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I successfully defended my Ph.D.
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I'm happy to announce that I've successfully defended my thesis "Deep Learning Features for Image Processing". After four years, I've defended it officially in front of the thesis committed last Friday and then again two days ago I've successfully publicly defended in front of my friends, family and colleagues.

I'm now a "Doctor of Philosophy in Computer Science :)

I will update my thesis with the last comments in November and send the final version to the university. At which point, I'll publish it on this website as well.

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Budgetwarrior: Track assets and portfolio, savings rates and auto-completion
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This last month, I've been reading quite a few blogs about personal finance and I've decided to integrate more features into budgetwarrior. This post is about three new features that I've integrated. It's not yet a new release, so if you want to test this version, you'll have to compile it from the master branch on Git.

As it was last time, the values on my screenshots have all been randomized.

If you have several assets with different distributions, I believe it is a great value to have them all shown at the same time. Especially if you want to change the distribution of your portfolio or if you plan big changes in it.

Track assets

The first feature I've added is a feature to precisely track each of your assets independently. And you can also track the allocation of your portfolio in terms of stocks, bonds and cash. The tool also lets you set the desired distribution of your assets and will compute the difference that you should make in order to comply to your desired distribution.

First, you need to define all your asset classes (your accounts, funds, and stocks, ...) and their distribution with budget asset add. It also supports to set a currency. The default currency is now CHF, but you can set it in the configuration file, for instance default_currency=USD. You can see your assets using budget asset:

View of your assets

You can then set the value of your assets using budget asset value add. The system will save all the values of your assets. For now, only the last value is used in the application to display. In the future, I plan to add new reports for evolution of the portfolio over time. You can see your current net worth with the budget asset value:

View of your portfolio

The different currencies will all be converted to the default currency.

Savings rate

The second change I did is to compute the savings rate of each month and year. The savings rate is simply the portion of your income that you are able to save each month. The savings rate for a year is simple the average of the savings rate of each month.

The savings rate of the month can be seen with budget overview month:

Savings rate of the month

The saving rates of each month can also be seen in the overview of the year with budget overview year:

Savings rate of the year

This shows the savings rate of each month, the average of the year and the average of the current year up to the current month.

The savings rate is a very important metric of your budget. In my case, it's currently way too low and made me realize I really need to save more. Any savings rate below 10% is too low. There are no rule as too much it should be, but I'd like to augment mine to at least 20% next year.

Auto-completion

The last feature is mostly some quality-of-life improvement. Some of the inputs in the console can now be completed. It's not really auto-completion per se, but you can cycle through the list of possible values using the UP and DOWN.

This makes it much easier to set some values such as asset names (in budget asset value add for instance), account names and objective types and sources. I'm trying to make the input of values easier.

Conclusion

I don't know exactly what else will be integrated in this feature, but I may already improve some visualization for asset values. If I learn something new about personal finance that I may integrate in the tool, I'll do it as well.

If you are interested by the sources or want to install this version, you can download them on Github: budgetwarrior.

The new features are in the master branch.

If you have a suggestion for a new features or you found a bug, please post an issue on Github, I'd be glad to help you.

If you have any comment, don't hesitate to contact me, either by letting a comment on this post or by email.

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Deep Learning Library 1.0 - Fast Neural Network Library
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DLL Logo

I'm very happy to announce the release of the first version of Deep Learning Library (DLL) 1.0. DLL is a neural network library with a focus on speed and ease of use.

I started working on this library about 4 years ago for my Ph.D. thesis. I needed a good library to train and use Restricted Boltzmann Machines (RBMs) and at this time there was no good support for it. Therefore, I decided to write my own. It now has very complete support for the RBM and the Convolutional RBM (CRBM) models. Stacks of RBMs (or Deep Belief Networks (DBNs)) can be pretrained using Contrastive Divergence and then either fine-tuned with mini-batch gradient descent or Conjugate Gradient or used as a feature extractor. Over the years, the library has been extended to handle Artificial Neural Networks (ANNs) and Convolutional Neural Networks (CNNs). The network is also able to train regular auto-encoders. Several advanced layers such as Dropout or Batch Normalization are also available as well as adaptive learning rates techniques such as Adadelta and Adam. The library also has integrated support for a few datasets: MNIST, CIFAR-10 and ImageNet.

This library can be used using a C++ interface. The library is fully header-only. It requires a C++14 compiler, which means a minimum of clang 3.9 or GCC 6.3.

In this post, I'm going to present a few examples on using the library and give some information about the performance of the library and the roadmap for the project.

Read more…

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