We present a theoretically grounded approach to train deep neural networks, including recurrent networks, subject to class-dependent label noise. We propose two procedures for loss correction that are agnostic to both application domain and network architecture. They simply amount to at most a matrix inversion and multiplication, provided that we know the probability of each class being corrupted into another. We further show how one can estimate these probabilities, adapting a recent technique for noise estimation to the multi-class setting, and thus providing an end-to-end framework. Extensive experiments on MNIST, IMDB, CIFAR-10, CIFAR-100 and a large scale dataset of clothing images employing a diversity of architectures --- stacking dense, convolutional, pooling, dropout, batch normalization, word embedding, LSTM and residual layers --- demonstrate the noise robustness of our proposals. Incidentally, we also prove that, when ReLU is the only non-linearity, the loss curvature is immune to class-dependent label noise.

Predicting business process behaviour is an important aspect of business process management. Motivated by research in natural language processing, this paper describes an application of deep learning with recurrent neural networks to the problem of predicting the next event in a business process. This is both a novel method in process prediction, which has largely relied on explicit process models, and also a novel application of deep learning methods. The approach is evaluated on two real datasets and our results surpass the state-of-the-art in prediction precision.

Please email your data science related quandaries to rachel@fast.ai. Note that questions are edited for clarity and brevity. In the last week I received two questions with diametrically opposed premises: one was excited that machine learning is now automated, the other was concerned that machine learning takes too many years of study. Q1: I heard that Google Cloud announced that entrepreneurs can easily and quickly build on top of ML/NLP APIs. Is this statement true: "The future of ML and data post Google Cloud - the future is here, NLP and speech advancements have been figured out by Google and are accessible by API. The secret sauce has been commoditized so you can build your secret sauce on top of it. The time to secret sauce is getting shorter to shorter"?

Automatic emotion recognition from speech is a challenging task which significantly relies on the emotional relevance of specific features extracted from the speech signal. In this study, our goal is to use deep learning to automatically discover emotionally relevant features. It is shown that using a deep Recurrent Neural Network (RNN), we can learn both the short-time frame-level acoustic features that are emotionally relevant, as well as an appropriate temporal aggregation of those features into a compact sentence-level representation. Moreover, we propose a novel strategy for feature pooling over time using attention mechanism with the RNN, which is able to focus on local regions of a speech signal that are more emotionally salient. The proposed solution was tested on the IEMOCAP emotion corpus, and was shown to provide more accurate predictions compared to existing emotion recognition algorithms.

Video Recognition of Basketball Movement: Combining the analysis of videos from sporting events with deep learning services, athletes' performance can be profiled to determine their behavior, which provides valuable data analysis for the sports industry Image Processing: Identifying and describing images accurately through image and caption processing to allow machines to "read" images instantaneously Smart Customer Hotline: This speech recognition technology automatically records voice messages of customer service representatives into text format and structures the content and key messages to allow for monitoring of customer service quality, analysis of consumer sentiment and risk control Real-time Broadcast Transliteration: Real-time transcription of audio of live broadcasts into subtitles, which can monitor and edit content during live shows Customized Recommendation: Built on the basis of big data analytics, providing customized service and support based on users' purchasing ...

In a previous post I discussed the promising applications for deep learning in the enterprise. The greatest potential for deep learning is in adding business-relevant structure to less-structured, sense-like data -- such as images, audio and other sensor data. How quickly does the tone and affect of a support call from a frustrated customer change, broken down by support rep? It's that time-to-mollification that matters to your business, not the raw sound data. Generally when training machine learning algorithms (and deep nets are an extreme example of this), the more data the better. There's a persistent danger of "overfitting" your data -- performing very well on the training set, but poorly on new data.

But deciding to build an app in the first place was driven by a broader purpose. As one doctor put it, "If you want to do world-class sociology research, build Facebook."

I get a lot of questions about how to learn TensorFlow and Deep Learning. I'll often hear, "How do I start learning TensorFlow?" or "How do I start learning Deep Learning?". My answer is, "Learn Deep Learning and TensorFlow at the same time!". See, it's not easy to learn one without the other. Of course, you can use other libraries like Keras or Theano, but TensorFlow is a clear favorite when it comes to libraries for deep learning. And now is the best time to start.

I know OpenAI will be releasing their official version sometime soon, but for those of you who want to try out using ES right away I made my own implementation. Let me know if you have any comments on it!

Among techniques for data analysis and automation, machine learning has received strong attention the last few years. This is mainly due to the recent breakthroughs within deep learning, but has quite rightfully renewed interest also in more simple and approachable techniques. In this webinar we introduce you to what machine learning is, focus on some of the fundamental concepts and see examples of how it works in SAS.