Architecting Intelligence: A Comprehensive Guide to Advanced Deep Learning Training Course

Introduction

Deep learning has revolutionized fields from computer vision to natural language processing, but moving beyond foundational concepts requires a deeper understanding of modern architectures. This course is designed for those who have a solid grasp of basic neural networks and are ready to explore the state-of-the-art models that power today's most sophisticated AI applications. You will learn to design, implement, and fine-tune complex architectures, tackling challenging tasks with greater efficiency and accuracy.

This five-day training course provides a hands-on journey into the world of advanced deep learning. You will gain practical experience with transformative models like Transformers for language and attention mechanisms in vision, and you'll learn to handle specialized data types and problems. By the end of this program, you will be equipped to build custom deep learning solutions for a variety of complex real-world challenges.

Duration 5 days

Target Audience This course is for data scientists, machine learning engineers, and researchers who have prior experience with deep learning frameworks like TensorFlow or PyTorch. Participants should be comfortable with building and training basic neural networks.

Objectives

  • To understand the principles behind advanced convolutional neural network (CNN) architectures.
  • To master the implementation of Recurrent Neural Networks (RNNs) and their variants.
  • To explore the revolutionary Transformer architecture and its role in modern AI.
  • To build and apply deep learning models for natural language processing (NLP).
  • To implement and evaluate models for advanced computer vision tasks.
  • To learn how to work with attention mechanisms for improved model performance.
  • To understand the concept of generative models like GANs and VAEs.
  • To gain hands-on experience with transfer learning and fine-tuning pre-trained models.
  • To explore the use of graph neural networks (GNNs) for structured data.
  • To develop a systematic approach to debugging and optimizing deep learning models.

Course Modules

Module 1: Advanced CNN Architectures

  • A deep dive into ResNet, Inception, and DenseNet.
  • Understanding residual connections and their importance.
  • The concept of inception modules for efficient computation.
  • Using depth-wise separable convolutions.
  • A practical comparison of different CNN architectures.

Module 2: Advanced RNN Architectures

  • A review of basic RNNs and their limitations.
  • Understanding Long Short-Term Memory (LSTM) networks.
  • The structure and benefits of Gated Recurrent Units (GRUs).
  • Implementing stacked RNNs and bidirectional models.
  • A hands-on guide to building a sequence-to-sequence model.

Module 3: The Transformer Architecture

  • The shift from recurrent to self-attention mechanisms.
  • Understanding multi-head self-attention.
  • The role of positional encodings.
  • The encoder-decoder structure of the Transformer.
  • A practical walkthrough of a Transformer model.

Module 4: Attention Mechanisms

  • The core idea behind attention in neural networks.
  • Visualizing and interpreting attention weights.
  • Implementing attention for sequence modeling.
  • The use of attention in computer vision.
  • A discussion on the different types of attention.

Module 5: Natural Language Processing (NLP)

  • An overview of the NLP pipeline.
  • Using word embeddings and pre-trained language models.
  • Fine-tuning BERT and GPT-2 for specific tasks.
  • Building a text classification model with a Transformer.
  • A practical guide to building a conversational model.

Module 6: Advanced Computer Vision

  • Object detection with YOLO and Faster R-CNN.
  • The concept of semantic and instance segmentation.
  • Implementing a model for image style transfer.
  • An introduction to 3D vision and point cloud data.
  • A discussion on the challenges of real-world vision tasks.

Module 7: Generative Models

  • The concept of Generative Adversarial Networks (GANs).
  • Understanding the generator and discriminator.
  • An introduction to Variational Autoencoders (VAEs).
  • Building a simple GAN to generate images.
  • A discussion on the applications of generative models.

Module 8: Transfer Learning

  • The concept of using pre-trained models.
  • A practical guide to using VGG16 and ResNet for transfer learning.
  • Strategies for fine-tuning pre-trained models.
  • The benefits and drawbacks of transfer learning.
  • A hands-on project to apply transfer learning to a new dataset.

Module 9: Graph Neural Networks (GNNs)

  • An introduction to graph data and its applications.
  • The concept of Graph Convolutional Networks (GCNs).
  • Building a simple GNN for node classification.
  • A discussion on other graph-based models.
  • The challenges and opportunities of working with graph data.

Module 10: Model Optimization and Deployment

  • The importance of model compression and pruning.
  • An overview of quantization for smaller models.
  • The role of ONNX and TensorFlow Lite.
  • A conceptual guide to deploying models to a cloud platform.
  • A discussion on monitoring model performance in production.

Module 11: End-to-End Project 1 - NLP

  • A project to build a complete NLP solution.
  • The project will involve data preprocessing, model selection, and fine-tuning.
  • Students will apply a Transformer-based model to a real-world problem.
  • A discussion on how to evaluate the model's performance.
  • A review of the best practices for building an NLP pipeline.

Module 12: End-to-End Project 2 - Computer Vision

  • A second, hands-on project focused on a computer vision task.
  • Students will implement an advanced CNN architecture.
  • The project will emphasize proper data augmentation and validation.
  • A discussion on how to interpret model predictions.
  • A review of the project and next steps.

CERTIFICATION

  • Upon successful completion of this training, participants will be issued with Macskills Training and Development Institute Certificate

TRAINING VENUE

  • Training will be held at Macskills Training Centre. We also tailor make the training upon request at different locations across the world.

AIRPORT PICK UP AND ACCOMMODATION

  • Airport Pick Up is provided by the institute. Accommodation is arranged upon request

TERMS OF PAYMENT

Payment should be made to Macskills Development Institute bank account before the start of the training and receipts sent to info@macskillsdevelopment.com

For More Details call: +254-114-087-180

 

Architecting Intelligence: A Comprehensive Guide To Advanced Deep Learning Training Course in Namibia
Dates Fees Location Action