Discover the power of neural networks, complex models inspired by the brain's design. While single-parameter networks give a basic grasp, real-world tasks need networks with many parameters. Follow along as we simplify the training process, exploring forward and backward steps, using math tricks. Learn practical tips for data prep and model setup to train neural networks effectively.
Learn how to train a neural network with just one parameter, exploring the basics of optimization. Dive into the process of fine-tuning this single parameter to improve model performance and achieve your goals effectively.
Discover the essence of deep learning optimization algorithms, ranging from the foundational Gradient Descent to the advanced strategies of AdaGrad and Adam. Dive into their intricacies and learn how they shape the training process, leading to optimal model performance and convergence.
Selecting the best activation function is critical for effective neural network design. For hidden layers, ReLU is commonly used in CNNs and MLPs, while sigmoid and tanh suit RNNs. Output layer activation depends on the task: linear for regression, sigmoid for binary classification, softmax for multi-class, and sigmoid for multi-label classification.