Fundamentals of Deep Learning

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Fundamentals of Deep Learning



This course provides a structured introduction to the core principles and applications of deep learning. Participants will begin by exploring the mechanics of neural networks and progressively advance to convolutional neural networks, data augmentation, and transfer learning techniques. The curriculum emphasizes both theory and practical implementation, including the use of pre-trained models, recurrent networks for sequential data, and large language models for text-based tasks. Learners will apply these skills in a final project focused on object classification, reinforcing their ability to build, train, and optimize deep learning models for real-world applications. The course concludes with a comprehensive review, assessment, and guidance on setting up AI application development environments.

Course Fee

₹45,000 (including GST)

Duration

8 hours

Mode

Online/Hybrid


Program CURRICULUM
Module 1
The Mechanics of Deep Learning
  • Explore the fundamental mechanics and tools involved in successfully training deep neural networks
  • Train your first computer vision model to learn the process of training.
  • Introduce convolutional neural networks to improve accuracy of predictions in vision applications.
  • Apply data augmentation to enhance a dataset and improve model generalization.
Module 2
Pre-trained Models and Large Language Models
  • Leverage pre-trained models to solve deep learning challenges quickly. Train recurrent neural networks on sequential data.
  • Integrate a pre-trained image classification model to create an automatic doggy door.
  • Leverage transfer learning to create a personalized doggy door that only lets in your dog.
  • Use a Large Language Model (LLM) to answer questions based on provided text.
Module 3
Final Project: Object Classification
  • Apply computer vision to create a model that distinguishes between fresh and rotten fruit.
  • Create and train a model that interprets color images
  • Build a data generator to make the most out of small datasets
  • Improve training speed by combining transfer learning and feature extraction
  • Discuss advanced neural network architectures and recent areas of research where students can further improve their skills.
Module 4
Final Review
  • Review key learnings and answer questions.
  • Complete the assessment and earn a certificate
  • Complete the workshop survey
  • Learn how to set up your own AI application development environment

Details

Course Information

Duration: 8 Hours

Mode: On-line / Hybrid

Price: ₹45,000 (including GST)

For Whom

  • Beginners in Deep Learning and AI
  • Data Scientists and Analysts transitioning into Deep Learning
  • Machine Learning Engineers seeking practical model-building experience
  • Researchers and Academics exploring neural network applications
  • Graduate Students in Computer Science, AI, or related fields
  • Developers interested in applying transfer learning and pre-trained models