Deep Learning

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

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

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

Use Java and Deeplearning4j to build robust, scalable, and highly accurate AI models from scratch

Deep learning simplified by taking supervised, unsupervised, and reinforcement learning to the next level using the Python ecosystem

Learn the essential background and model functioning of various neural network architectures such as CNN. RNN. LSTM, GANs, Autoencoders and more using best practices from an industry expert

Apply modern deep learning techniques to build and train deep neural networks using Gorgonia

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

Implement popular deep learning techniques to make your IoT applications smarter

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

Learn how to apply TensorFlow to a wide range of deep learning and Machine Learning problems with this practical guide on training CNNs for image classification, image recognition, object detection and many computer vision challenges.