Filters Feature Maps Stride & Padding Math Examples

🏊 Pooling & Subsampling

Reduce spatial dimensions with pooling layers: max pooling, average pooling, and modern alternatives. News reporter storytelling approach with practical examples.

Max Pooling Average Pooling Global Pooling Performance Impact

🏗️ CNN Architecture Design

Building complete CNN architectures: layer stacking, feature hierarchies, and architectural principles.

Historical CNN architectures: LeNet-5 and the revolutionary AlexNet that started the deep learning boom.

📈 VGG & Network Depth

Going deeper with VGG networks: understanding the impact of network depth and small filter design.

💎 ResNet & Skip Connections

Revolutionary residual connections: solving vanishing gradients and enabling ultra-deep networks.

Multi-scale feature extraction with Inception modules and efficient network design principles.

🎯 Object Detection Basics

From classification to detection: bounding boxes, IoU, and object localization fundamentals.

Region-based detection: R-CNN, Fast R-CNN, and Faster R-CNN architectures and improvements.

⚡ YOLO & Real-time Detection

You Only Look Once: real-time object detection with single-shot detection methods.

🖼️ Semantic Segmentation

Pixel-level understanding: FCN, U-Net, and semantic segmentation architectures.

⏱️ 60 minutes

📅 Future

🔄 Module III: Recurrent Networks & Sequences

11 Lectures • 11 Hours

Master sequential data processing with RNNs, LSTMs, GRUs, and modern attention mechanisms

Progress: 0/11 lectures available 0% Complete

📋 Planned

Lecture 25

📝 Introduction to Sequential Data

Understanding time series, text, speech, and sequential patterns in data.

🔄 Vanilla RNN Fundamentals

Basic recurrent neural networks: hidden states, unfolding, and simple sequence modeling.

⚡ RNN Training & Challenges

Backpropagation through time, vanishing/exploding gradients, and training difficulties.

Long Short-Term Memory: gates, cell states, and solving long-term dependencies.

Gated Recurrent Units: simplified gating mechanisms and modern RNN variants.

Processing sequences in both directions: bidirectional architectures and applications.

🎯 Attention Mechanisms

Revolutionary attention: focusing on relevant parts of sequences and solving alignment problems.

🔮 Transformer Architecture

Self-attention and transformers: the architecture that revolutionized NLP and beyond.

📚 BERT & Language Models

Bidirectional transformers: BERT, pre-training, fine-tuning, and modern language understanding.

🗨️ Text Generation & GPT

Generative language models: GPT architecture, autoregressive generation, and large language models.

📈 Sequence-to-Sequence Applications

Real-world applications: machine translation, summarization, question answering, and chatbots.

⏱️ 60 minutes

📅 Future

🚀 Module IV: Advanced Deep Learning

10 Lectures • 10 Hours

Explore cutting-edge topics: GANs, Transfer Learning, Reinforcement Learning, PINNs, and emerging architectures

Progress: 0/10 lectures available 0% Complete

📋 Planned

Lecture 36

🔄 Transfer Learning & Fine-tuning

Leveraging pre-trained models: transfer learning strategies, fine-tuning, and domain adaptation.

🧬 Autoencoders & Representation Learning

Unsupervised learning: encoders, decoders, dimensionality reduction, and feature learning.

🎭 Generative Adversarial Networks

GANs fundamentals: generator-discriminator dynamics, training challenges, and basic GAN architectures.

🎨 Advanced GANs & Applications

StyleGAN, DCGAN, conditional GANs, and creative applications of generative models.

🎮 Deep Reinforcement Learning

RL fundamentals: Q-learning, policy gradients, and deep Q-networks for intelligent agents.

🏆 Advanced RL Algorithms

Actor-critic methods, PPO, A3C, and modern reinforcement learning algorithms.

⚗️ Physics-Informed Neural Networks

Cutting-edge fusion of physics and AI: solving PDEs, enforcing physical constraints, and scientific computing.

👁️ Vision Transformers & Modern Architectures

Transformers for vision: ViT, DETR, and the latest architectural innovations across domains.

🩺 AI in Healthcare Applications

Deep learning for medical imaging, diagnostics, drug discovery, and clinical decision support systems.

🌐 Future of Deep Learning

Emerging trends, ethical considerations, interpretability, and the future landscape of AI research.

⏱️ 60 minutes

📅 Future

🧪

Interactive Laboratory Sessions

15 Hands-On Labs • Google Colab Ready • Progressive Difficulty • Real Neural Networks

🚀 Lab Experience

Transform theory into practice! Our interactive labs let you build neural networks from scratch, solve real AI problems, and see the math come alive through hands-on coding.

✅ Available Now

🐍 Lab 0: Python & Environment Setup

Your journey into AI starts here. Master the tools of the trade.

2 Hours

Duration

5 Exercises

Hands-On

Beginner

Level

📁 View Files

🎯 You'll Build: Complete Python & Environment Setup, NumPy operations, Pandas and Matplotlib

✅ Available Now

📊 Lab 1: Python Foundations & First Neural Network

Uncover stories hidden in data. Master Pandas, Matplotlib, and Seaborn to preprocess, analyze, and beautifully visualize a real-world dataset from scratch.

2 Hours

Duration

8 Exercises

Hands-On

Beginner

Level

📁 View Files

🎯 You'll Build: A complete data analysis pipeline, data cleaning functions, multiple professional plots (histograms, scatter plots, heatmaps), and initial feature engineering.

✅ Available Now

📊 Lab 2: Data Science Foundations

Uncover stories hidden in data. Master Pandas, Matplotlib, and Seaborn to preprocess, analyze, and beautifully visualize real-world datasets from scratch.

2 Hours

Duration

7 Exercises

Hands-On

Beginner

Level

📁 View Files

🎯 You'll Build: A fully functional Perceptron from scratch, a complete gradient descent algorithm, and a model that can solve logical problems.

✅ Available Now

🧮 Lab 3: Neural Network from Scratch

Build a brain from zero. Implement forward and backward propagation, and code gradient descent using only Python and NumPy to truly understand how AI learns.

2 Hours

Duration

6 Exercises

Hands-On

Beginner

Level

📁 View Files

🎯 You'll Build: Complete neural network from scratch, custom activation functions, gradient descent optimization, and deep understanding of backpropagation

✅ Available Now

🧮 Lab 4: MNIST Digit Classification

Teach a computer to read handwriting. Build, train, and evaluate your first serious neural network using TensorFlow and Keras on the world-famous MNIST dataset.

2 Hours

Duration

6 Exercises

Hands-On

Beginner

Level

🚀 Interactive Lab

🎯 You'll Build: A complete image classification neural network, robust training and evaluation loop, and visualizations of your model's performance and accuracy

✅ Available Now

🏛️ Lab 5: Hyperparameter Optimization

Become the master chef of AI: Finding the perfect recipe for your model. Learn to optimize learning rates, network architecture, and training parameters using automated tools like Optuna.

3 Hours

Duration

8 Exercises

Hands-On

Intermediate

Level

📁 View Lab

🎯 You'll Build: Automated hyperparameter optimization systems, performance comparison dashboards, and production-ready model tuning workflows

✅ Available Now

🖼️ Lab 6: CNN for Image Recognition

Master convolutional neural networks for image classification. Build and train CNNs from scratch, understand filters, feature maps, and modern architectures like LeNet and AlexNet.

4 Hours

Duration

10 Exercises

Hands-On

Intermediate

Level

📁 View Lab

🎯 You'll Build: Complete CNN architectures, custom image classifiers, feature visualization systems, and performance optimization techniques

✅ Available Now

🔄 Lab 7: Transfer Learning Project

Master transfer learning with pre-trained models for advanced computer vision tasks.

3 Hours

Duration

Advanced

Level

📁 View Lab

🎯 You'll Build: Custom image classifier using transfer learning

✅ Available Now

🎯 Lab 8: Object Detection System

Build real-world object detection systems with YOLO and advanced computer vision.

4 Hours

Duration

Advanced

Level

📁 View Lab

🎯 You'll Build: Real-time object detection application

✅ Available Now

📝 Lab 9: RNN for Text Processing

Master recurrent neural networks for natural language processing and text analysis.

3.5 Hours

Duration

Advanced

Level

📁 View Lab

🎯 You'll Build: Text classification and language modeling systems

✅ Available Now

💭 Lab 10: Sentiment Analysis Application

Build production-ready sentiment analysis systems for social media and customer feedback.

3 Hours

Duration

Advanced

Level

📁 View Lab

🎯 You'll Build: Real-time sentiment analysis web application

✅ Available Now

🎨 Lab 11: Generative AI Project

Explore generative AI with GANs, VAEs, and cutting-edge generative models.

4.5 Hours

Duration

Expert

Level

📁 View Project

🎯 You'll Build: Image generation system with GANs

✅ Available Now

🚀 Lab 12: Final AI Application

Capstone project: Build a complete end-to-end AI application integrating all concepts.

6 Hours

Duration

Expert

Level

📁 View Project

🎯 You'll Build: Complete production-ready AI application

🎓 How to Use the Labs

🚀

1. Quick Start

Click "Open in Colab" → Save copy to Drive → Enable GPU → Start coding!

📚

2. Learn by Doing

Complete exercises step-by-step, run code, see immediate results, build understanding.

🧠

3. Build Neural Networks

Create AI from scratch, solve real problems, understand how deep learning works.

💡 Pro Tip: Complete each lab after finishing the corresponding lectures for maximum learning impact!

🎓 Created by Dr. Daya Shankar

📚 Comprehensive 45-lecture course covering theoretical foundations and practical implementations

📖 Reference Materials:

• Ian Goodfellow et al. - Deep Learning (MIT Press)
• Michael Nielsen - Neural Networks and Deep Learning
• Josh Patterson & Adam Gibson - Deep Learning: A Practitioner's Approach
• Additional resources: GeeksforGeeks, NPTEL Lectures, Latest Research Papers

// ...