Deep Learning Tutorials

In this tutorial, you'll get an overview of Artificial Intelligence (AI) and take a closer look in what makes Machine Learning (ML) and Deep Learning different.

Explore cross-entropy in machine learning in our guide on optimizing model accuracy and effectiveness in classification with TensorFlow and PyTorch examples.

A walkthrough of Autoencoders, their variations, and potential applications in the real world.

A complete guide to understanding CNNs, their impact on image analysis, and some key strategies to combat overfitting for robust CNN vs deep learning applications.

Learn to navigate the landscape of common activation functions—from the steadfast ReLU to the probabilistic prowess of the softmax.

The tutorial answers the most frequently asked questions about deep learning and explores various aspects of deep learning with real-life examples.

Explore the key differences between PyTorch, TensorFlow, and Keras - three of the most popular deep learning frameworks. Understand their unique features, pros, cons, and use cases to choose the right tool for your project.

Understanding deep neural networks and their significance in the modern deep learning world of artificial intelligence

Learn how to construct and implement Convolutional Neural Networks (CNNs) in Python with Tensorflow Framework 2

Learn what natural language processing (NLP) is and discover its real-world application, using Google BERT to process text datasets.

Discover Long Short-Term Memory (LSTM) networks in Python and how you can use them to make stock market predictions!

This Keras tutorial introduces you to deep learning in Python: learn to preprocess your data, model, evaluate and optimize neural networks.