Welcome to our collection of deep learning tutorials! Here, you will find a range of resources to help you understand and apply deep learning techniques. Whether you're a beginner or an experienced AI practitioner, these tutorials are designed to provide you with the knowledge and skills you need to succeed.
Tutorials Overview
- Introduction to Deep Learning
- Neural Networks
- Convolutional Neural Networks (CNNs)
- Recurrent Neural Networks (RNNs)
- Generative Adversarial Networks (GANs)
- Deep Learning with PyTorch
- Deep Learning with TensorFlow
Introduction to Deep Learning
Deep learning is a subset of machine learning that structures algorithms in layers to create an "artificial neural network" that can learn and make intelligent decisions on its own.
Read more about deep learning fundamentals
Neural Networks
Neural networks are a series of algorithms that can recognize underlying relationships in a set of data through a process that mimics the way the human brain operates.
Convolutional Neural Networks (CNNs)
CNNs are a class of deep neural networks, most commonly applied to analyzing visual imagery.
Recurrent Neural Networks (RNNs)
RNNs are a type of artificial neural network designed to recognize patterns in sequences of data, such as time series or natural language.
Generative Adversarial Networks (GANs)
GANs consist of two neural networks, the generator and the discriminator, which are trained simultaneously.
Deep Learning with PyTorch
PyTorch is an open-source machine learning library based on the Torch library, used for applications such as computer vision and natural language processing.
Deep Learning with TensorFlow
TensorFlow is an open-source software library for dataflow programming across a range of tasks.