Build foundational knowledge in neural networks, optimization algorithms, and modern deep learning techniques.
Understand the building blocks of deep learning with perceptrons, layers, and activation functions.
Learn gradient descent, Adam, and learning rate techniques to improve model accuracy.
Explore cross-entropy, mean squared error, and custom loss design strategies.
Build convolutional neural networks for image classification tasks using PyTorch and TensorFlow.
LSTM networks for forecasting stock prices and weather patterns using historical data.
Build your first neural network, understand optimization techniques, and experiment with real-world datasets.