Deep Learning

Understand basic to advanced deep learning algorithms by implementing them from scratch, along with their practical applications

Gain knowledge of various deep neural network architectures and their application areas to conquer your NLP issues.

Concepts, tools, and techniques to explore deep learning architectures and methodologies

Hands-on projects cover all the key deep learning methods built step-by-step in PyTorch

Implement techniques such as image classification and natural language processing (NLP) by understanding the different neural network architectures

Take your neural networks to a whole new level with the simplicity and modularity of Keras, the most commonly used high-level neural networks API.

Understand the core concepts of deep learning and deep reinforcement learning by applying them to develop games

Learn how to train popular deep learning architectures such as autoencoders, convolutional and recurrent neural networks while discovering how you can use deep learning models in your software applications with Microsoft Cognitive Toolkit

Leverage the power of Java and deep learning to build production-grade Computer Vision applications

Use the serverless computing approach to save time and money

Speed up the design and implementation of deep learning solutions using Apache Spark

Learn advanced state-of-the-art deep learning techniques and their applications using popular Python libraries

Introduction to deep learning and PyTorch by building a convolutional neural network and recurrent neural network for real-world use cases such as image classification, transfer learning, and natural language processing.

A comprehensive guide to advanced deep learning techniques, including Autoencoders, GANs, VAEs, and Deep Reinforcement Learning, that drive today's most impressive AI results

Leverage the power of deep learning and Keras to develop smarter and more efficient data models