Fundamentals of Deep Learning

Build a strong foundation in deep learning through clear explanations, practical examples, and guided video lessons. This course introduces how neural networks learn from data and how to build, train, and evaluate models using Python.

Start with artificial neurons, activation functions, loss functions, and optimization. Understand forward propagation and backpropagation, then explore popular architectures, including Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs), including LSTM and GRU.

Practical lessons introduce applications such as image classification and sentiment analysis. You will also explore autoencoders for data reconstruction and Generative Adversarial Networks (GANs) for image generation. Exercises use Google Colab, allowing you to run the examples in your browser.

Designed for students, educators, and professionals beginning their deep learning journey, this course combines essential concepts with hands-on implementation.