Course Overview

The Deep Learning Course provides an in-depth understanding of neural networks and their applications across various domains. Participants will explore topics such as deep learning fundamentals, architectures, training techniques, and optimisation strategies. The course covers popular frameworks like TensorFlow and PyTorch and includes hands-on projects for building, training, and deploying deep learning models. By the end of the course, learners will be equipped to solve complex problems using deep learning techniques.

 

Prerequisites

Basic knowledge of Python programming, linear algebra, and machine learning is recommended.

Target Audience

This course is ideal for data scientists, machine learning engineers, AI enthusiasts, and individuals looking to specialise in deep learning. It is suitable for those with a background in programming, mathematics, or machine learning concepts.

Course Highlights

  • Learn the fundamentals of deep learning and neural networks.
  • Develop skills in designing and training deep learning models.
  • Explore advanced architectures like CNNs, RNNs, and transformers.
  • Gain hands-on experience with TensorFlow, PyTorch, and related tools.
  • Understand best practices for optimising and deploying models.

Course Objectives

By the end of this course, learners will be able to:

  • Explain the principles and architecture of deep learning models.
  • Build and train basic neural networks using deep learning frameworks.
  • Apply advanced architectures like CNNs, RNNs, and transformers.
  • Optimise model performance through hyperparameter tuning and regularisation.
  • Implement models for real-world tasks like image recognition and text analysis.
  • Use tools like TensorFlow and PyTorch for deep learning projects.
  • Evaluate and interpret model results to refine outputs.
  • Identify challenges in deep learning projects and propose solutions.
  • Stay updated with emerging trends and tools in deep learning.
  • Address ethical concerns and biases in deep learning applications.

Course Outline

Instructional Methods: Group discussions on deep learning concepts, practical demonstrations of neural networks, and case studies on real-world applications.

Topics Covered:

  • Overview of deep learning and its role in AI.
  • Key differences between deep learning and traditional machine learning.
  • Components of a neural network: neurons, weights, biases, and activation functions.
  • Common applications of deep learning (e.g., vision, NLP, speech recognition).
  • The deep learning workflow: data, model, training, evaluation.

Instructional Methods: Hands-on exercises in creating neural networks, group discussions on model design, and practical examples using TensorFlow or PyTorch.

Topics Covered:

  • Structure and layers of neural networks (input, hidden, output).
  • Activation functions: ReLU, Sigmoid, Softmax, and Tanh.
  • Forward and backward propagation.
  • Loss functions and optimisation (MSE, cross-entropy).
  • Designing and implementing basic feedforward neural networks.

Instructional Methods: Practical exercises in implementing architectures, group discussions on use cases, and case studies on specific models.

Topics Covered:

  • Convolutional Neural Networks (CNNs) for image processing.
  • Recurrent Neural Networks (RNNs) for sequential data.
  • Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRUs).
  • Transformers and their applications in NLP.
  • Autoencoders for dimensionality reduction and anomaly detection.

Instructional Methods: Hands-on exercises in training models, group discussions on hyperparameter tuning, and practical activities in performance improvement.

Topics Covered:

  • Preparing datasets for training, validation, and testing.
  • Batch processing and gradient descent optimisation.
  • Regularisation techniques: dropout, L1/L2 regularisation.
  • Hyperparameter tuning and grid search.
  • Avoiding overfitting and underfitting in deep learning models.

Instructional Methods: Hands-on exercises in using tools, group discussions on framework selection, and practical demonstrations of implementation.

Topics Covered:

  • Introduction to TensorFlow and PyTorch for model building.
  • Using Keras for quick prototyping and experimentation.
  • Visualising training processes with TensorBoard.
  • Deploying models using ONNX or TensorFlow Lite.
  • Comparing frameworks for specific project needs.

Instructional Methods: Group discussions on emerging topics, practical activities in advanced implementations, and case studies on cutting-edge research.

Topics Covered:

  • Generative Adversarial Networks (GANs) for image generation.
  • Reinforcement learning in deep learning.
  • Explainability in deep learning (e.g., SHAP, LIME).
  • Ethical considerations and biases in deep learning models.
  • Emerging trends: federated learning, edge AI, and multimodal models.

Certification

A certificate of completion will be awarded upon successful completion of the course.

Course Fees

$788 $488

Deep Learning

Learn to Build, Train, and Deploy Deep Learning Models Using TensorFlow and PyTorch

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Learn to Build, Train, and Deploy Deep Learning Models Using TensorFlow and PyTorch

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