28 Jan 2017 Rachel Thomas
Applications are now open for Deep Learning Part 2, to be offered at the University of San Francisco Data Institute on Monday evenings, Feb 27-April 10. The course will cover integrating multiple cutting-edge deep learning techniques, as well as combining classic machine learning techniques with deep learning.
In part 1, we worked hard to curate a diverse group of participants, because we’d observed that artificial intelligence is missing out because of its lack of diversity. A study of 366 companies found that ethnically diverse companies are 35% more likely to perform well financially, and teams with more women perform better on collective intelligence tests. Scientific papers written by diverse teams receive more citations and have higher impact factors.
Everyone benefited from having a class full of curious coders from a variety of backgrounds. We had a number of students interested in using deep learning for social good, including Sara Hooker, founder and executive director of Delta Analytics, which partners non-profits with data scientists. She is now working on a project to use audio data streamed from recycled cell phones in endangered forests to track harmful human activity. Another student was a former Literature PhD student interested in analyzing gender and language in Github commits. Several students connected over their shared interest in Alzheimer’s research. International fellow Samar Haider is a researcher applying natural language processing to his native language of Urdu, one of the 70 different spoken languages in Pakistan, many of which have not been well-studied and are in need of the additional resources deep learning can provide. Another international fellow said he never expected to be using so many command line tools (we provide scripts and guidance to walk you through the setup) and he ended up creating an Amazon Machine Image which saved memory to share with the rest of the class.
One of the ways we achieved this great outcome was by, together with USF Data Institute, sponsoring diversity fellowships and international fellowships. It was such a success that we’ve decided to do it again.
I am saddened and angered that President Trump is banning immigrants from certain countries from entering the US, even when they have visas and green cards. The deep learning community is suffering from its lack of diversity already, and we are trying to fight that. We can’t change government policy at fast.ai, but we can do our little bit: we will again offer free remote international fellowships for those selected outside San Francisco to attend classes virtually, have access to all the same online resources, and be a part of our community. People of all religions and from all countries, including Iran, Iraq, Libya, Somalia, Syria, Sudan, and Yemen, are welcome and encouraged to apply.
Diversity fellowship are full or partial tuition waivers to attend the in-person course in San Francisco for women, people of Color, LGBTQ people, or veterans. We are looking for applicants who have shown the ability to follow through on projects and a significant level of intellectual curiosity.
International fellowships allow those who can not get to San Francisco to attend virtual classes for free during the same time period as the in-person class and provides access to all the same online resources. (Note that international fellowships do not provide an official completion certificate through USF). Our international fellows from part 1 contributed greatly to the community.
Both fellowships require completion of part 1. When applying, please let us know about any way that you have contributed to the student community (such as forum posts, pull requests, or open source projects). To apply, email your resume to firstname.lastname@example.org and email@example.com, along with a note of whether you are interested in the diversity or international fellowships and a brief paragraph on how you want to use deep learning. Note that to be eligible, you must have completed Deep Learning Part 1, either in person, or through our MOOC. Deep Learning Part 1 involves approximately 70 hours of work, so if you haven’t finished yet, you should get studying. The deadline to apply is 2/13.
17 Jan 2017 Jeremy Howard
With part 2 of our in person SF course starting in 6 weeks, and applications having just opened, we figured we better tell you a bit about what to expect!… So here’s an overview of what we’re planning to cover.
The main theme of this part of the course will be tackling more complex problems, that require integrating a number of techniques. This includes both integrating multiple deep learning techniques (such as combining RNNs and CNNs for attentional models), as well as combining classic machine learning techniques with deep learning (such as using clustering and nearest neighbors for semi-supervised and zero-shot learning). As always, we’ll be introducing all methods in the context of solving end-to-end real world modeling problems, using Kaggle datasets where possible (so that we have a clear best-practice goal to aim for).
Since we have no pre-requisites for the course other than a year of coding experience and completion of part 1 of the course, we’ll be fully explaining all the classic ML techniques we’ll use as well.
In addition, we’ll be covering some more sophisticated extensions of the DL methods we’ve seen, such as adding memory to RNNs (e.g. for building question answering systems / “chat bots”), and multi-object segmentation and detection methods.
Some of the methods we’ll examine will be very recent research directions, including unpublished research we’ve done at fast.ai. So we’ll be looking at journal articles much more frequently in this part of the course—a key teaching goal for us is that you come away from the course feeling much more comfortable reading, understanding, and implementing research papers. We’ll be sharing some simple tricks that make it much easier to quickly scan and get the key insights from a paper.
Python 3 and Tensorflow
This part of the course will use Python 3 and Tensorflow, instead of Python 2 and Theano as used in part 1. We’ll explain our reasoning in more detail in a future post; we hope that you will come away from the course feeling confident in both of these tools, and able to identify the strengths and weaknesses of both, to help you decide what to use in your own projects.
We’ve found using Python 3 to develop the course materials quite a bit more pleasant than Python 2. Whilst version 3 of the language has provided some incremental improvements for many years, until recently we’ve found the lack of support for Python 3 in scientific computing libraries resulted in it being a very frustrating experience. The good news is that that’s all changed now, and furthermore recent developments in Python 3.4 and 3.5 have greatly improved the productivity of the language.
Our view of Tensorflow is that buried in a rather verbose and complex API there’s a very nice piece of software buried away in there. We’ll be showing how to write custom GPU accelerated algorithms from scratch in Tensorflow, staying within a small and simple subset of the Tensorflow API where things stay simple and elegant.
Structured data, time series analysis, and clustering
One area where deep learning has been almost entirely ignored is in the area of structured data analysis (i.e. analyzing data where each column represents a distinct feature, such as from a database table). We had wondered whether this is because deep learning is simply less well suited to this task than the very popular decision tree ensembles (such as random forests and XGBoost, which we’re big fans of), but we’ve recently done some research that has shown that deep learning can be both simpler and more effective than these techniques. But getting it to work well requires getting a lot of little details right—details that have never been fully understood or documented elsewhere to the best of our knowledge.
We’ll be showing how to get state of the art results in structured data analysis, including showing how to use the wonderful XGBoost, and comparing these techniques. We’ll also take a brief detour into looking at R, where structured data analysis is still quite a bit more straightforward than Python.
Most of the structured data sets we’ll investigate will have a significant time series component, so we’ll also be discussing the best ways to deal with this kind of data. Time series pop up everywhere, such as fraud and credit models (using time series of transactions), maintenance and operations (using time series of sensor readings), finance (technical indicators), medicine (medical sensors and EMR data), and so forth.
We will also begin our investigation of cluster analysis, showing how it can be combined with a softmax layer to create more accurate models. We will show how to implement this analysis from scratch in Tensorflow, creating a novel GPU accelerated algorithm.
Deep dive into computer vision
We will continue our investigation into computer vision applications from part 1, getting into some new techniques and new problem areas. We’ll study resnet and inception architectures in more detail, with a focus on how these architectures can be used for transfer learning. We’ll also look at more data augmentation techniques, such as test time augmentation, and occlusion.
We’ll learn about the K nearest neighbors algorithm, and use it in conjunction with CNNs to get state of the art results on multi-frame image sequence analysis (such as videos or photo sequences). From there, we will look at other ways of grouping objects using deep learning, such as siamese and triplet networks, which we will use to get state of the art results for image comparisons.
Unsupervised and semi-supervised learning, and productionizing models
In part 1 we studied pseudo-labeling and knowledge distillation for semi-supervised learning. In part 2 we’ll learn more techniques, including bayesian-inspired techniques such as variational autoencoder and variational ladder networks. We will also look at the role of generative models in semi-supervised learning.
We will show how to use unsupervised learning to build a useful photo fixing tool, which we’ll then turn into a simple web app in order to show how you can put deep learning models into production.
Zero-shot learning will be a particular focus, especially the recently developed problem of generalized zero-shot learning. Solving this problem allows us to build models on a subset of the full dataset, and apply those models to whole new classes that we haven’t seen before. This is important for real-world applications, where things can change and new types of data can appear any time, and where labeling can be expensive, slow, and/or hard to come by.
And don’t worry, we haven’t forgotten NLP! NLP is a great area to apply unsupervised and semi-supervised learning, and we will look at a number of interesting problems and techniques in this space, including how to use siamese and triplet networks for text analysis.
Segmentation, detection, and handling large datasets
Handling large datasets requires careful management of resources, and doing it in a reasonable time frame requires being thoughtful about the full modeling process. We will show how to build models on the well-known Imagenet dataset, and will show that analysing such a large dataset can readily be done on a single machine fairly quickly. We will discuss how to use your GPU, CPUs, RAM, SSD, and HDD together, taking advantage of each part most effectively.
Whereas most of our focus on computer vision so far has been classification, we’ll now move our focus to localization—that is, finding the objects in an image (or in NLP, finding the relevant parts of a document). We have looked at some simple heatmap and bounding box approaches in part 1 already; in part 2 we build on that to look at more complete segmentation systems, and methods for finding multiple objects in an image. We will look at the results of the recent COCO competition to understand the best approaches to these problems.
Neural machine translation
As recently covered by the New York Times, Google has totally revamped their Translate tool using deep learning. We will learn about what’s behind this system, and similar state of the art systems—including some more recent advances that haven’t yet found their way into Google’s tool.
We’ll start with looking at the original encoder-decoder model that neural machine translation is based on, and will discuss the various potential applications of this kind of sequence to sequence algorithm. We’ll then look at attentional models, including applications in computer vision (where they are useful for large and complex images). In addition, we will investigate stacking layers, both in the form of bidirectional layers, and deep RNN architectures.
Question answering and multi-modal models
Recently there has been a lot of hype about chatbots. Although in our opinion they’re not quite ready for prime time (which is why pretty much all production chatbots still have a large human element), it’s instructive to see how they’re built. In general, question answering systems are built using architectures that have an explicit memory; we will look at ways of representing that in a neural network, and see the impact it has on learning.
We will also look at building visual Q&A systems, where you allow the user to ask questions about an image. This will build on top of the work we did earlier on zero-shot learning.
Reinforcement learning has become very popular recently, with Google showing promising results in training robots to complete complex grasping actions, and DeepMind showing impressive results in playing computer games. We will survey the reinforcement learning field and attempt to identify the most promising application areas, including looking beyond the main academic areas of study (robots and games) to opportunities for reinforcement learning of more general use.
We hope to see you at the course! Part 1 was full, and part 2 is likely to be even more popular, so get your application in soon!
03 Jan 2017 Rachel Thomas
Buried in a Reddit comment, Francois Chollet, author of Keras and AI researcher at Google, made an exciting announcement: Keras will be the first high-level library added to core TensorFlow at Google, which will effectively make it TensorFlow’s default API. This is excellent news for a number of reasons!
As background, Keras is a high-level Python neural networks library that runs on top of either TensorFlow or Theano. There are other high level Python neural networks libraries that can be used on top of TensorFlow, such as TF-Slim, although these are less developed and not part of core TensorFlow.
Using TensorFlow makes me feel like I’m not smart enough to use TensorFlow; whereas using Keras makes me feel like neural networks are easier than I realized. This is because TensorFlow’s API is verbose and confusing, and because Keras has the most thoughtfully designed, expressive API I’ve ever experienced. I was too embarrassed to publicly criticize TensorFlow after my first few frustrating interactions with it. It felt so clunky and unnatural, but surely this was my failing. However, Keras and Theano confirm my suspicions that tensors and neural networks don’t have to be so painful. (In addition, in part 2 of our deep learning course Jeremy will be showing some tricks to make it easier to write custom code in Tensorflow.)
For a college assignment, I once used a hardware description language to code division by adding and shifting bits in the CPU’s registers. It was an interesting exercise, but I certainly wouldn’t want to code a neural network this way. There are a number of advantages to using a higher level language: quicker coding, fewer bugs, and less pain. The benefits of Keras go beyond this: it is so well-suited to the concepts of neural networks, that Keras has improved how Jeremy and I think about neural networks and facilitated new discoveries. Keras makes me better at neural networks, because the language abstractions match up so well with neural network concepts.
Writing programs in the same conceptual language that I’m thinking in allows me to focus my attention on the problems I’m trying to solve, and not on artifacts of the programming language. When most of my mental energy is spent converting between the abstractions in my head and the abstractions of the language, my thinking becomes slower and fuzzier. TensorFlow effects my productivity in a similar way that having to code in Assembly would effect my productivity.
As Chollet wrote, “If you want a high-level object-oriented TF API to use for the long term, Keras is the way to go.” And I am thrilled about this news.
Note: For our Practical Deep Learning for Coders course, we used Keras and Theano. For Practical Deep Learning for Coders Part 2, we plan to use Keras and TensorFlow. We prefer Theano over TensorFlow, because Theano is more elegant and doesn’t make scope super annoying. Unfortunately, only TensorFlow supports some of the things we want to teach in part 2.
UPDATE: I drafted this post last week. After publishing, I saw on Twitter that Francois Chollet had announced the integration of Keras into TensorFlow a few hours earlier.