Duration 3 days – 21 hrs
Overview
This course is designed for experienced practitioners seeking to master advanced deep learning architectures and methods. Participants will explore Recurrent Neural Networks (RNNs), Long Short-Term Memory networks (LSTMs), Generative Adversarial Networks (GANs), and Transformers. The course also covers transfer learning, fine-tuning, and customizing deep learning models for complex real-world tasks in domains such as NLP, computer vision, and generative AI.
Objectives
- Understand and implement advanced architectures like RNNs, LSTMs, GANs, and Transformers
- Apply transfer learning and pretrained models to accelerate development
- Fine-tune deep learning models for specific applications and datasets
- Use modern frameworks (e.g., TensorFlow, PyTorch, Hugging Face) for building and optimizing custom models
- Evaluate and improve model performance on sequential, image, or language data
Audience
- Deep learning practitioners, AI engineers, and data scientists
- Researchers and developers working with NLP, time series, image generation, or sequential models
- Technical professionals aiming to build state-of-the-art AI systems
- Anyone with prior deep learning experience looking to apply and customize advanced techniques
Prerequisites
- Strong Python programming skills
- Proficiency with deep learning concepts and frameworks (e.g., CNNs, Keras, PyTorch, TensorFlow)
- Completion of a foundational deep learning or neural network course
- Experience with training and evaluating ML models
Course Content
Day 1: Sequence Models – RNNs and LSTMs
- Understanding sequence modeling and time-series use cases
- Implementing RNNs and LSTMs in TensorFlow or PyTorch
- Applications: sentiment analysis, language modeling, anomaly detection
- Hands-on: Build and train an LSTM for text classification
Day 2: GANs and Transformers
- Generative Adversarial Networks (GANs): architecture, training, and use cases
- Implementing a basic GAN for image generation
- Transformers: self-attention, encoder-decoder architecture, applications in NLP
- Hands-on: Fine-tune a transformer model (e.g., BERT or GPT-based) using Hugging Face
Day 3: Transfer Learning & Model Customization
- Transfer learning principles and benefits
- Using pretrained CNNs and transformer models (ResNet, BERT, etc.)
- Fine-tuning models for specific tasks (domain adaptation, small dataset training)
- Final project: Build a complete application using advanced deep learning techniques
