The Deep Learning Specialization consists of five different courses. The courses are free to take, but you need to sign up for a subscription of $49/month if you want access to the graded assignments or earn certificates. There is a seven day free trial. The individual courses are free, but you need to visit the course pages separately (you can't sign up to them from the Specialization page). Though the courses officially start on 15 August, the course materials for the first three courses are already available.

Here's this week's newsletter: Last week, VentureBeat invited Amazon, Facebook, Google, IBM, and other giants of AI into a big tent with brands like Coca-Cola, The New York Times, Tumi, and Walmart, as well as such promising startups as Bark.us, The gathering was MB 2017, and the need for practical AI was on nearly everyone's mind. Walmart, for instance, is using machine learning to better serve its 140 million weekly shoppers and to make new services possible. Laurent Desegur, vice president of customer experience engineering at WalmartLabs, explained the role of data science to make possible so-called Pick-up Towers within stores, which allow customers to order and pay online for items and then retrieve them, skipping the checkout lines. Desegur also described a 20-store pilot of Scan and Go shopping, a self-serve experience.

With continual advancements in artificial intelligence, it won't be long before machine learning touches more and more aspects of our lives – including the way we drive. As more autonomous cars hit the road, the way we can expect to drive in the future is shifting. In this course, you'll learn the robotics fundamentals and machine learning concepts that go into self-driving cars. The course will help you develop an understanding of artificial neural networks, and some of the complex algorithms that are applied in these vehicles. Increase your knowledge about machine learning with The Robotics & Machine Learning of Self-Driving Cars.

Long Short-Term Memory (LSTM) Recurrent Neural Networks are a powerful type of deep learning suited for sequence prediction problems. A possible concern when using LSTMs is if the added complexity of the model is improving the skill of your model or is in fact resulting in lower skill than simpler models. In this post, you will discover simple experiments you can run to ensure you are getting the most out of LSTMs on your sequence prediction problem. Get the Most out of LSTMs on Your Sequence Prediction Problem Photo by DoD News, some rights reserved. The LSTM recurrent neural network has a few key capabilities that give the method its impressive power on a wide range of sequence prediction problems.

As artificial intelligence alters work done in all manner of industries, companies and governments can help workers transition by supporting incomes and facilitating skills training. The pressure is on for companies and governments to address the ways that artificial intelligence (AI) is altering the future of work. In this video, recorded at the Aspen Ideas Festival in June, experts--Markle Foundation CEO and president Zoë Baird; Joy Buolamwini, founder of the Algorithmic Justice League at MIT Media Lab; James Fallows, national correspondent of the Atlantic; and Coursera cofounder Andrew Ng--discuss how to make the transition into this new age easier for everyone. Andrew Ng: AI is the new electricity. About 100 years ago, we started rolling out electricity in the United States, and it changed every single major industry, everything ranging from healthcare and culture to transportation, communications, and manufacturing are now all electricity powered.

Automatic decision-making approaches, such as reinforcement learning (RL), have been applied to (partially) solve the resource allocation problem adaptively in the cloud computing system. However, a complete cloud resource allocation framework exhibits high dimensions in state and action spaces, which prohibit the usefulness of traditional RL techniques. In addition, high power consumption has become one of the critical concerns in design and control of cloud computing systems, which degrades system reliability and increases cooling cost. An effective dynamic power management (DPM) policy should minimize power consumption while maintaining performance degradation within an acceptable level. Thus, a joint virtual machine (VM) resource allocation and power management framework is critical to the overall cloud computing system. Moreover, novel solution framework is necessary to address the even higher dimensions in state and action spaces. In this paper, we propose a novel hierarchical framework for solving the overall resource allocation and power management problem in cloud computing systems. The proposed hierarchical framework comprises a global tier for VM resource allocation to the servers and a local tier for distributed power management of local servers. The emerging deep reinforcement learning (DRL) technique, which can deal with complicated control problems with large state space, is adopted to solve the global tier problem. Furthermore, an autoencoder and a novel weight sharing structure are adopted to handle the high-dimensional state space and accelerate the convergence speed. On the other hand, the local tier of distributed server power managements comprises an LSTM based workload predictor and a model-free RL based power manager, operating in a distributed manner.

Graph models are relevant in many fields, such as distributed computing, intelligent tutoring systems or social network analysis. In many cases, such models need to take changes in the graph structure into account, i.e. a varying number of nodes or edges. Predicting such changes within graphs can be expected to yield important insight with respect to the underlying dynamics, e.g. with respect to user behaviour. However, predictive techniques in the past have almost exclusively focused on single edges or nodes. In this contribution, we attempt to predict the future state of a graph as a whole. We propose to phrase time series prediction as a regression problem and apply dissimilarity- or kernel-based regression techniques, such as 1-nearest neighbor, kernel regression and Gaussian process regression, which can be applied to graphs via graph kernels. The output of the regression is a point embedded in a pseudo-Euclidean space, which can be analyzed using subsequent dissimilarity- or kernel-based processing methods. We discuss strategies to speed up Gaussian Processes regression from cubic to linear time and evaluate our approach on two well-established theoretical models of graph evolution as well as two real data sets from the domain of intelligent tutoring systems. We find that simple regression methods, such as kernel regression, are sufficient to capture the dynamics in the theoretical models, but that Gaussian process regression significantly improves the prediction error for real-world data.

We advocate the use of curated, comprehensive benchmark suites of machine learning datasets, backed by standardized OpenML-based interfaces and complementary software toolkits written in Python, Java and R. Major distinguishing features of OpenML benchmark suites are (a) ease of use through standardized data formats, APIs, and existing client libraries; (b) machine-readable meta-information regarding the contents of the suite; and (c) online sharing of results, enabling large scale comparisons. As a first such suite, we propose the OpenML100, a machine learning benchmark suite of 100~classification datasets carefully curated from the thousands of datasets available on OpenML.org.

AdaBoost [9] is considered a successful Ensemble method and is commonly used in combination with traditional Machine Learning algorithms, especially Boosted Decision Trees [3]. One of the main principles behind it is the additional emphasis given to the so-called hard to classify examples from a training set. Deep Neural Networks have also had great success on many visual problems, and there are a number of benchmark datasets in this area where the state-of-the-art results are held by some Deep Learning algorithm [12, 4]. Ideas from Transfer of Learning have found applications in Deep Learning; for example, in Convolutional Neural Networks (CNNs), when sub-features learned early in the training process can be carried forward to a new CNN in order to improve generalisation on a new problem of the same domain [13]. It has also been shown that these Transfer of Learning methods reduce the "warm-up" phase of the training, where a randomly-initialised CNN would have to relearn basic feature selectors from scratch.

Google uses continuous streams of data from industry partners in order to deliver accurate results to users. Unexpected drops in traffic can be an indication of an underlying issue and may be an early warning that remedial action may be necessary. Detecting such drops is non-trivial because streams are variable and noisy, with roughly regular spikes (in many different shapes) in traffic data. We investigated the question of whether or not we can predict anomalies in these data streams. Our goal is to utilize Machine Learning and statistical approaches to classify anomalous drops in periodic, but noisy, traffic patterns. Since we do not have a large body of labeled examples to directly apply supervised learning for anomaly classification, we approached the problem in two parts. First we used TensorFlow to train our various models including DNNs, RNNs, and LSTMs to perform regression and predict the expected value in the time series. Secondly we created anomaly detection rules that compared the actual values to predicted values. Since the problem requires finding sustained anomalies, rather than just short delays or momentary inactivity in the data, our two detection methods focused on continuous sections of activity rather than just single points. We tried multiple combinations of our models and rules and found that using the intersection of our two anomaly detection methods proved to be an effective method of detecting anomalies on almost all of our models. In the process we also found that not all data fell within our experimental assumptions, as one data stream had no periodicity, and therefore no time based model could predict it.