The “Basics of Neural Networks” course is an intensive one-week course organized by the Oxford Training Centre that provides a very firm grounding in the world of Artificial Neural Networks-ANN-and their applications to machine learning and deep learning. In this course, participants will be taken through the basics of Neural Networks: architecture, algorithms, types, and the all-important training and optimization process. This course is intended for those who wish to further their understanding of how Neural Networks work, what their use cases are, and how they may be applied in real-world scenarios.
Objectives and target group
Objectives
By the end of this course, participants will be able to:
- Understand the core principles of Neural Networks and their role in Machine Learning and Deep Learning.
- Explore various Neural Network architectures and learn how different types of Neural Networks are used in practice.
- Gain an in-depth understanding of Neural Network layers, activation functions, and optimization techniques.
- Learn the process of training a Neural Network, including the use of backpropagation and optimization methods.
- Apply Neural Network algorithms to solve real-world problems and gain insights from Neural Network models.
- Understand how to use Neural Networks in different applications, from image recognition to speech processing.
Target Group
This course is designed for:
- Beginners in Machine Learning and Artificial Intelligence who want to learn the basics of Neural Networks and how they are applied in various fields.
- Students and Professionals who wish to expand their knowledge in Deep Learning and its integration with Machine Learning.
- Data Scientists and Engineers who are looking to incorporate Neural Networks into their workflow and solve complex problems using AI-driven solutions.
- Anyone with an interest in AI technologies, including developers and tech enthusiasts, who want to understand the power and potential of Neural Networks.
Content
The “Basics of Neural Networks” course is structured around key topics that provide an in-depth understanding of Neural Networks:
- Introduction to Neural Networks and Machine Learning
- Overview of Neural Networks and Artificial Neural Networks (ANN)
- The relationship between Machine Learning, Deep Learning, and Neural Networks
- Key concepts and components of Neural Network models
- Neural Network Architecture
- Exploration of different types of Neural Networks: Feedforward, Convolutional (CNN), and Recurrent (RNN)
- Neural Network layers: input, hidden, and output layers
- The role of activation functions in Neural Networks and their importance in network performance
- Neural Network Training and Backpropagation
- Understanding Neural Network training and its components
- Introduction to the Backpropagation algorithm and its role in training Neural Networks
- Techniques for optimizing Neural Networks and improving model accuracy
- Neural Network Optimization and Algorithms
- Common Neural Network algorithms used for various machine learning tasks
- Overview of optimization techniques such as gradient descent
- Fine-tuning and improving Neural Network performance through optimization strategies
- Practical Applications and Use Cases
- Real-world Neural Network examples and how they are applied in industries like healthcare, finance, and e-commerce
- Case studies of successful Neural Network implementations and problem-solving
- Hands-on Neural Network tutorials to apply theoretical knowledge in practical scenarios
- Neural Network Resources and Further Learning
- A guide to Neural Network resources, libraries, and tools
- How to implement Neural Networks using popular frameworks like TensorFlow, Keras, and PyTorch
- Next steps for advancing in the field of Deep Learning and Machine Learning