Fundamentals of Deep Learning with Python

This course introduces the basic concepts of deep learning and neural networks. It is intended for learners with a basic background in Python who are new to machine learning and AI.
Topics to be covered include:
• Key definitions and concepts in deep learning
• Neurons, neural networks, and multilayer perceptrons (MLPs)
• Introduction to TensorFlow or PyTorch: installation and simple examples
• Building simple models with Keras or PyTorch
• The training process: forward pass, loss functions, backpropagation
• Gradient Descent Algorithm and its variations, such as Stochastic Gradient Descent (SGD), Adam, and other common optimizers.
• Evaluating the performance of deep learning models.
• Using Keras/PyTorch and Scikit-learn for machine learning.
• Applying Dropout to prevent overfitting.
• Completing a practical project using a neural network in a Python environment.
Please email teh.rtc.reg@gmail.com for more information and instructions to apply. **Registration is only through email submission and approval by the Tehran-RTC**