Data & AI Training
Deep Learning
Learn neural networks in depth with PyTorch: training, CNNs for vision, sequence models, transformers, transfer learning and practical GPU workflows.
At a glance
- CategoryData & AI Training
- LevelAdvanced
- Curriculum6 modules, 4 project ideas
- InstituteAIIT Roorkee, Roorkee
Course Overview
Deep learning drives modern computer vision, speech recognition and large language models. Understanding how neural networks learn gives you the foundation to work with today's most capable AI systems.
The course covers neural network fundamentals, training techniques, convolutional networks, sequence models, attention and transformers, transfer learning and fine tuning, using PyTorch with practical experiments on images and text.
Learning Objectives
By the end of Deep Learning, learners should be able to:
- Explain forward propagation, backpropagation and optimisation
- Build and train neural networks in PyTorch
- Apply CNNs to image classification and detection tasks
- Understand attention and the transformer architecture
- Fine tune pretrained models for new tasks
Topics Covered
01Neural network foundations
- Perceptrons and activation functions
- Backpropagation
- Loss functions and optimisers
02Training well
- Initialisation and normalisation
- Regularisation and dropout
- Learning rate schedules
03Computer vision
- Convolutions and pooling
- CNN architectures
- Data augmentation
04Sequences
- Embeddings
- RNNs and LSTMs
- Attention
05Transformers
- Self-attention
- Encoder and decoder models
- Pretrained models
06Applied deep learning
- Transfer learning and fine tuning
- Experiment tracking
- Efficient training and deployment
Topic order and depth may be adjusted for the batch, format and learner level.
Practical Learning
Every module pairs explanation with hands-on work. Typical practical activities in this course:
Building a network from tensors upwards
Training experiments with visualised learning curves
Fine tuning pretrained vision and text models
Paper reading sessions on key architectures
Representative Projects
Projects are chosen with mentors based on your level and interests. Examples include:
- Image classifier for a custom dataset
- Text sentiment classifier with transformers
- Object detection prototype
- Research style experiment report
Who Should Join
- Learners who have completed Machine Learning
- Engineering and computer science students
- Researchers working with images, audio or text
- Developers moving into AI engineering
Prerequisites
- Python and NumPy
- Machine Learning basics
- Comfort with basic linear algebra
Learning Format
This course can be offered in the following formats, depending on the batch:
- Short-Term Courses
- Long-Term Courses
- Certification-Oriented Programs
- Weekend Programs
- Workshops
- Live Project Training
- Internship Programs
Ask the team for current batch timings and the formats open for Deep Learning.
Career Relevance
Skills from this course are relevant to roles and directions such as:
- Deep Learning Engineer
- Computer Vision Engineer
- NLP Engineer
- AI Research Assistant
Career relevance describes where these skills are commonly used. It is not a promise of employment. Learners can use AIIT Roorkee's placement assistance for career preparation support.
FAQs
Do I need a powerful GPU?
Not necessarily. Many exercises run on free cloud notebook GPUs, and the course teaches efficient approaches for limited hardware.
Which framework is used?
PyTorch is the main framework, as it is widely used in research and industry. Concepts transfer to other frameworks.
Is Deep Learning required before Generative AI?
Not for using generative AI tools or building LLM applications. It is valuable if you want to understand or fine tune the models themselves.
What is the duration and schedule of this course?
Duration, batch timings and format are shared when you enquire, because they depend on whether you choose a short-term, long-term, weekend or training format. Contact the team by phone, WhatsApp or email for current batches.
Is placement assistance available?
Learners can use AIIT Roorkee's placement assistance, which includes career counselling, CV preparation, LinkedIn development, interview preparation and mock interviews. Placement assistance is career preparation support and does not constitute a guarantee of employment or placement.
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Practical Lab Environment
Deep Learning Developer Console
Enquire
Enquire About This Course
Share your details and the AIIT Roorkee team will contact you about Deep Learning.
- Call +91 7579 1857 75
- Email info@aiitroorkee.com
- #651/55, Ganga Enclave, Near Sainik Colony, Roorkee - 247667, Uttarakhand, India
Deep Learning enquiry
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