I only learned yesterday that I’d have to install Caffee first before I could start the demo in Mocha. Caffee is a CNN (Convolutional Neiral Network) developed by Berkely Vison and Learning Center. The tutorial I’m following (at mochajl.readthedocs.io) requires using Cafe’s API to export the Caffee model parameters to HDF5 format. After that, the exported file can be input to Mocha. To sum up, I have to install Caffee first. That’s next…
Setting up Julia locally first, before trying the cloud environment. Running OS X, El Capitan. Python and iPython weee already installed. So I first installed Julia, then installed IJulia.jl from the Julia REPL (Read Eval Print Loop). IJulia is an interface between Julia and Jupyter notebooks. Then installed Images.jl, also from the Julia REPL. Then Gadfly, a visualization tool written in Julia. Will continue tomorrow night.
Since my last post, I discovered the Julia language. Like Lua, it is a high-performance programming language ideally suited to numeric analysis. Depending on how I get on with Julia, I may decide to run Mocha (a machine learning library for Julia) on an AWS instance instead of Torch.
Tofay I launched a virtual machine or instance running Ubuntu 14.4, on AWS. That was a straightforward process. Next .. to configure the instance so that I can install Torch.
For the first time since getting a MacBook around 6 years ago, I’m seriously considering moving to a different platform, for both hardware (Apples’s recent shift to less powerful GPUs) and software (latest updates to OS X) reasons.
Objective: compare two algorithms by inputting the same data to both. The two algorithms are the naive exact matching algorithm and the Boyer-Moore algorithm. They are implemented in Python code. The data consists of a pattern (P) and text (T).
I’m doing an online course in genomic data science, which requires some coding in Python. In this blog, I’ll write about my journey of learning Programming languages best suited to Data science, especially Python and Julia, and Deep learning frameworks such as Mocha.
Not returning any value, still.
Fixed it now. The problem was with another function that the higher function was calling.
… Is a gigantic task when you consider that the Milky Way galaxy is about 100,000 light years across, and our sun is 27,000 light years from the Galactic Center. We’re in a lonely part of the Milky Way, on the inner edge of a spiral called the Orion Arm. Yet the Milky Way galaxy is just one galaxy in a huge group of galaxies called a supercluster. A team of astronomers at the university of Hawaii have been mapping this supercluster. We now know that it’s 500 million light years in diameter and contains a hundred thousand galaxies. The Milky Way galaxy is located in the outskirts of this supercluster. Laniakea, our hone supercluster of galaxies.
The fastest supercomputers today have thousands of processors working in parallel, giving a total processing power that is measured in petaFLOPS – quadrillions of floating point operations per second. So, supercomputers are used to run complex models that require huge numbers of calculations every second. For example, supercomputers are used to run models of climate in the past. The results of running these models can then be used to make predictions about future climate changes. As well as running on supercomputers, the work of modelling climate change is also allocated to millions of personal computers around the world, via a technology known as distributed computing. The modelling task is broken down into millions of small tasks that are allocated to millions of processing units, which return the results of processing each task to the server and then receive further tasks.