D24
Advanced SpecializationData & AIAdvanced10 weeks
Deep Learning
Deep Learning is a 10-week advanced course teaching neural network fundamentals, backpropagation, CNNs, transfer learning, RNNs/LSTMs, transformer architectures, and generative models like GANs and diffusion, all built in PyTorch. Aimed at learners with Python and basic ML experience, it covers deploying models via ONNX and TorchServe, resulting in projects like an image classifier, a fine-tuned transformer, and a real-time object detection app.
Build a solid foundation in neural networks, then design, train, and deploy CNNs, sequence models, transformers, and generative architectures using PyTorch.
What you'll learn
Neural network fundamentals: backpropagation, optimizers, and regularization in PyTorch
Convolutional networks for image classification and transfer learning
Sequence models and transformer architectures for text and vision tasks
Training generative models (GANs, diffusion) and deploying models to production
Course details
Duration
10 weeks
Best for
Learners with Python and basic machine learning experience who want to build and deploy neural networks in PyTorch
Enrollment isn't open for this course yet — check back soon or contact us.
Course Curriculum
A module-by-module breakdown of what you'll cover in Deep Learning.
01Getting Started & Environment Setup
4 topicsWeeks 1–2
- Installing PyTorch and verifying GPU/CPU detection
- Creating free Hugging Face and Weights & Biases accounts
- Installing ONNX Runtime for the deployment module
- Verifying your setup with a basic tensor operation
02Basic
4 topicsWeek 3
- Linear algebra and calculus for deep learning
- Tensors, autograd, and manual backpropagation
- Building models with PyTorch's nn.Module
- GPU/CPU device management
03Intermediate
4 topicsWeeks 4–5
- Perceptrons, MLPs, and activation functions
- Loss functions and optimizers (SGD, Adam, RMSProp)
- Dropout, normalization, and early stopping
- Hyperparameter search and experiment tracking
04Advanced
6 topicsWeeks 6–7
- Convolution and pooling operations
- Transfer learning with ResNet/EfficientNet
- Data augmentation for vision tasks
- Visualizing model predictions with Grad-CAM
- Vanilla RNNs and the vanishing-gradient problem
- LSTM and GRU gating mechanisms
05Pro
7 topicsWeek 8
- Self- and multi-head attention
- Transformer and vision-transformer architectures
- Fine-tuning pretrained transformers
- GAN and diffusion model fundamentals
- Model export to ONNX and quantization
- Serving models with TorchServe/TF Serving
- Distributed and multi-GPU training basics
06AI Pro
3 topicsWeeks 9–10
- Using Claude, ChatGPT, and Perplexity for deep learning development
- Verifying AI-suggested model fixes with real training runs
- Project: Use AI to diagnose an overfitting model, then verify with isolated fixes
Tools & Technologies
Hands-on practice with the same tools used by working professionals.
PyTPyTorch
HugHugging Face Transformers
DifDiffusers
WeiWeights & Biases
ONNONNX Runtime
TenTensorRT
RayRay Tune / Optuna
GitGitHub Copilot
ChaChatGPT
ClaClaude
PerPerplexity
Real-Time, Industry-Based Projects
This is project-based learning modeled on real industry scenarios — you'll build portfolio-ready work as you go, not just watch lectures.
Image classifier built with transfer learning and wrapped in a web demo
LSTM-based sequence forecaster benchmarked against a classical baseline
Fine-tuned transformer classifier with attention-map visualizations
Small diffusion or GAN image generator evaluated with an FID score
Real-time object detection app using a quantized, ONNX-exported model
Every course includes a guided internship on top of your project work — so you graduate with real experience, not just a certificate.
Communication & Soft Skills
Built into every course, alongside the technical curriculum.
Communication Skills
Practice explaining technical work clearly to teammates, managers, and clients.
Practical AI Skills
Hands-on training in using AI tools well — prompting, everyday workflows, and where AI actually helps versus where it doesn't.
Teamwork & Collaboration
Work in small project teams using real workflows like stand-ups and code reviews.
Problem Solving
Build the habit of breaking down ambiguous problems into clear, solvable steps.
Time Management
Learn to plan, prioritize, and deliver project work against realistic deadlines.
Presentation Skills
Present your project work and results with confidence, as you would to a client.
Resume & Profile Building
Build a strong resume, LinkedIn profile, and project portfolio that recruiters notice.
Interview Preparation
Mock interviews and portfolio reviews to get you ready for real job applications.
Career & Placement Support
100% of students enrolled in Deep Learning get full access to placement support — real tools and guidance, not just a promise. This is support and access, not a guaranteed job outcome.
Resume Builder
A resume auto-built from your real completed projects and certificates, ready to export.
Shareable Portfolio
A public portfolio page showcasing your approved projects, with a link you can share with employers.
Interview Preparation
Guidance on presenting your projects and experience clearly in real interviews.
Peer & Mentor Community
Ongoing access to our learner community for questions, feedback, and support after you finish.
What Your Certificates Look Like
Real, verifiable certificates with your name, Deep Learning, and a QR code anyone can scan to confirm they're genuine — one for completing the course, and a separate one if you take the internship track.
Course Completion Certificate
SAMPLE
Data24ZoneCertificate of Completion
Illustrative recipient
Your Name Here
Example certificate for completion of
Deep Learning
- Certificate No.
- D24-SAMPLE
- Mode
- Self-Paced
- Duration
- 10 weeks
- Example issue date
- On completion
Sample only — not a real, issued certificate.
Verification is available only on issued certificates.
Internship Completion Certificate
SAMPLE
Data24ZoneCertificate of Internship
Illustrative recipient
Your Name Here
Example certificate for completion of
Deep Learning
- Certificate No.
- D24-SAMPLE
- Mode
- Internship
- Duration
- 10 weeks
- Example issue date
- On completion
Sample only — not a real, issued certificate.
Verification is available only on issued certificates.
Student Reviews
“The PyTorch-first approach made backpropagation finally click, and the transformer module set me up to fine-tune models confidently at work.”
Arjun Mehta
Machine Learning Engineer
“Going from CNNs to diffusion models in one course felt ambitious, but the project-based structure kept everything grounded and practical.”
Sneha Reddy
Data Scientist
Frequently Asked Questions
What will I learn in the Deep Learning course?
You'll learn: Neural network fundamentals: backpropagation, optimizers, and regularization in PyTorch; Convolutional networks for image classification and transfer learning; Sequence models and transformer architectures for text and vision tasks; Training generative models (GANs, diffusion) and deploying models to production.
What tools and technologies does the Deep Learning course cover?
This course covers: PyTorch, Hugging Face Transformers, Diffusers, Weights & Biases, ONNX Runtime, TensorRT, Ray Tune / Optuna, GitHub Copilot, ChatGPT, Claude, Perplexity.
Is the Deep Learning course beginner-friendly?
This is a advanced-level course, so it assumes some prior familiarity with the subject. It starts with a "Getting Started & Environment Setup" module covering tool installation before the main curriculum.
How long does the Deep Learning course take to complete?
The course is designed to be completed in approximately 10 weeks, depending on your pace.
What enrollment options are available for Deep Learning?
Internship (₹2,499) — Work through real projects with our team and earn an internship certificate (course certificate not included). Self-Paced (₹1,999) — Learn anytime with the full recorded curriculum and community access.
Is there an internship option for Deep Learning?
Yes — the Internship track means working through real projects with our team and earning a separate Internship Certificate.
Will I get a certificate after completing Deep Learning?
Yes. Self-paced enrollment earns a course completion certificate once your final project is reviewed and approved. Internship enrollment earns an internship certificate once your internship projects are approved. Every certificate is publicly verifiable on our website and can be added directly to your LinkedIn profile.
What kind of projects will I build in Deep Learning?
You'll work on real projects such as: Image classifier built with transfer learning and wrapped in a web demo; LSTM-based sequence forecaster benchmarked against a classical baseline; Fine-tuned transformer classifier with attention-map visualizations; Small diffusion or GAN image generator evaluated with an FID score; Real-time object detection app using a quantized, ONNX-exported model.
Is placement support included with Deep Learning?
Yes — every enrolled student gets full access to our placement support: resume building, a shareable project portfolio, mock interview preparation, and guidance applying what you've built to real job applications. This is support and access, not a guaranteed job outcome — how quickly it leads to an offer depends on the market and your own effort.
