Neural NetworksComputer VisionTransfer LearningEdge AI & NPU
Deep Learning
This course takes you from AI fundamentals to building and applying intelligent Deep Learning solutions for real-world applications.
6mo
Course duration
4mo
Teaching
2mo
Projects
+6mo
Employment Training · optional
Taught in person at Jubilee Hills, Hyderabad. How to reach us
The curriculum
TeachingWeeks 1-16 · 4 mo
ProjectWeeks 17-24 · 2 mo
Employment Training Programme, add-onWeeks 25-48 · 6 mo
10 Modules · Teaching Weeks 1-16
- Introduction to Artificial Intelligence-AI
- Introduction to Machine Learning-ML
- Introduction to Deep Learning-DL
- Perceptron and Multilayer Perceptron-MLP
- Artificial Neural Networks
- Shallow and Deep Neural Networks
- Supervised Learning and Unsupervised Learning
- Training and Inference
- Applications
- Introduction to Reinforcement Learning-RL
- Fundamentals of Python Programming Language
- Functions and User Defined Functions
- Libraries for Data Science and Deep Learning
- Available Programs and Licenses
- Advantages of LLMs for Python Programming
- Graphical User Interface
- Technical Computing
- Python and OpenCV
- Digital Image Processing
- Types of Images
- Conversion
- Digital Image Filters
- Camera and Specifications
- Computer Vision
- Object Location Identification
- Convolutional Neural Networks
- Feature Extraction using Kernels
- Important Layers in Deep Learning for Classification
- FC Layer in DL Architecture
- Serialization and Activation Functions
- FC Layer Inputs and Outputs
- Training and Inference
- Parameters and Hyperparameters
- Hyperparameter Tuning
- Deep Learning for Regression
- Comparison between Classification and Regression
- Converting Classification Architecture to Regression and vice versa
- Available Datasets
- Open Sourced and Licenses
- Preprocessing, Training and Inference
- DL for Own Datasets
- When to choose ML and DL
- How much data do we need in DL
- Preprocessing Data and Accuracy Dependency
- Studying and Understanding Popular DL Architectures
- Comparison and Applications
- Transfer Learning and why & when to use it
- Dataset size, Training and Accuracy considerations
- Advantages of Transfer Learning
- Comparison between CPU, GPU, TPU and NPU
- Supported AI Operations by Edge AI Devices
- Dataset size and the CNN Architecture
- Training and Importance of Optimization
- Model Conversion and Deployment
- Transfer Learning Implementation for Edge AI Devices
- Memory Limitation of Edge AI Devices
- AI Accelerators and Neural Processing Unit-NPU
- Necessity of NPU Accelerators for Real Time Applications
- Supervised Learning with ML
- Dataset size requirement for DL and ML
- Requirement of Domain Knowledge for ML
- Training Algorithms and Comparison
- Inference with Trained Models
- Advantages of ML over DL
- Collecting Real Data from Sensors
- Preprocessing and Preparing the Dataset
- Training and Inference using Real Data
- Importance of Mathematical Models
- Importance of Simulation
- Collecting Data from Simulation
- Training and Inference using Simulation Data
- Synthetic Data Availability
- Training and Inference using Synthetic Data
- Present Applications of DL in Medical
- Future Applications of DL in Medical
- Present Applications of DL in Autonomous Systems
- Future Applications of DL in Autonomous Systems
Project phase · Weeks 17-24
- Minor Project: Design, Development, and Implementation
- Major Project: Real-time AI Application
Employment Training Programme, add-on · Weeks 25-48
The six-month course finishes with sixteen weeks of teaching and a minor and a major project. A six-month Employment Training Programme of real-time project work is available after that as an add-on, on merit basis.
Additional skills training
Along with the technical curriculum, every learner gets dedicated training in professional and career skills.
English Language SkillsImprove grammar, vocabulary, reading, writing, and workplace English.
Communication SkillsDevelop effective verbal and written communication.
Soft Skills DevelopmentBuild confidence, teamwork, problem-solving, and interpersonal skills.
Public Speaking & Presentation SkillsImprove presentation, speaking, and audience-engagement skills.
Resume PreparationCreate professional, industry-ready resumes.
Interview PreparationLearn interview techniques, question handling, and professional responses.
Mock InterviewsParticipate in practice interviews with feedback and performance evaluation.
5 Real-World Interview OpportunitiesGet opportunities to attend five actual industry interviews.
Career Guidance & Placement PreparationReceive guidance for career planning, job opportunities, and placement readiness.
The campus, Jubilee Hills.
Classrooms, a robotics lab and rooms named after the pioneers. Inaugural batches sit here in person.
Ready to join the next batch?
Train the way Hyderabad hires: project-first, certification-aligned and reviewed by people who do this for a living.
Book a free demo