Modern Deep Learning Syllabus with Modules

Quick overview of the modules of Deep Learning syllabus: 

• Neural Network Basics: Perceptrons, backpropagation, and gradient descent form the foundational concepts taught at the beginning of any deep learning syllabus. 
• Advanced Architectures: Includes CNNs for computer vision, RNNs and LSTMs for sequence modeling, and Transformers for modern large‑scale language and vision tasks. 
• Optimization Techniques: Covers essential optimizers such as Adam and RMSProp for stabilizing and speeding up model training. 
• Regularization Methods: Dropout and Batch Normalization are introduced to reduce overfitting and improve generalization. 
• Generative Models: Students explore GANs, VAEs, and related architectures used for generating images, audio, and text. 
• Applications: Practical exposure in Computer Vision and NLP tasks forms a major part of the curriculum. 
• Programming Frameworks: Hands‑on implementation using Python libraries like TensorFlow and PyTorch. 

In this guide, you’ll learn how a deep learning syllabus moves from core neural network basics to advanced architectures, optimization, regularization, and generative models, while using TensorFlow and PyTorch to build practical CV and NLP projects. 

To build these skills yourself, explore upGrad’s Deep Learning courses and learn how modern AI systems are built. Also, consider advancing further with the Executive Post Graduate Certificate in Generative AI & Agentic AI from IIT Kharagpur to gain hands-on experience with AI systems. 

Neural Network Basics 

This module builds conceptual clarity about how neural networks represent functions and learn from data using gradients. It sets the stage for all later topics by grounding learners in perceptrons, forward/backward passes, and core training mechanics. 

Perceptrons and Multilayer Perceptrons (MLPs) 

Perceptrons are the simplest neural units that linearly separate data using weights and a bias term. Extending to multilayer perceptrons (MLPs) introduces hidden layers and nonlinear activations, allowing networks to model complex functions beyond linear separability. You’ll understand the forward pass (how inputs transform through weighted sums and activations), decision boundaries in low‑dimensional spaces, and practical scenarios where MLPs excel, primarily tabular datasets and baseline classification or regression problems. 

Key outcomes: 

• Distinguish single‑layer vs. multilayer setups and when each is appropriate. 
• Interpret neurons, weights, bias, and the role of activation in introducing nonlinearity. 
• Build baseline MLPs and evaluate their limitations on high‑dimensional image/text data. 

Backpropagation and Gradients 

Backpropagation uses the chain rule to compute partial derivatives of the loss with respect to each parameter, enabling gradient‑based updates. You’ll develop an intuition for how gradients flow through layers, why weight updates converge, and the symptoms of vanishing/exploding gradients in deep networks. Practical guidance includes choosing sensible initializations, normalizing inputs/activations to stabilize learning, and reading/debugging loss curves to diagnose training issues. 

Key outcomes: 

• Compute gradient flow conceptually and relate it to parameter updates. 
• Recognize gradient pathologies and apply fixes (init schemes, normalization). 
• Use loss/metric curves for early diagnostics and course correction. 

Gradient Descent Family 

In the deep learning syllabus, you will compare batch, stochastic, and mini‑batch gradient descent, understanding trade‑offs between speed, noise, and stability. Learning rate schedules (step, cosine, warm‑up) and practical heuristics (gradient clipping, early checks on batch loss) help you train models more reliably. 

Key outcomes: 

• Select an update scheme suited to dataset size and computational budget. 
• Apply learning‑rate warm‑up and decay to improve convergence. 
• Implement simple guardrails that prevent divergence early. 

Optimization Techniques 

Good models need good optimization. This section helps you pick and tune optimizers/schedules that converge reliably and generalize well. 

Adam and RMSProp in Practice 

Adam and RMSProp use adaptive learning rates and momentum‑like terms (with bias correction in Adam) to speed convergence on noisy objectives. You’ll learn sensible defaults, when to tune β‑parameters or ε, and why SGD with momentum can sometimes generalize better on large‑scale vision tasks. 

Key outcomes: 

• Compare adaptive vs. non‑adaptive optimizers for different data regimes. 
• Tune critical hyperparameters and recognize over‑adaptation signs. 
• Switch to SGD at the right time to improve generalization, if needed. 

Scheduling and Stabilization 

Schedules such as step decay, cosine annealing, cyclic policies, and one‑cycle can significantly influence convergence speed and final accuracy. Stabilization tactics, gradient clipping, mixed‑precision training, and early detection of divergence (spiking loss, exploding norms), ensure smoother training. 

Key outcomes: 

• Attach the right schedule to the right optimizer/task. 
• Implement clipping and FP16/AMP safely without degrading accuracy. 
• Act quickly when instability appears (reduce LR, increase warm‑up, check batch norm stats). 

Regularization Methods 

Prevent overfitting and improve robustness. You’ll combine architectural, weight‑level, and data‑level regularizers for better validation performance. 

Dropout 

Dropout randomly “thins” neurons during training, mitigating co‑adaptation. You’ll learn where to place dropout (dense layers, selective use in CNNs/RNNs), typical probability ranges (e.g., 0.1–0.5), and how to tune it alongside weight decay and data augmentation. 

Key outcomes: 

• Use dropout judiciously without harming gradient flow. 
• Balance dropout with other regularizers for best validation performance. 

Batch Normalization and Alternatives 

Batch Normalization stabilizes activations and gradients, accelerating training—particularly in CNNs. You’ll contrast BN with LayerNorm/GroupNorm (common in Transformers and small‑batch regimes), and learn training vs. inference behavior (running stats, freezing BN) and pitfalls (tiny batch sizes, distribution shift). 

Key outcomes: 

• Pick the right normalization for architecture and batch size. 
• Handle BN correctly during fine‑tuning and inference. 

Data‑Level Regularization 

Applying augmentations (flip, crop, color jitter, mixup, cutmix) to expand effective data diversity, plus label smoothing to soften targets and L2 weight decay for smoother parameter landscapes is included in the Deep Learning syllabus. Early stopping with a patience window provides a safety net against overfitting. 

Key outcomes: 

• Build robust augmentation pipelines matched to task and data scale. 
• Combine label smoothing and weight decay without underfitting. 
• Use early stopping criteria that respect metric variance. 

Generative Models 

Learn how models create data. Compare GANs and VAEs pragmatically, with a quick look at diffusion trends and ethics. 

GANs (Generative Adversarial Networks) 

You’ll study the generator–discriminator min‑max game, losses (e.g., non‑saturating, hinge), and training pathologies like mode collapse and instability. Stabilization tricks include label smoothing, spectral normalization, balanced updates, and careful architecture choices. Applications span image synthesis, super‑resolution, and style transfer. 

Key outcomes: 

• Implement stable training loops that avoid collapse. 
• Evaluate fidelity/diversity and align architecture to the use case. 

VAEs (Variational Autoencoders) 

VAEs model latent distributions via an encoder–decoder with KL‑regularized objectives. You’ll weigh reconstruction quality against sampling diversity, and apply VAEs to anomaly detection, controllable generation, and representation learning where interpretable latents are valuable. 

Key outcomes: 

• Tune β‑VAE style objectives for disentanglement vs. fidelity. 
• Choose latent dimensionality and priors for downstream needs. 

When to Use GANs vs. VAEs

You’ll use a simple selection lens: GANs for high‑fidelity outputs, VAEs for smooth latent spaces and control, and diffusion models as modern, stable generators with strong diversity. Brief coverage of ethical issues (misuse, bias, watermarking) rounds out responsible generative AI practice. 

Key outcomes: 

• Select the right generative family to balance fidelity, diversity, and controllability. 
• Incorporate safety measures and disclosure in generative workflows. 

Applications 

Translate learning into real projects. You’ll scope tasks, pick metrics, and think like a practitioner from dataset to deployment. 

Computer Vision Projects 

You’ll progress from a classification baseline (training from scratch) to transfer learning and fine‑tuning pretrained models. Advanced tasks include object detection (Faster R‑CNN, YOLO series) and segmentation (U‑Net, Mask R‑CNN). Evaluation covers accuracy, mAP, IoU, plus dataset hygiene, augmentation policy, and class‑imbalance handling. 

Key outcomes: 

• Ship a strong transfer‑learning baseline quickly. 
• Diagnose failure modes using per‑class metrics and confusion matrices. 

NLP Projects 

Projects include text classification, NER, Q&A, summarization, and generation. You’ll handle tokenization, embeddings, and prompt‑based or parameter‑efficient fine‑tuning for foundation models. Evaluation uses F1 and BLEU/ROUGE, with emphasis on domain shift handling and data curation. 

Key outcomes: 

• Build end‑to‑end NLP pipelines with modern tokenizers and adapters. 
• Compare classical metrics and choose the right one per task. 

End‑to‑End Workflow and MLOps Basics 

Cover data pipelines, experiment tracking, and versioning; packaging models for serving (REST/gRPC) with latency–throughput trade‑offs; and live monitoring for drift with policies for periodic re‑training. 

Key outcomes: 

• Maintain reproducible experiments and model lineage. 
• Operate models in production with alerting and retrain triggers. 

Programming Frameworks 

Implement everything hands‑on with Python, TensorFlow/Keras, and PyTorch. Emphasis on clean code, scaling, and deployable artifacts. 

Python Foundations for DL 

Use numpy/pandas/matplotlib for data prep and EDA. Set up virtual environments for reproducibility, and decide when to use notebooks vs. scripts. Write clean training loops with utility modules for datasets, metrics, logging, and checkpoints. 

Key outcomes: 

• Create reproducible, well‑structured DL projects. 
• Automate common tasks and enforce coding hygiene. 

Building with TensorFlow/Keras 

Work with Sequential vs. Functional APIs, callbacks (ModelCheckpoint, EarlyStopping), and TensorBoard. Use TF Datasets, mixed precision, and distribution strategies for multi‑GPU/TPU. Export SavedModel for deployment, with a brief overview of TF‑Lite for on‑device inference. 

Key outcomes: 

• Compose complex models with the Functional API. 
• Scale training and prepare models for serving/mobile. 

Building with PyTorch 

Master tensors, autograd, and the nn module. Build DataLoaders and training loops, and optionally adopt Lightning/Accelerate for boilerplate reduction. Export to TorchScript or ONNX and optimize inference with backends like Torch‑TensorRT. 

Key outcomes: 

• Implement flexible training pipelines quickly. 
• Optimize models for production‑grade latency and portability. 

Conclusion 

The deep learning syllabus provides a clear, structured path from core neural network concepts to advanced architectures, optimization methods, and hands‑on applications. By combining theory with practical projects in computer vision, NLP, and modern frameworks like TensorFlow and PyTorch, it equips learners with the skills to design, train, evaluate, and deploy reliable deep learning models for real‑world use.