Caffe 2 continues the strong support for vision type problems but adds in recurrent neural networks (RNN) and long short term memory (LSTM) networks for natural language processing, handwriting recognition, and time series forecasting. MXNet supports deep learning architectures such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) including Long Short-Term Memory (LTSM) networks. This framework provides excellent capabilities for imaging, handwriting and speech recognition, forecasting and natural language processing. DL4J has a rich set of deep network architecture support: RBM, DBN, Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), RNTN, and Long Short-Term Memory (LTSM) network.
Deep learning is a fascinating field of study and the techniques are achieving world class results in a range of challenging machine learning problems. Which library should you use and which techniques should you focus on? In this post you will discover a 14-part crash course into deep learning in Python with the easy to use and powerful Keras library. This mini-course is intended for python machine learning practitioners that are already comfortable with scikit-learn on the SciPy ecosystem for machine learning. Applied Deep Learning in Python Mini-Course Photo by darkday, some rights reserved. Before we get started, let's make sure you are in the right place.
In "Big data – a road map for smarter data," I describe a set of machine learning architectures that will provide advanced capabilities to include image, handwriting, video, and speech recognition, natural language processing and object recognition. There is no perfect deep learning network that will solve all your business problems. Hopefully, the below table with the accommodating descriptive outline will provide you insights towards the best fit for purpose framework for your business problem. The ranking is based on the number of stars awarded by developers in GitHub. The numbers were compiled at the beginning of May of 2017.
The existence of Artificial intelligence has been for a long time in the industry. The impact of AI has never been limited moreover, it has become an important element in recent years. The huge improvements in various fields, especially in mobile App development, is due to the development of various libraries and framework. Thus we can say that AI has become a responsive IT field and has lots of research going into it. There are many AI solutions that came into existence due to the implementation and integration of libraries and framework.
But where do you start? Which library do you use? There are just so many! This list is by no means exhaustive, it's simply a list of libraries that I've used in my computer vision career and found particular useful at one time or another. Some of these libraries I use more than others -- specifically, Keras, mxnet, and sklearn-theano.