Deep Learning

Leverage the power of Tensorflow to design deep learning systems for a variety of real-world scenarios

Implement neural network models in R 3.5 using TensorFlow, Keras, and MXNet

Insightful projects to master deep learning and neural network architectures using Python and Keras

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

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

Build and deploy powerful neural network models using the latest Java deep learning libraries

Use the serverless computing approach to save time and money

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

Explore TensorFlow's capabilities to perform efficient deep learning on images

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

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

Understand basic-to-advanced deep learning algorithms, the mathematical principles behind them, and their practical applications

Delve into neural networks, implement deep learning algorithms, and explore layers of data abstraction with the help of TensorFlow.

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.

Build neural network models in text, vision and advanced analytics using PyTorch