Foundation

1- Introduction to Deep Learning and its Significance in AI 2- Neural Network Basics 3- Forward and Backward Propagation, Loss Functions 4- Vectorization and Efficient Computation 5- Tools and Frameworks 6- Hyperparameter Tuning Techniques 7- Regularization Methods 8- Optimization Algorithms 9- Batch Normalisation and Gradient Clipping 10- Transfer Learning and Fine Tuning 11- CNN Fundamentals 12- Popular Architectures 13- Advanced CNN Topics 14- Applications 15- Recurrent Neural Networks 16- Attention Mechanisms and Transformer Architecture 17- Self Supervised Learning with Transformers 18- Applications: NLP, Machine Translation, Speech Recognition 19- Generative Adversarial Networks (GANs) and Training Challenges 20- Variational Autoencoders (VAEs) and Latent Space Representations 21- Diffusion Models and Energy Based Models 22- Few Shot and Zero Shot Learning, Foundation models 23- Explainability and Interpretability in Deep Learning 24- Basics of Graph Theory and Graph Neural Networks (GNNs) 25- GNN Variants 26- Applications in Social Networks, Chemistry, and Recommendation Systems 27- Data Preparation, Augmentation, and Pipeline Structuring 28- Model Evaluation Metrics and Error Analysis 29- Deployment Strategies 30- Real World Case Studies 31- Foundation