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Tech News

3711 Articles
article-image-kali-linux-2018-1-released
Savia Lobo
04 Apr 2018
2 min read
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Kali Linux 2018.1 released

Savia Lobo
04 Apr 2018
2 min read
Kali Linux 2018.1, the first of the many versions of Kali Linux for this year is now available. This release contains all the updates and bug fixes since the last version 2017.3, released in November 2017. The 2018.1 version is boosted by the new Linux 4.14.12 kernel. This brings in an added support for newer hardware and an improved performance. This means, ethical hackers and penetration testers can now use Kali in a more efficient manner to enhance security.   The release also has two exceptional features which include, AMD Secure Memory Encryption, a new feature in the AMD processors that enables automatic encryption and decryption of DRAM. The addition of this feature means that systems will no longer be vulnerable to cold-boot attacks because, even with physical access, the memory will be not be readable. Increased Memory Limits – This release also includes a support for 5-level paging, a new feature of the upcoming processors. These new processors will support 4 PB (petabytes) of physical memory and 128 PB of virtual memory. Several packages including zaproxy, secure-socket-funneling, pixiewps, seclists, burpsuite, dbeaver, and reaver have been updated in Kali 2018.1. Also, for those using Hyper-V to run Kali virtual machines provided by Offensive Security, the Hyper-V virtual machine is now generation 2. This means, the Hyper-V VM is now UEFI-based and supports expanding/shrinking of HDD. The generation 2 also includes Hyper-V integration services, which supports Dynamic Memory, Network Monitoring/Scaling, and Replication. Know more about Kali’s latest release on the Kali Linux Blog.
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article-image-huawei-launches-hiai
Richard Gall
04 Apr 2018
2 min read
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Huawei launches HiAI

Richard Gall
04 Apr 2018
2 min read
Huawei launched the P20 to considerable acclaim. But the launch features news that's even more exciting - particularly if you're a machine learning developer/aficionado. The Chinese telecoms giant has launched HiAI, its artificial intelligence engine, to coincide with the P20's release. What is HiAI? HiAI is Huawei's AI engine. It will power applications on the new P20 mobile, giving users an experience that contains some of the most exciting artificial intelligence capabilities on the planet. But more importantly, it will also open up new opportunities for mobile developers and machine learning engineers. Engineers can now download the Driver Development Kit (DDK), IDE and SDK to begin using HiAI. HiAI's key features Huawei has made sure HiAI brings a range of artificial intelligence features - it certainly looks like it should be enough to ensure they are competing with other innovators in the space. Here are some of the key features of the software: Automatic Speech Recognition - this isn't available outside of China at the moment. Essentially, it turns human speech into text. Natural Language Understanding engine - The Natural Language Understanding engine complements the work done by the ASR engine above. Essentially, it allows a computer to 'interpret' various dimensions of human language and 'act' accordingly. Computer vision - Computer vision is what makes a number of popular mobile apps possible - for example, in aging software, or even Snapchat where you can add filters. HiAI includes a computer vision engine which is capable of facial and object recognition HiAI is going to only make Huawei's new phone even better - the more applications that are able to utilize artificial intelligence, the more attractive the phone will be to consumers. Certainly, Huawei is an underrated giant of the telecoms space, particularly when it comes to consumer tech. With its new artificial intelligence engine, it might have created something that could be the beginning of more success and greater market share outside of China. Learn more on Huawei's website. Source: XDA
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article-image-sails-js-1-0-has-arrived-on-the-shores
Sugandha Lahoti
04 Apr 2018
2 min read
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Sails.js 1.0 has arrived on the shores

Sugandha Lahoti
04 Apr 2018
2 min read
Sails v1.0 is here! Sails.js is an MVC framework for Node.js, to easily build custom, enterprise-grade Node.js apps. It combines the familiarity of working with MVC pattern of frameworks like Ruby on Rails, with the requirements of modern apps, such as data-driven APIs with a service-oriented architecture. Version 1.0 promises to provide a better developer experience over backward compatibility. Because of this, the upgrade to Sails 1.0 may include more breaking changes than previous versions. Here’s an overview of what's changed in this release and some of the new features you might want to take advantage of in your app. Sails v1.0 no longer supports Node v0.x; the earliest version it supports is Node 4.x. Sails v1.0 introduces custom builds. These custom builds provide more control over an app’s dependencies as certain core hooks are now installed as direct dependencies of an app. It also makes npm install sails run considerably faster. The new Sails also comes with several improvements in app configurations. This includes: datastores, which is by far the biggest change in app configuration. Datastores represent the data sources configured for an app. automatic install of lodash and async are now customizable to any version. view engine configuration syntax has been normalized to be consistent with the approach in Express v4+. The new blueprint API is getting expanded to include a new endpoint which might require developers to make changes in the client-side code. Most importantly, Sails v1.0 comes with a new release of Waterline ORM (v0.13). This Waterline version introduces full support for SQL transactions, picking/omitting attributes in result sets, dynamic database connections, and more granular control over query behavior. It also comes with a major stability and performance overhaul. These cover the majority of changes that Sails version 1.0 has introduced. A comprehensive list of all changes along with the necessary code files required to upgrade to the version 1.0 is available on the Sails Documentation.
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article-image-apple-steals-ai-chief-google
Richard Gall
04 Apr 2018
2 min read
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Apple steals AI chief from Google

Richard Gall
04 Apr 2018
2 min read
Google are the leaders when it comes to artificial intelligence. Apple have somewhat fallen behind - where Google are perhaps known more for technical innovation and experimentation, Apple's success is built on it's focus on design and customer experience. But that might be changing thanks to a high profile coup. "Apple have hired Google's chief of search and artificial intellience", the New York Times reports. John Giannandrea, after 8 years working at Google, will be joining Apple to help drive the organization's machine learning and artificial intelligence projects forward. Anyone who has used Siri will know that Apple have some catching up to do in terms of conversational UI - Amazon's Alexa and Google Assistant have captured the marketplace and seem to be defining the future. One of the reasons Apple has struggled to keep up the pace with the likes of Google and Facebook, as noted by a number of news sites, is that they have a completely different approach to user data. As we've seen in recent weeks, Facebook have a huge wealth of data on users that expands beyond the limits of the platform - Google, in defining the foundations of many people's experiences of search, also has a huge amount of data on users. As the New York Times explains: Apple has taken a strong stance on protecting the privacy of people who use its devices and online services, which could put it at a disadvantage when building services using neural networks. Researchers train these systems by pooling enormous amounts of digital data, sometimes from customer services. Apple, however, has said it is developing methods that would allow it to train these algorithms without compromising privacy. Giannandrea's perspective on AI would seem to be well-aligned with Apple's philosophy. In a number of interviews and conference talks, he has played down talks of automation and human's becoming obsolete, instead urging people to consider the biases and ethical considerations of artificial intelligence. Read more: Apple Recruits Google's Search and AI Chief John Giannandrea to Help Improve Siri [Gizmodo] Apple hires Google’s former AI boss to help improve Siri [The Verge]  
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Sugandha Lahoti
03 Apr 2018
2 min read
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D3 5.0 is out!

Sugandha Lahoti
03 Apr 2018
2 min read
D3.js, the popular javascript library is now available in version 5.0. This version D3 5.0, introduces only a few non-backward-compatible changes. D3.js is a JavaScript library for manipulating documents based on data. D3 combines powerful visualization components and a data-driven approach to DOM manipulation. It helps bring data to life using HTML, SVG, and CSS without restriction to a proprietary framework. Here are the most notable changes available in D3 5.0: D3 5.0 now uses Promises instead of asynchronous callbacks to load data. Promises simplify the structure of asynchronous code, especially in modern browsers that support async and await. D3 now also uses the Fetch API instead of XMLHttpRequest where the d3-request module has been replaced by d3-fetch. D3 5.0 also deprecates and removes the d3-queue module. Developers can use Promise.all to run a batch of asynchronous tasks in parallel, or a helper library such as p-queue to control concurrency. D3 no longer provides the d3.schemeCategory20 categorical color schemes. It now includes d3-scale-chromatic, which implements excellent schemes from ColorBrewer, including categorical, diverging, sequential single-hue and sequential multi-hue schemes. It also provides implementations of marching squares and density estimation via d3-contour. There are two new d3-selection methods: selection.clone for inserting clones of the selected nodes, and d3.create for creating detached elements. In addition, D3’s package.json no longer pins exact versions of the dependent D3 modules. This fixes an issue with duplicate installs of D3 modules. As a developer you can be assured that the API has been very stable since the release of 4.0. The only significant breakage will be in adopting modern asynchronous patterns i.e. promises and Fetch. You can download the latest version from d3.zip. The latest release can also be linked directly by copying this snippet: <script src="https://d3js.org/d3.v5.min.js"></script> The list of entire changes and code files are available in the release notes.
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article-image-data-science-news-daily-roundup-2nd-april-2018
Packt Editorial Staff
02 Apr 2018
2 min read
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Data Science News Daily Roundup – 2nd April 2018

Packt Editorial Staff
02 Apr 2018
2 min read
Apache Releases Trafodion, SAP announces general availability of SAP Predictive Analytics application edition, Pachyderm 1.7, and more in today’s top stories and news around data science, machine learning, and deep learning. Top Data Science news of the Day The 5 biggest announcements from TensorFlow Dev Summit 2018 Other Data Science News at a Glance Apache Releases Trafodion, a webscale SQL-on-Hadoop solution. Apache Trafodion has moved from incubator status to become a high level project. Trafodion enables transactional or operational workloads on Apache Hadoop. Read more on I Programmer SAP has announced general availability of the application edition of SAP Predictive Analytics software, to help enterprise clients harness machine learning. With this, one can create and manage predictive models that deliver powerful data-driven insights to every business user across the enterprise in real-time. Read more on inside SAP IBM’s GPU-Accelerated Semantic Similarity Search at Scale Shows ~30000x Speed Up. The proposed model is a linear-complexity RWMD that avoids wasteful and repetitive computations and reduces the average time complexity to linear. Read more on IBM Research Blog Announcing Pachyderm 1.7, an open source and enterprise data science platform that is enabling reproducible data processing at scale. Read more on Medium Mobodexter announces general availability of Paasmer 2.0, a dockerized version of their IoT Edge software that removes the hardware dependency to run Paasmer Edge Software. Paasmer becomes one of the few IoT software platforms in the world to add the Docker capability on the IoT Edge. Read more on Benzinga Announcing AIRI: Integrated AI-Ready Infrastructure for Deploying Deep Learning at Scale. AIRI is purpose-built to enable data architects, scientists and business leaders to extend the power of the NVIDIA DGX-1 and operationalise AI-at-scale for every enterprise. Read more on Scientific Computing World
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Sugandha Lahoti
02 Apr 2018
4 min read
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The 5 biggest announcements from TensorFlow Developer Summit 2018

Sugandha Lahoti
02 Apr 2018
4 min read
The second TensorFlow Developer Summit was filled with exciting product announcements and technical talks from the TensorFlow team and guest speakers. Here are 5 major features extended to the TensorFlow machine learning framework, announced at the Summit. TensorFlow.js: Machine Learning brought to your browsers Using TensorFlow.js, developers can now define, train, and run machine learning models entirely in the browser. This open-source library can be run using Javascript and a high-level layers API. What does this mean from a developer’s perspective? TensorFlow.js allows importing of an existing, pre-trained model, say a TensorFlow or Keras model into the TensorFlow.js format. Developers can use transfer learning to re-train an imported model, using only a small amount of data. What does this mean from a user’s perspective? No need to install any libraries or drivers. Just open a webpage, and your program is ready to run. TensorFlow.js automatically supports WebGL, so it will accelerate your code when a GPU is available. With TensorFlow.js, users may also open their webpage from a mobile device, where the model will take advantage of sensor data from the mobile’s gyroscope or an accelerometer. All the data stays on the client, making TensorFlow.js useful for privacy preserving and low-latency inference. You can see TensorFlow.js in action by trying out the Emoji Scavenger Hunt game from a browser on your mobile phone. TensorFlow Hub: A library for reusable Machine Learning modules in TensorFlow The next major announcement at the TensorFlow Developer summit was the TensorFlow Hub. This platform is an aggregator to publish, discover, and reuse parts of machine learning modules in TensorFlow. Module here refers to a self-contained piece of a TensorFlow graph, along with its weights, that can be reused across other similar tasks. Model reusing helps a developer train a model using a smaller dataset, improve generalization, or speed up training. TensorFlow Hub comes with two tools that help in finding potential issues in neural networks. The first is a graphical debugger for inspecting the artificial neurons of an AI. The other visualize how well the model as a whole analyzes large amounts of data. TensorFlow Model Analysis TFMA is an open-source library that combines the power of TensorFlow and Apache Beam to compute and visualize evaluation metrics. TFMA ensures that ML models meet specific quality thresholds and behaves as expected for all relevant slices of data. TFMA uses Apache Beam to do a full pass over the specified evaluation dataset. This allows more accurate calculation of metrics and also scales up to massive evaluation datasets. TFMA allows developers to visualize model metrics over time in a time series graph. It visualizes metrics computed for a single model over multiple versions of the exported SavedModel. TFMA uses Slicing metrics to analyze the performance of a model on a more granular level. TensorFlow is now available in more languages and platforms TensorFlow Developer Summit also brought a good news for swift programmers. As of April 2018, TensorFlow for Swift will be open sourced. TensorFlow for Swift is more than just language binding for TensorFlow. It integrates first-class compiler and language support, providing the full power of graphs with the usability of eager execution. TensorFlow Lite, TensorFlow’s cross-platform solution for deploying trained ML models on mobile, also has major updates. It will now feature full support for Raspberry Pi and increased support for ops/models (including custom ops). The TensorFlow Lite core interpreter is now only 75 KB in size (vs 1.1 MB for TensorFlow) with speedups of up to 3x when running quantized image classification models. New applications and domains opened using TensorFlow TensorFlow Developer Summit also made announcements pertaining to sectors beyond the core deep learning and neural network models. The TensorFlow Probability API provides state-of-the-art methods for Bayesian analysis. This library contains building blocks like probability distributions, sampling methods, and new metrics and losses. They’ve also released Nucleus, a library for reading, writing, and filtering common genomics file formats for use in TensorFlow. This is released along with DeepVariant, an open-source TensorFlow based tool for genome variant discovery. Both these tools intend to help spur new research and advances in genomics. The TensorFlow Developer Summit also showcased a new blog, YouTube channel, and other community resources.  
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article-image-nvidia-open-sources-nvvl-library-for-machine-learning-training
Sugandha Lahoti
30 Mar 2018
2 min read
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NVIDIA open sources NVVL, library for machine learning training

Sugandha Lahoti
30 Mar 2018
2 min read
NVIDIA has open sourced NVVL, a library that provides GPU accelerated video decoding for DL training. Quick rundown of NVIDIA NVVL : The NVIDIA NVVL library uses hardware acceleration to load sequences of video frames to ease out the training of machine learning algorithms. It uses FFmpeg's libraries to parse and read the compressed packets from video files and the video decoding hardware available on NVIDIA GPU. It can off-load and accelerate the decoding of these compressed packets, providing a ready-for-training tensor in GPU device memory. NVVL can additionally perform data augmentation while loading the frames. Frames can be scaled, cropped, and flipped horizontally using the GPUs dedicated texture mapping units. It significantly reduces the demands on the storage and I/O systems during training by using compressed video files instead of individual frame image files. Thereby saving upto 40X on storage space and bandwidth. Also reducing CPU load by 2X when training on video datasets. NVVL Dependencies: CUDA Toolkit. NVIDIA NVVL works well with versions 8.0 and above. It performs better with CUDA 9.0 or later. FFmpeg's libavformat, libavcodec, libavfilter, and libavutil. These can be installed from source as in the example Dockerfiles or from the Ubuntu 16.04 packages libavcodec-dev libavfilter-dev libavformat-dev libavutil-dev. NVIDIA has also provided a super-resolution example project which quantifies the performance advantage of using NVVL. When training this example project on a NVIDIA DGX-1, the CPU load when using NVVL was 50-60% of the load seen when using a normal dataloader for .png files. There is a wrapper for PyTorch available as most users will want to use the deep learning framework wrappers rather than using the library directly. For a complete list of details and code files, visit the NVIDIA Github.
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article-image-data-science-news-daily-roundup-29th-march-2018
Packt Editorial Staff
29 Mar 2018
3 min read
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Data Science News Daily Roundup – 29th March 2018

Packt Editorial Staff
29 Mar 2018
3 min read
TensorFlow 1.7.0 is out, March release of SQL Operations Studio is now available, Google announced the integration of NVIDIA® TensorRTTM and TensorFlow, Introducing new HP Z8 workstation for machine learning, and more in today’s top stories around machine learning, deep learning, and data science news. Top Data Science news of the Day TensorFlow 1.7.0 released! Peep-in to see new updates and improvements Other Data Science News at a Glance Google announced integration of NVIDIA® TensorRTTM and TensorFlow. This new integration simplifies the path to use TensorRT from within TensorFlow with world-class performance. Read more on Google Developers Blog. Google Cloud Platform introduces Cloud Text-to-Speech powered by DeepMind WaveNet technology. Cloud Text-to-Speech program lets you choose from 32 different voices,  12 languages and variants. It can correctly pronounce complex text such as name, date, time and address for authentic sounding speech right out of the gate. Read more on Google Cloud Platform blog. HP introduced its new HP Z8 workstation for machine learning development. The machine is based on Nvidia’s Quadro GV100 platform, the latest version of its Quadro graphics processing unit (GPU) for workstations. Read more on Venture Beat NVIDIA and Arm to integrate. NVIDIA’s open-source Deep Learning Accelerator (NVDLA) architecture into Arm’s Project Trillium platform for machine learning. Read more on Forbes Announcing the release of TopN, an open source PostgreSQL extension that returns the top values in a database. The TopN extension enables you to serve instant and approximate results to TopN queries. Read more on PostgreSQL. Nvidia open sources NVVL: a library that provides GPU accelerated video decoding for DL training. With NVVL, one can save 40X on storage space and bandwidth, reduce CPU load by 2X when training on video datasets. It’s great for GPU dense systems like DGX-2. Read more on GitHub. Google uses Machine Learning to Discover Neural Network Optimizers. Neural Optimizer Search makes use of a recurrent neural network controller which is given access to a list of simple primitives that are typically relevant for optimization.  Read more on Google Research Blog The March release of SQL Operations Studio is now available.Read more on SQL Server Blog. Ripple Joins Hyperledger Blockchain Consortium. Through this partnership with Hyperledger, Ripple developers will be able to access Interledger Protocol (ILP) in Java for enterprise use. Read more on Coindesk pgbedrock is a new open source tool for managing access in one’s @postgresql cluster. pgbedrock is an application for managing the roles, memberships, schema ownership, and most importantly the permission for tables, sequences, and schema in a Postgre database. Read more on GitHub Bing announces improvements and new scenarios to its intelligent search features which tap into advances in AI to provide people with more comprehensive answers, faster. Read more on Bing’s Blog
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article-image-tensorflow-1-7-0-released-updates-and-improvements
Savia Lobo
29 Mar 2018
2 min read
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TensorFlow 1.7.0 released: updates and improvements

Savia Lobo
29 Mar 2018
2 min read
Early this month, TensorFlow released its major version 1.6.0. Soon after that they announced rc-0 and rc-1 for TensorFlow 1.7.0. And to our surprise, TensorFlow 1.7.0 has arrived much sooner than expected! Clearly moving quickly is essential to the TensorFlow team. Both the rc-0 and rc-1 gave us a starter on what might be expected in the TF 1.7.0. This major release contains with some major improvements, features, bug fixes, and other changes. Major features and improvements in TensorFlow 1.7.0: Eager mode is moving out of contrib, try tf.enable_eager_execution(). EGraph rewrites emulating fixed-point quantization compatible with TensorFlow Lite are now supported by new tf.contrib.quantize package. Easy customize gradient computation are now available with tf.custom_gradient. TensorBoard Debugger Plugin, the graphical user interface (GUI) of TensorFlow Debugger (tfdbg), is now in alpha. Experimental support for reading a sqlite database as a Dataset with new tf.contrib.data.SqlDataset. Distributed Mutex / CriticalSection added to tf.contrib.framework.CriticalSection. Better text processing with tf.regex_replace. Easy, efficient sequence input with tf.contrib.data.bucket_by_sequence_length Bug fixes in TF 1.7.0  version include: Added MaxPoolGradGrad support for XLA and disabled CSE pass from Tensorflow in XLA. Added support for building C++ Dataset op kernels as external libraries, using the tf.load_op_library() mechanism. Added support for scalars in tf.contrib.all_reduce. Deprecated tf.contrib.learn. Additional changes are: Added library for statistical testing of samplers and helpers to stream data from the GCE VM to a Cloud TPU. Added TensorSpec to represent the specification of Tensors. Integrated TPUClusterResolver with GKE's integration for Cloud TPUs and also ClusterResolvers with TPUEstimator. Fixed MomentumOptimizer lambda Constant folding pass is now deterministic. A support for float16 dtype in tf.linalg.* Read full coverage about the version release on TensorFlow’s GitHub Repository.  
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Richard Gall
28 Mar 2018
3 min read
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What is a full-stack developer?

Richard Gall
28 Mar 2018
3 min read
Full stack developer has been named as one of the most common developer roles according to the latest stack overflow survey. But what exactly does a full stack developer do and what does a typical full stack developer job description look like? Full stack developers bridge the gap between the font end and back end Full stack developers deal with the full spectrum of development, from back end to front end development. They are hugely versatile technical professionals, and because they work on both the client and server side, they need to be able to learn new frameworks, libraries and tools very quickly. There’s a common misconception that full stack developers are experts in every area of web development. They’re not – they’re often generalists with broad knowledge that doesn’t necessarily run deep. However, this lack of depth isn’t necessarily a disadvantage. Because they have experience in both back end and front end development they know how to provide solutions to working with both. But most importantly, as Agile becomes integral to modern development practices, developers who are able to properly understand and move between front and back ends is vital. From an economic perspective it also makes sense – with a team of full-stack developers you have a team of people able to perform multiple roles. What a full stack developer job description looks like Every full-stack developer job description looks different. The role is continually evolving and different organizations will require different skills. Here are some of the things you’re likely to see: HTML / CSS JavaScript JavaScript frameworks like Angular or React Experience of UI and API design SQL and experience with other databases At least one backend programming language (python, ruby, java etc) Backend framework experience (for example, ASP.NET Core, Flask) Build and release management or automation tools such as Jenkins Virtualization and containerization knowledge (and today possibly serverless too) Essentially, it’s up to the individual to build upon their knowledge by learning new technologies in order to become an expert full stack developer. Full stack developers need soft skills But soft skills are also important for full-stack developers. Being able to communicate effectively, manage projects and stakeholders is essential. Of course, knowledge of Agile and Scrum are always in-demand; being collaborative is also vital, as software development is never really a solitary exercise. Similarly, commercial awareness is highly valued - a full stack developer that understands they are solving business problems, not just software problems is invaluable.
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Sugandha Lahoti
28 Mar 2018
2 min read
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How Google’s DeepMind is creating images with artificial intelligence

Sugandha Lahoti
28 Mar 2018
2 min read
The research team at DeepMind have been using deep reinforcement learning agents to generate images as humans do. DeepMind’s AI Agents understand how digits, characters, and portraits are actually constructed instead of analyzing pixels that represent it on a screen. DeepMind’s AI agents interact with the computer paint program, placing strokes on digital canvas and changing the brush size, pressure and color. How does DeepMind generate images? As a part of the initial training process, the agent starts by drawing random strokes with no visible intent or structure. Following the reinforcement learning approach, the agent is then ‘rewarded’. This ‘encourages’ it to produce meaningful drawings. To monitor the performance of the first network, DeepMind trained a second neural network, called the discriminator. This discriminator predicts whether a particular drawing was produced by the agent, or if it was sampled from a dataset of real photographs. The painting agent is rewarded by how much it manages to “fool” the discriminator into thinking that the drawings are real. Most importantly, DeepMind’s AI agents produce images by writing graphics programs to interact with a paint environment. This is different from how a GAN works where the generator in GAN setups directly output pixels.  Moreover, the model can also apply what it has learned on the simulated paint program to re-create characters in other similar environments. This is because the framework is interpretable in the sense that it produces a sequence of motions that control a simulated brush. Training DeepMind AI agents This agent was trained to generate images resembling MNIST digits: it was shown what the digits look like, but not how they are drawn. By attempting to generate images that fool the discriminator, the agent learned to control the brush and to maneuver it to fit the style of different digits. This model was also trained to reproduce specific images on real datasets. When trained to paint celebrity faces, the agent is capable of capturing the main traits of the face, such as shape, tone, and hairstyle, much like a street artist would when painting a portrait with a limited number of brush strokes. Source: DeepMind Blog For further details on methodology and experimentation, read the research paper.
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article-image-data-science-news-daily-roundup-27th-march-2018
Packt Editorial Staff
27 Mar 2018
2 min read
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Data Science News Daily Roundup – 27th March 2018

Packt Editorial Staff
27 Mar 2018
2 min read
Scala 2.12.5 releases, Reticulate package for interfacing R and Python, JSON Comes to CockroachDB, and more in today’s top stories around machine learning, deep learning, and data science news. Top Data science News Stories of the Day Scala 2.12.5 is here! R interface to Python via the Reticulate Package. Other Data Science News at a Glance TensorFlow 1.7.0-rc1 has been released. With Tensorflow 1.7.0-rc1, TensorBoard Debugger Plugin, the graphical user interface (GUI) of TensorFlow Debugger (tfdbg), is now in alpha. Also, Eager mode is moving out of contrib. See the full release notes on GitHub. CockroachDB has announced support for JSON in their most recent 2.0 Beta release. Developers can now use both structured and semi-structured data within the same database. Read more on the CockroachDB Blog. Microsoft has announced the general availability of Clustered and NonClustered Columnstore indexes in Standard tier Azure SQL Databases. Application vendors can now develop an application which leverages columnstore functionality and deploy it on both Standard and Premium performance tiers. Read more on the Microsoft Azure Blog. IBM has launched the Model Asset eXchange. MAX is effectively an App Store for free Machine Learning models to help developers and data scientists easily discover, rate and deploy AI. Read more on InsideHPC. MongoDB is now available on Google Cloud Platform (GCP) through MongoDB’s simple-to-use, fully managed Database as a Service (DBaaS) product, MongoDB Atlas. Read more on the Google Cloud Platform Blog. Kaggle API v1.1 released to programmatically create and maintain datasets via the the command line. Read more on the Kaggle Blog. The Linux Foundation has launched the LF Deep Learning Foundation. The new organization is designed to support and maintain open source innovation in artificial intelligence, machine learning, and deep learning. Read more on SDTimes. Microsoft announced that it uses Brainwave, a specialized hardware for AI computation to get more than 10 times faster performance for a machine learning model that powers functionality of its Bing search engine. Read more on VentureBeat. H2O.ai unveils H2O4GPU and Driverless AI for the Latest NVIDIA CUDA 9 and Tesla V100 Platforms. Read more on BusinessWire. AMAX, plans to showcase its Deep Learning and AI solutions at NVIDIA's GPU Technology Conference (GTC) from March 27-29 at the San Jose McEnery Convention Center. Read more on PRNewswire.
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Savia Lobo
27 Mar 2018
2 min read
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R interface to Python via the Reticulate Package

Savia Lobo
27 Mar 2018
2 min read
Announcing the Reticulate package, an R interface to Python. This package consists of comprehensive set of tools for interoperability between Python and R. With this new package, one can: Call Python from R in several ways including R Markdown, sourcing Python scripts, importing Python modules, and using Python interactively within an R session. Translate between R and Python objects (for example, between R and Pandas data frames, or between R matrices and NumPy arrays). Bind to different versions of Python including virtual environments and Conda environments in a flexible manner. Reticulate embeds a Python session within one’s R session, enabling seamless, high-performance interoperability. It can dramatically streamline the workflow for R developers who use Python for their experiments or for a member of data science team that use both the languages. Python in R Markdown The reticulate package also includes a Python engine for R Markdown which has following  features: It can run Python chunks in a single Python session embedded within one’s R session (shared variables/state between Python chunks) Prints Python output, including graphical output from matplotlib. Access to objects created within Python chunks from R using the py object (e.g. py$x would access an x variable created within Python from R). Access to objects created within R chunks from Python using the r object (e.g. r.x would access to x variable created within R from Python) Built in conversion for many Python object types is provided, including NumPy arrays and Pandas data frames. One can also use Pandas to read and manipulate data, and easily plot the Pandas data frame using ggplot2. Read more about the Reticulate package in detail on R Studio GitHub Repo  
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Richard Gall
27 Mar 2018
7 min read
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How cybersecurity can help us secure cyberspace

Richard Gall
27 Mar 2018
7 min read
With cybercrime on the rise, companies have started adopting the hard ways of preventing system breaches. Cybersecurity has become the need of the hour. This article will explore how cyberattacks bring companies down to their knees giving rise to cybersecurity. The article also looks at some of the cybersecurity strategies that an organization can adopt to safeguard itself from the prevalent attacks. Malware, Phishing, Ransomware, DDoS - these terms have become widespread today due to the increasing number of cyberattacks. The cyber threats that organizations face have grown steadily during the last few years and can disrupt even the most resilient organizations. 3 cyber attacks that shook the digital world 2011: Sony Who can forget the notorious Sony hack of April 2011? Sony’s PlayStation Network was hacked by a hacking group called “OurMine,” compromising the personal data of 77 million users. This cyberattack made Sony pay more than 15 million dollars in compensation to the people whose accounts were hacked. A hack made possible through a simple SQL inject could have been prevented using data encryption. Not long after this hack, in 2014, Sony Pictures was attacked through a malware by a hacker group called “Guardians of Peace” stealing more than 100 terabytes of confidential data. Sony had once again not paid heed to its security audit, which showed flaws in the firewall and several routers and servers resulting in the failure of infrastructure management and a monetary loss of 8 million dollars in compensation. 2013: 3 billion Yahoo accounts hacked Yahoo has been the target of the attackers thrice. During its takeover by Verizon, Yahoo disclosed that every one of Yahoo's 3 billion accounts had been hacked in 2013. However, one of the worst things about this attack was that it was discovered only in 2016, a whopping two years after the breach. 2017: WannaCry One of the most infamous ransomware of 2017, WannaCry spanned more than 150 countries targeting businesses running outdated Windows machines by leveraging some of the leaked NSA tools. The cyber attack that has been linked to North Korea hit thousands of targets, including public services and large corporations. The effects of WannaCry were so rampant that Microsoft, in an unusual move to curb the ransomware, released Windows patches for the systems it had stopped updating. On a somewhat unsurprising note, WannaCry owed its success to the use of outdated technologies (such as SMBv1) and improper maintaining their systems update for months, failing to protect themselves from the lurking attack. How cyber attacks damage businesses Cyberattacks are clearly bad for business. They lead to: Monetary loss Data loss Breach of confidential information Breach of trust Infrastructure damages Impending litigations and compensations Remediations Bad reputation Marketability This is why cybersecurity is so important - investing in it is smart from a business perspective as it could save you a lot of money in the long run. Emerging cybersecurity trends Tech journalist and analyst Art Wittmann once said "the idea that security starts and ends with the purchase of a prepackaged firewall is simply misguided". It's a valuable thing to remember when thinking about cybersecurity today. It's about more than just buying software; it's also about infrastructure design, culture and organizational practices. Cybersecurity is really a range of techniques and strategies designed to tackle different threats from a variety of sources. Gartner predicts that worldwide cybersecurity spending will climb to $96 billion in 2018. This rapid market growth is being driven by numerous emerging trends, including: Cloud computing Internet of things Machine learning Artificial Intelligence Biometrics and multi-factor authentication Remote access and BYOD--Bring your own device Effective cybersecurity strategies The most effective strategy to mitigate and minimize the effects of a cyberattack is to build a solid cybersecurity. Here are some of the ways in which an organization can strengthen their cybersecurity efforts: Understand the importance of security In the cyberage, you have to take the role of security seriously. You need to protect the organization with the help of a security team. When building a security team, you should take into accountthe types of risks that could affect the organization, how these risks will impact the business, and remedial measures in case of a breach Top notch security systems You cannot compromise on the quality of systems installed to secure your systems. Always remember what is at stake. Shoulda situation of attack arise, you need the best quality of security for your business. Implement a Red and Blue Team The organization must use the Red Team and Blue Team tactics, where the Red Team tactics can be used in penetration for accessing sensitive data, and the Blue Team tactics will defend your system from complex attacks. This team can be appointed internally or this job could be outsourced to the experts. Security audits Security audits are conducted with the aim of protect, detect, and respond. The security team must actively investigate their own security systems to make sure that everything is at par to defend against the lurking attack if it should occur. The security team must also be proactive with countermeasures to defend the organization walls against these malicious lurkers. Employees must also be properly educated to take proper precautions and act wisely in case of occurrence of a breach. Continuous monitoring Securing your organization against cyberattacks is a continuous process. It is not a one-time-only activity. The security team must be appointed to do regular audits of the security systems of the organizations. There should be a systematic and regular process, penetration testing must be conducted at regular intervals. The results of these tests must be looked at seriously to take mitigation steps to correct any weak or problematic systems. Enhance your security posture In an event of a breach, once the security team has confirmed the breach, they need to react quickly. However, don't start investigating without a plan. The compromised device should be located, its behavior should be analyzed and remedial actions should be underway. Vigilance In the words of the world’s most famous hacker, Kevin Mitnick, “Companies spend millions of dollars on firewalls, encryption,and secure access devices, and its money wasted; none of these measures address the weakest link in the security chain.” It cannot be stressed enough how important it is to be ever vigilant. The security team must stay current with the latest threat intelligence and always be on the lookout for the latest malicious programs that disrupt the organizations. Think ahead The question is never “if”, the real question is “when.”The attackers come sneaking when you are not looking. It is absolutely critical that organizations take a proactive stance to protect themselves by dropping the “if” attitude and adopting the “when” attitude. If you liked this post explore the book from which it was taken: Cybersecurity - Attack and Defense Strategies. Written by Yuri Diogenes and Erdal Ozkaya, Cybersecurity - Attack and Defense Strategiesuses a practical approach to the cybersecurity kill chain to explain the different phases of the attack, which includes the rationale behind each phase, followed by scenarios and examples that bring the theory into practice. Yuri Diogenes is a Senior Program Manager @ Microsoft C+E Security CxP Team and a professor at EC-Council University for their master's degree in cybersecurity program. Erdal Ozkaya is a doctor of philosophy in cybersecurity, works for Microsoft as a cybersecurity architect and security advisorand is also a part-time lecturer at Australian Charles Sturt University.
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