Skip to main content

Why Tensorflow


You have a lot of data which you want to make sense of, learn patterns, but you don't have the necessary expertice to develop algorithms that will learn through the data. Ofcourse you can develop your own machine learning algorithms to make sense of the data. There might be benifits in developing your own algorithms, proprietary being one, but you might have to invest time and money.

What if you have access to ready made machine learning algorithms which you just have to use in your products? Google's Tensorflow offers such tried and tested algorithms using APIs that you just have to call in your programs. All you have to provide is data, and Tensorflow will take care of the intelligence to learn.

Tensorflow adds the following capabilities to your products
1. Access to machine learning algorithms such as Neural Networks.
2. Increase performance of your models using multiple CPUs and GPUs without change in code.
3. Do numerical computations using data flow graphs.

To learn more about Tensorflow check Tensorflows official website at

Check the white paper of Tensorflow at

Check the presentation of Jeff Dean to know more about Tensorflow applications at Google http://static.googleusercontent.com/media/research.google.com/en//people/jeff/BayLearn2015.pdf

Install Tensorflow using

Go through Tensorflow tutorials at

Download and check source code of tutorials at


Keep following this blog as the researchers at Cerelabs try their hands on Tensorflow...

Comments

Popular posts from this blog

GPU - The brain of Artificial Intelligence

Machine Learning algorithms require tens and thousands of CPU based servers to train a model, which turns out to be an expensive activity. Machine Learning researchers and engineers are often faced with the problem of running their algorithms fast. Although initially invented for processing graphics in computer games, GPUs today are used in machine learning to perform feature detection from vast amount of unlabeled data. Compared to CPUs, GPUs take far less time to train models that perform classification and prediction. Characteristics of GPUs that make them ideal for machine learning Handle large datasets Needs far less data centre infrastructure Can be specialized for specific machine learning needs Perform vector computations faster than any known processor Designed to perform data parallel computation NVIDIA CUDA GPUs today are used to build deep learning image processing tools for  Adobe Creative Cloud. According to NVIDIA blog future Adobe applicati

Building Commonsense in AI

It is often debated that what makes humans the ultimate intelligent species is the innate quality of doing commonsense reasoning. Humans use common sense knowledge about the world around to take appropriate decisions, and this turns out to be the necessary ingredient for their survival. AI researches have long thought about building commonsense knowledge in AI. They argue that if AI possess necessary commonsense knowledge then it will be a truly intelligent machine. We will discuss two major commonsense projects that exploit this idea: Cyc tries to build a comprehensive ontology and knowledge base of everyday commonsense knowledge. This knowledge can be used by AI applications to do human-like reasoning. Started in 1984, Cyc has come a long way. Today, OpenCyc 4.0 includes the entire Cyc ontology, containing 239,000 concepts and 2,093,000 facts and can be browsed on the OpenCyc website - http://www.cyc.com/platform/opencyc/ . OpenCyc is available for download from Source

Understanding Projection Pursuit Regression

The following article gives an overview of the paper "Projection Pursuit Regression” published by Friedman J. H and Stuetzle W. You will need basic background of Machine Learning and Regression before understanding this article. The algorithms and images are taken from the paper. ( http://www.stat.washington.edu/courses/stat527/s13/readings/FriedmanStuetzle_JASA_1981.pdf )  What is Regression? Regression is a machine learning technology used to predict a response variable given multiple predictor variables or features. The main distinction is that the response to be predicted is any real value and not just any class or cluster name. Hence though similar to Classification in terms of making a prediction, it is largely different given what it’s predicting.  A simple to understand real world problem of regression would be predicting the sale price of a particular house based on it’s square footage, given that we have data of similar houses sold in that area in the past. T