This series provides an introduction to neural networks and deep learning, covering their applications and basic understanding. The fundamental building blocks of neural networks, including neurons, activation functions, and layers, are explained, along with their contribution to the learning process. Training a neural network is explored, highlighting the significance of forward and backward propagation, cost functions, and gradient descent. Popular deep learning algorithms, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), their specific applications, and recent advancements in the field, including transfer learning and generative adversarial networks (GANs), are also discussed.