About This Program
PROGRAM OVERVIEW
Deep Learning is a specialized subset of Machine Learning based on artificial neural networks, enabling cutting-edge AI. Sub-fields include Natural Language Processing (NLP), Computer Vision, Generative AI, and Reinforcement Learning.
As the driving force behind the current AI revolution, this field offers unprecedented future-proofing and high compensation. The curriculum includes building neural networks from scratch, utilizing PyTorch/TensorFlow, fine-tuning LLMs, and deploying production-grade image recognition models.
Skills & Outcomes
WHAT YOU'LL LEARN
Neural Networks
Build ANNs, CNNs, and RNNs from scratch using PyTorch and TensorFlow — tools used by the world's top AI labs.
Natural Language Processing
Build 10+ real NLP applications — text classifiers, chatbots, and sentiment analyzers — for a standout portfolio.
Computer Vision
Train image classification, object detection, and segmentation models using real-world datasets and modern architectures.
Generative AI & LLMs
Fine-tune large language models, build RAG pipelines, and ace your technical interviews with dedicated mock sessions.
PyTorch & TensorFlow
Get hands-on with both major deep learning frameworks and learn when and how to use each for maximum efficiency.
Model Optimization & Deployment
Optimize models with quantization, pruning, and distillation, then deploy them to production using cloud APIs.
Course Structure
CURRICULUM
01.
Fundamentals & Foundations
Python & NumPy for Deep Learning
Mathematics for DL (Linear Algebra, Calculus, Probability)
Introduction to Neural Networks & Backpropagation
Activation Functions, Loss Functions & Optimizers
PyTorch & TensorFlow Setup & Basics
02.
Core Architectures & Computer Vision
Convolutional Neural Networks (CNNs)
Transfer Learning & Pre-trained Models (ResNet, VGG, EfficientNet)
Object Detection (YOLO, Faster R-CNN)
Recurrent Neural Networks & LSTMs
Attention Mechanisms & Transformers
03.
NLP, Generative AI & Advanced Topics
NLP with Transformers (BERT, GPT, T5)
Fine-tuning Large Language Models (LLMs)
Retrieval-Augmented Generation (RAG) Pipelines
Generative Models (GANs, Diffusion Models)
Reinforcement Learning Fundamentals
04.
Capstone Project & Placement Prep
End-to-End Deep Learning Project (CV or NLP)
Model Optimization, Quantization & Deployment
Mock Interview Sessions
Resume & LinkedIn Optimization
Placement Drive & Referrals
ENROLLMENT OPEN
Limited seats left for the upcoming batch.
Starts On
June 15, 2026
Format
Live Online
Duration
12 Weeks
Batch Size
30 Students Max
Language
English
Level
Beginner to Pro
Applications close in 5 days. No upfront payment required.
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