The 1993–1994 Academic Training Programme included a lecture series on neural networks, delivered by F. James. The series aimed to explore how multilayer feed-forward networks can be used to solve real-world problems. The lectures covered topics such as the McCulloch-Pitts neuron, the structure of neural networks, and the advantages of neural networks, including their parallel processing capabilities, ability to solve complex problems, and fault tolerance. The series also discussed the challenges of neural networks, such as the difficulty in solving large problems and the need for extensive training data. The lecture series also introduced the concept of supervised learning, where networks learn from examples by adjusting weights to minimize the error between predicted and actual outputs. The back-propagation algorithm was highlighted as a key method for training neural networks, allowing the adjustment of weights to reduce the error in predictions. The series also discussed the use of neural networks in high-energy physics, such as in the identification of jets and b quark jets. In addition, the lecture series covered the development of neural network packages like JETNET 2.0 and JETNET 3.0, which are used for pattern recognition in high-energy physics. These packages utilize multilayer perceptron back-propagation algorithms and topological self-organizing maps. The series also discussed the importance of regularization techniques in neural networks to prevent overfitting and improve generalization. The lecture series emphasized the need for a balance between the complexity of the network and the amount of training data, as well as the importance of choosing an appropriate network architecture. It also discussed the challenges of solving large problems with neural networks and the need for efficient algorithms and techniques to handle these challenges. The series concluded with an overview of the theoretical foundations of neural networks, including the use of inverse problems and computational complexity in neural networks.The 1993–1994 Academic Training Programme included a lecture series on neural networks, delivered by F. James. The series aimed to explore how multilayer feed-forward networks can be used to solve real-world problems. The lectures covered topics such as the McCulloch-Pitts neuron, the structure of neural networks, and the advantages of neural networks, including their parallel processing capabilities, ability to solve complex problems, and fault tolerance. The series also discussed the challenges of neural networks, such as the difficulty in solving large problems and the need for extensive training data. The lecture series also introduced the concept of supervised learning, where networks learn from examples by adjusting weights to minimize the error between predicted and actual outputs. The back-propagation algorithm was highlighted as a key method for training neural networks, allowing the adjustment of weights to reduce the error in predictions. The series also discussed the use of neural networks in high-energy physics, such as in the identification of jets and b quark jets. In addition, the lecture series covered the development of neural network packages like JETNET 2.0 and JETNET 3.0, which are used for pattern recognition in high-energy physics. These packages utilize multilayer perceptron back-propagation algorithms and topological self-organizing maps. The series also discussed the importance of regularization techniques in neural networks to prevent overfitting and improve generalization. The lecture series emphasized the need for a balance between the complexity of the network and the amount of training data, as well as the importance of choosing an appropriate network architecture. It also discussed the challenges of solving large problems with neural networks and the need for efficient algorithms and techniques to handle these challenges. The series concluded with an overview of the theoretical foundations of neural networks, including the use of inverse problems and computational complexity in neural networks.