Deep Learning, NLP & Large Language Models

Program Overview

Go beyond the surface of AI. Master the deep mathematics and engineering behind deep neural networks, transformer architectures, self-attention, Hugging Face pipelines, and custom language model training on GPU clusters.

What You Will Master:

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Master PyTorch tensors, autograd, custom layers, loss functions, and optimizers

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Implement CNNs and Vision Transformers (ViT) for advanced computer vision

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Deconstruct the Transformer architecture from scratch: Multi-head attention & positional encoding

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Train, fine-tune, and evaluate NLP models with Hugging Face Transformers & Datasets

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Implement parameter-efficient adaptation (LoRA, QLoRA) on custom domain text

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Deploy distributed deep learning inference with vLLM, TensorRT, and Docker

Industry-Aligned Curriculum

Designed with tech leads to bridge theory with real enterprise engineering demands.

5 Modules • 16 Weeks Structured Track • Hands-On Live Coding Labs

01 Deep Learning Foundations & PyTorch Mechanics

Weeks 1-3

Neural network math: Forward propagation, backpropagation, and gradient descent
PyTorch deep dive: Tensors, autograd, nn.Module, Dataset, and DataLoader
Activation functions, weight initializations, and regularization (Dropout, BatchNorm)
Loss functions and optimization algorithms: AdamW, RMSProp, and learning rate schedulers

02 Computer Vision & Convolutional Networks

Weeks 4-6

Convolution layers, pooling, receptive fields, and feature map visualization
Architectures: ResNet, EfficientNet, and transfer learning in PyTorch
Vision Transformers (ViT) vs. CNNs: Patch embeddings and self-attention on images

03 Natural Language Processing & The Transformer

Weeks 7-10

Text tokenization: WordPiece, Byte-Pair Encoding (BPE), and vocabulary building
Attention is All You Need: Scaled dot-product attention, multi-head attention, and masking
Building an encoder-decoder and decoder-only transformer from scratch in PyTorch
Hugging Face Transformers: AutoModel, AutoTokenizer, and Trainer API

04 Large Language Models, PEFT & Quantization

Weeks 11-13

Modern LLMs: Llama, Mistral, Gemma architectures and rotary positional embeddings (RoPE)
Fine-tuning techniques: Full fine-tuning vs. LoRA, QLoRA, and Prefix Tuning
4-bit and 8-bit quantization mechanics, memory footprint calculation, and flash attention
Direct Preference Optimization (DPO) and RLHF concepts for model alignment

05 Inference Optimization, Deployment & Capstone

Weeks 14-16

High-throughput model serving with vLLM, TensorRT-LLM, and Triton Inference Server
KV caching, continuous batching, and speculative decoding techniques
Capstone project defense, code review, and senior AI engineer placement support

Production Capstone Projects

Build verified GitHub repository projects to showcase in your portfolio and resume.

Healthcare AI

Custom Medical Diagnostic NLP Classifier

Domain-adapted ClinicalBERT model fine-tuned on medical transcription notes with multi-label classification and confidence calibration.

Core Research

Mini-Transformer LLM Trained from Scratch

Complete decoder-only language model built in pure PyTorch with byte-pair encoding (BPE), multi-head attention, and cross-entropy training.

Multimodal AI

Multimodal Vision-Language Q&A Pipeline

Integration of vision encoders (CLIP) with causal language models to enable zero-shot image understanding and document query answering.

Technologies & Tools You Will Use

Practical mastery on the modern tech stacks demanded by high-growth startups and tech giants.

PyTorch Hugging Face CUDA Python Weights & Biases FastAPI Docker Transformers

Learn from Industry Veteran

Live mentorship and code reviews from practitioners who have shipped software at scale.

Rahul Sharma

AI Architect & Deep Learning Researcher

Specializes in NLP, transformer attention mechanisms, and custom foundation model fine-tuning. Ex-research lead at premier AI labs.

Career Outcomes & Placement Support

Comprehensive career guidance from Day 1 to land competitive tech roles.

Target Roles You Qualify For:

🎓 Deep Learning Engineer 🎓 NLP Specialist 🎓 Applied AI Scientist 🎓 LLM Research Associate

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Resume & LinkedIn Review by hiring managers

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1-on-1 Mock Technical & Behavioral Interviews

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Direct Placement Referrals to 500+ Partner Companies

Frequently Asked Questions

Got questions about admissions, batch schedules, or refunds? Here are quick answers.

Who is eligible to join this course?

College students, fresh graduates, and working professionals looking to upskill or transition into high-paying tech careers are all eligible. Basic familiarity with computer operations is sufficient; foundational concepts are thoroughly taught in the initial modules.

What happens if I miss a live class?

All live lectures are recorded in high-definition and uploaded to your student portal within 2 hours. You retain lifetime access to recordings, lecture slides, code repositories, and batch discussion forums.

How does the monthly EMI payment work?

We offer flexible zero-cost and low-cost monthly EMI options starting at just ₹2,666 / month. You can split the tuition without financial stress and focus 100% on learning.

Will I receive a recognized certificate?

Yes! Upon completing the syllabus and submitting the required capstone projects, you will receive an industry-recognized Certificate of Completion from Talvion IT Academy with a verifiable credential ID for LinkedIn.