Today we see a wide ranging explosion of applications that are wide and connected with an emphasis on storage and processing. Most companies are storing a lot of data but not solving the problem of what to do with it. Yet most of the information is stored in raw form: There a huge amount of information locked-up in databases: information that is potentially important but has not yet been discovered.

Our Success: Combinatorial Innovation

Many big companies like Google, Facebook and Baidu are Open-Sourcing their most powerful algorithms that are built mainly for Natural Language and Image understanding.

This is an enormous opportunity for small and medium high-tech companies to develop Smart Vertical Applications: we call this process COMBINATORIAL INNOVATION.

Since all the Heavy Work of creating the fundamental algorithms is already done, developing powerful industrial application is just a matter of Combining the existing code in a proper way.

Machine Learning Applications

We’ve seen there is a huge demand in industry for machine learning applications: everyone is collecting large amounts of data but very few companies have the technical knowledge to extract the information that the customer needs.

Here is a brief list of the projects we have developed for our customers.

Machine Learning Benchmarks

If you work with Machine Learning and Deep Learning most probably you have the same problem we have every day: when it’s time to buy new hardware you cannot find any recent and reliable benchmark.

In this section we compare the performances of different GPUs (NVIDIA GTX 1080, TITAN X) versus different frameworks (TensorFlow, Nervana Systems Neon, Berkeley Caffe) and different algorithms.

Soon we’ll add some full system and distributed system benchmarks as well.

Frameworks

Here are some of the frameworks we are working with.

TensorFlow

TensorFlow was released by Google in November, 2015. It is a framework for numerical computation on data flow graphs. Each node in the graph represents a mathematical operation, while the edges between nodes represent the multidimensional arrays (tensors) that flow between operations. TensorFlow is particularly suited for ML algorithm and it was designed to run on heterogeneous (and possibly distributed) resources.

Tensorflow

Neon Nervana

Neon is the Nervana's Python based framework for Deep Learning. It is directly developed with NN in mind and for this reason it is easier to use and it provides higher level function for creating complex NN architectures. One of the goal of Neon is to provide great performance along ease of use. For this reason it works on selected NVIDIA hardware or Nervana's hardware. It is also possible to use their Cloud service to run the code. There is no need for other frameworks if we want to explore its Deep Learning features.

Neon Nervana

Caffe

Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is originally developed by the Berkeley Vision and Learning Center (BVLC) and by community contributors.

We are currently using the NVIDIA caffe fork.

Caffe

Scikit-learn

Scikit-learn is a free software machine learning framework for the Python programming language. It features various classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy. Scikit-learn is largely written in Python, with some core algorithms written in Cython to achieve performance.

scikit-learn