Deep Neural Network Training and Certification in Kraków, Poland

The Deep Neural Network Training is designed to provide participants with a strong foundation in building, training, and optimizing deep learning models. This training covers the essential concepts of neural networks, including architecture design, activation functions, backpropagation, and advanced optimization techniques. Participants will gain hands-on experience in implementing deep learning models using popular frameworks, equipping them with the skills to solve complex problems in various domains such as image recognition, natural language processing, and predictive analytics.

Introduction to Deep Neural Networks and Architecture

1. Overview of Deep Learning
• Understanding the concept of deep learning and its applications
• Differences between shallow and deep neural networks
• Introduction to key terms: neurons, layers, activation functions
• Practical exercise: Building a simple neural network from scratch
2. Deep Neural Network Architecture
• The anatomy of a deep neural network: Input, hidden, and output layers
• Activation functions: Sigmoid, ReLU, and softmax
• Initialization of weights and biases
• Practical exercise
3. Backpropagation Algorithm
• Understanding forward and backward propagation
• Derivation of the backpropagation algorithm and gradient descent
• Importance of the learning rate and optimization techniques
• Practical exercise
4. Optimization Algorithms
• Gradient Descent: Stochastic, Mini-batch, and Batch gradient descent
• Advanced optimization algorithms: Adam, RMSprop
• Practical exercise

Model Regularization, Advanced Techniques, and Real-World Applications

5. Regularization Techniques
• Overfitting and underfitting: Causes and solutions
• Dropout, L2 regularization, and early stopping
• Practical exercise
6. Advanced Techniques in Deep Learning
• Batch normalization for faster and more stable training
• Transfer learning: Leveraging pre-trained models for new tasks
• Practical exercise
7. Evaluating Deep Neural Networks
• Common performance metrics: Accuracy, precision, recall, F1-score
• Cross-validation and hyperparameter tuning
• Practical exercise
8. Practical Applications of Deep Neural Networks
• Real-world use cases: Image classification, NLP, and time-series forecasting
• Building a deep neural network for image recognition (e.g., CIFAR-10)
• Practical exercise
9. Course Recap and Q&A Session
• Summary of key concepts and techniques
• Open floor for questions and challenges encountered during the exercises
• Final thoughts and resources for further learning