Training Torch in machine learning and deep learning

Advanced training on the use of the Torch framework in deep learning projects. The program guides participants through the process of designing, implementing and optimizing deep learning models, with a special focus on the practical aspects of working with large data sets. The class combines a solid theoretical foundation with intensive hands-on workshops, where participants implement a variety of neural network architectures and learn to solve real-world problems using deep learning. The workshops are conducted in an environment that reflects real-world design challenges.

• Machine learning engineers working on deep learning projects
• Data scientists specializing in neural networks
• ML developers interested in the Torch framework
• Researchers working on advanced AI models
• Computer vision and natural language processing specialists
• Deep learning model optimization engineers
• AI solution architects looking for powerful tools
• Programmers looking to expand competencies with deep learning

1. Torch architecture basics
• Ecosystem and components of the framework
• Tensors and tensor operations
• Automatic differentiation
• Memory management and performance optimization
2. Neural network design
• Implementation of layers and models
• Activation functions and initialization of scales
• Mechanisms of regularization
• Advanced network architectures
3. Training and optimization
• Strategies for training models
• Gradient optimization techniques
• Managing the learning process
• Debugging deep learning models
4. Deployment and scaling
• Deployment of Torch models
• Optimizing inferencing performance
• Distributed model training
• Integration with production infrastructure

Upon completion of the training, the participant will have a deep working knowledge of designing and implementing deep learning models in the Torch environment. He will gain the ability to effectively train complex neural network architectures using advanced optimization techniques. Will develop the ability to identify and solve performance problems in deep learning projects. Will learn to implement custom neural network layers and components tailored to specific design needs. Will be able to optimize the model training and inference process for performance and resource consumption. Will gain hands-on experience in deploying Torch models in a production environment.

• Solid knowledge of the basics of deep learning
• Experience in Python programming
• Knowledge of linear algebra and differential calculus
• Practical knowledge of neural networks

• Torch Architecture
• Tensor operations
• Automatic differentiation
• Neural network design
• Gradient optimization
• Regularization of models
• Distributed training
• Model deployment
• Performance optimization
• Memory management
• Custom layers
• Production integration