Learn how deep learning libraries compare to each other, what distributed TensorFlow is, and how a graphics processing unit can accelerate model training.
Learn about the challenges of pose estimation—poor image quality, occluded pose elements, etc.—and how to overcome them using fully convolutional networks.
This paper presents the comparison of the five deep learning tools in terms of training time and accuracy. The evaluation includes classifying digits from the MNIST data set using a fully connected neural network architecture (FCNN).
Find how deep learning can benefit healthcare and finance, study industrial use cases, and see the advantages of intelligent video analytics and anomaly detection.
