Andrew Ng taught one of the most-viewed online courses of all time--more than 1.5 million people have registered to take one of the many sequences of his free online course about machine learning. That experience spurred him to co-found Coursera. Today Ng announced that this summer he's launching sequels to that blockbuster, with a series of courses on the AI concept known as deep learning. For the past two years Ng had been applying concepts of deep learning in the commercial sector, as a chief scientist for the Chinese tech giant Baidu. But he left that company in March, and since then has been working on three undisclosed projects in AI.

Recall RNNs: with static graphs, the input sequence length will stay constant. Here we introduce datasets module which contains wrappers for popular datasets used to benchmark deep learning architectures. There are large amounts of ready to use modules in torch.nn Notice how PyTorch uses object oriented approach to define basic building blocks and give us some'rails' to move on while providing ability to extend functionality via subclassing. Here we will use tf.layers and tf.contrib.learn The code follows the official tutorial on tf.layers: So, both TensorFlow and PyTorch provide useful abstractions to reduce amounts of boilerplate code and speed up model development.

Deep convolutional neural networks (CNNs) can be applied to malware binary detection through images classification. The performance, however, is degraded due to the imbalance of malware families (classes). To mitigate this issue, we propose a simple yet effective weighted softmax loss which can be employed as the final layer of deep CNNs. The original softmax loss is weighted, and the weight value can be determined according to class size. A scaling parameter is also included in computing the weight. Proper selection of this parameter has been studied and an empirical option is given. The weighted loss aims at alleviating the impact of data imbalance in an end-to-end learning fashion. To validate the efficacy, we deploy the proposed weighted loss in a pre-trained deep CNN model and fine-tune it to achieve promising results on malware images classification. Extensive experiments also indicate that the new loss function can fit other typical CNNs with an improved classification performance.

Sales forecast is an essential task in E-commerce and has a crucial impact on making informed business decisions. It can help us to manage the workforce, cash flow and resources such as optimizing the supply chain of manufacturers etc. Sales forecast is a challenging problem in that sales is affected by many factors including promotion activities, price changes, and user preferences etc. Traditional sales forecast techniques mainly rely on historical sales data to predict future sales and their accuracies are limited. Some more recent learning-based methods capture more information in the model to improve the forecast accuracy. However, these methods require case-by-case manual feature engineering for specific commercial scenarios, which is usually a difficult, time-consuming task and requires expert knowledge. To overcome the limitations of existing methods, we propose a novel approach in this paper to learn effective features automatically from the structured data using the Convolutional Neural Network (CNN). When fed with raw log data, our approach can automatically extract effective features from that and then forecast sales using those extracted features. We test our method on a large real-world dataset from CaiNiao.com and the experimental results validate the effectiveness of our method.

If you are interested in meta-tutorials on a variety of data science topics, you have come to the right place. Of the six 7-step tutorials included herein, the first 3 tutorials cover, in order, the machine learning process from data preparation through to several different types of machine learning tasks, including both theoretical understanding and practical implementation using Python libraries. The fourth tutorial covers deep learning, mainly from an "understanding" perspective, while the final 2 cover database topics: SQL for data science, and understanding NoSQL databases. And so with a nod to Douglas Adams, and the answer to life, universe, and everything, let's have a look at 42 steps to mastering data science. Whatever term you choose, they refer to a roughly related set of pre-modeling data activities in the machine learning, data mining, and data science communities.

Everyone now is learning, or claiming to learn, Deep Learning (DL), the only field of Artificial Intelligence (AI) that went viral. Paid and free DL courses count 100,000s of students of all ages. Too many startups and products are named "deep-something", just as buzzword: very few are using DL really. Most ignore that DL is the 1% of the Machine Learning (ML) field, and that ML is the 1% of the AI field. Remaining 99% is what's used in practice for most tasks.

Time-series data arise in many fields including finance, signal processing, speech recognition and medicine. A standard approach to time-series problems usually requires manual engineering of features which can then be fed into a machine learning algorithm. Engineering of features generally requires some domain knowledge of the discipline where the data has originated from. For example, if one is dealing with signals (i.e. A similar situation arises in image classification, where manually engineered features (obtained by applying a number of filters) could be used in classification algorithms.

A common incorrect assumption about the evolution of Artificial General Intelligence (AGI), that is self-aware sentient automation, will follow the path of ever more intelligent machines and thus accelerate towards a super intelligence once human level sentient automation is created. I'm writing this article to argue that this likely will not be the case and that there will be an initial divergence of two kinds of artificial intelligences. First, let us establish here that the starting point will come from present day Deep Learning technology. More specifically, I refer these as intuition machines (see: Intuition Machines a Cognitive Breakthrough). There will be a fork in the evolution of more intelligent machines.

Mixing artificial intelligence with climate science helps researchers to identify previously unknown atmospheric processes and rank climate models. Many of the latest climate models seek to increase the detail in simulations of cloud structure. As Earth-observing satellites become more plentiful and climate models more powerful, researchers who study global warming are facing a deluge of data. Some are now turning to the latest trend in artificial intelligence (AI) to help trawl through all the information, in the hope of discovering new climate patterns and improving forecasts. "Climate is now a data problem," says Claire Monteleoni, a computer scientist at George Washington University in Washington DC who has helped to pioneer the marriage of machine-learning techniques with climate science.

Connecting the dots for a Deep Learning App … Our day to day activities is filled with Emotions and Sentiments. Ever wondered how we can identify these sentiments through computers? Oops, computers who have no brains:)? Try this Deep Learning App yourself (refresh a couple of times initially if there's Application Error): Dot 0: Deep Learning in Sentiment Analysis Sentiment analysis is a powerful application which extends its arms to the following fields in the modern day world. Alternately simple products take off with good reviews. According to Wikipedia: Sentiment analysis (sometimes known as opinion mining or emotion AI) refers to the use of natural language processing, text analysis, computational linguistics, and biometrics to systematically identify, extract, quantify, and study affective states and subjective information.