Univ_Techs ::: To Explore Tech

     ML Kit is a mobile SDK that brings Google's machine learning expertise to Android and iOS apps in a powerful yet easy-to-use package. Whether you're new or experienced in machine learning, you can implement the functionality you need in just a few lines of code. There's no need to have deep knowledge of neural networks or model optimization to get started. On the other hand, if you are an experienced ML developer, ML Kit provides convenient APIs that help you use your custom TensorFlow Lite models in your mobile apps.      At Google I/O'18 developer conference in May, Google introduced ML Kit, a cross-platform suite of machine learning tools for the Firebase mobile development platform. ML Kit uses the Neural Network API on Android devices and is designed to compress and optimize machine learning models for mobile devices.  It brings Google’s machine learning expertise to mobile developers in a powerful and easy-to-us...

      Apple announced a new feature for developers called Create ML . It Create machine learning models for use in your app. Create ML is a machine learning framework in Swift which can be used to train the machine learning models using native Apple technologies like Swift, Xcode, and Other Apple frameworks. This means machine learning is possible within Apple ecosystem without any third-party service.  Create ML technology has following features,.. A simple approach for training the model using images, texts, and tabular data Models still can be trained using complex algorithm if you are expert Machine learning models can be created using Xcode Playground, Swift, and native Apple framework The process of creating and training module can be automated using Swift scripts.      You train a model to recognize patterns by showing it representative samples. For example, you can train a model to recognize dogs by showing it l...

          G oogle has invested a large amount of developers time and effort into the capabilities of machine learning.  Finally, the tool was developed by the Brain Team that is TensorFlow.  TensorFlow was orginally designed to be used for Machine Learning and Neural Network applications.          But now, you can use TensorFlow no need to zero  % of installation.  Google announces Colab to do this as easy. Welcome to Colaboratory!      Colaboratory is a Google research project created to help disseminate machine learning education and research. It's a Jupyter notebook environment that requires no setup to use and runs entirely in the cloud.      Colaboratory notebooks are stored in Google Drive and can be shared just as you would with Google Docs or Sheets. Colaboratory is free to use.    How to write Hello, World...

   In order to tackle with machine learning, you will have to learn few programming languages.      The increasing demand for experts in machine learning in past few years has increased curiosity to know the programming languages which one can use in machine learning. But before discussing, the best programming languages for machine learning you must have brief information about the concept of machine learning. What is machine learning?      In the field of computer science, machine learning is a part of artificial intelligence that provides your computer an ability to learn to improve its performance with data, without being programmed exclusively. In today’s technical world machine learning is one of the fastest progressing concepts on which most of the tech giant companies are heavily investing to improve their merchandise.      In 1959, Arthur Samuel used the words machine learning...

     If you are a beginner, check out our articles on ”Machine learning crash course” and “Machine learning specialization course”.      Each of these Frameworks is different from each other and takes much time to learn, during the time of making this list we took care of features other than the basic ones, User base and community & support was one of the most important parameters. Some frameworks are more mathematically oriented, and hence geared more towards statistical than neural networks. Some of them provide a rich set of linear algebra tools; some are mainly focused only on deep learning. 1. TensorFlow TensorFlow an open source software library for data-based programming across a range of tasks, which was developed by Google Brain team and initially released on 9th of November 2015, though the stable release was made available only on 27th of April this year. It is capable of doing regressions, classificat...

ML algorithms study is regarded to be the ‘Sexiest job of the 21st century’ as shown in the Harvard Business Review article. For beginners who are eager to study Machine learning basics, here is a great quick guide to the top 10 Machine Learning Algorithms used by ML programmers that you must know. Machine Learning algorithms do not require human intervention, they are able to study data and advance from experience. Learning data entails studying the function that plots input and output and studying the unseen structure from unlabeled data. Ensure you choose the right machine learning task that is appropriate to your problem. Try different Algorithms for every problem to evaluate their performance and then choose the best. Types of Machine Learning Algorithms A basic understanding of varying types of ML Algorithms will enable you to understand better on how algorithms work.   Click Here : To see the types of ML The Top Machine Learning Algorithms  1. L...

     The Artificial Intelligent(AI) fiction has nearly become a fact with Google’s new AI system that accomplishes real-world tasks over the phone. In the recent years, we are constantly listening to AI buzz and its research and evolution in natural language processing, machine learning, and deep learning. Also, experienced with few of the applications like Google Voice Search, WaveNet, etc.        In all these years the various downsides of AI are like not understanding natural languages, doesn’t engage with callers efficiently, stuck with flow and force callers to adjust to the system instead of system adjusting to the caller.      However, Google’s new technology called Google Duplex will avoid the stilted computerized voices and bring more peace by conducting natural conversations to complete real-world tasks over the phone. What are the real-world tasks accomplished by Google Duplex? ...

Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves. Supervised learning : The computer is presented with example inputs and their desired outputs, given by a "teacher", and the goal is to learn a general rule that maps inputs to outputs. As special cases, the input signal can be only partially available, or restricted to special feedback:                     Semi-supervised learning : the computer is given only an incomplete training signal: a training set with some (often many) of the target outputs missing. Active learning : the computer can only obtain training labels for a limited set of instances (based on a budget), an...

What's New in Tensorflow Machine Learning Google's TensorFlow 1.4 machine learning library adds the contributed Dataset API for working with data sources, but watch out for breakage caused by the update. TensorFlow, Google’s contribution to the world of machine learning and data science, is a general framework for quickly developing neural networks. Despite being relatively new, TensorFlow has already found wide adoption as a common platform for such work, thanks to its powerful abstractions and ease of use. TensorFlow 1.4 API additions TensorFlow Keras API The biggest changes in TensorFlow 1.4 involve two key additions to the core TensorFlow API. The  tf.keras  API allows users to employ the Keras API, a neural network library that predates TensorFlow but is quickly being displaced by it. The tf.keras API allows software using Keras to be transitioned to TensorFlow, either by using the Keras interface permanently, or as a prelude to the s...

M achine learning is one of the fastest growing fields in the technical world. All major tech giants are investing heavily in machine learning and data science to improve their products. To work in this field, you need to learn some particular programming languages and skills. To give us an idea about the real situation, IBM’s JeanFrancoisPuget used the trend search available on indeed.com and wrote a blog post that indicates how skill popularity evolves. To do this, the machine learning and data science skills were searched in conjunction with the prominent programming languages like C, Java, C++, and JavaScript. Python and R were included as they are known to be popular for machine learning and data science. Apart from the languages mentioned above, Scala and Julia were also included. Here are the interesting results that were observed: So, what do we learn from this data? The graph shows that there are a number of programming languages that...

      Machine learning is a field of computer science that gives computers the ability to learn without being explicitly programmed. Arthur Samuel, an American pioneer in the field of computer gaming and artificial intelligence, coined the term "Machine Learning" in 1959 while at IBM. Machine Learning - Techniques: Regression is used to predict numbers, such as optimum pricing, Jeffcock says. For example, it might be used to predict home prices based on the number of rooms and the size of the yard. Other uses include estimating customer lifetime value. Interestingly, Jeffcock notes, even though machine learning is one of today’s hottest buzzwords, not all the techniques are new. Regression concepts can be traced back to British statistician Sir Francis Galton, Charles Darwin’s half-cousin, as early as 1875. Classification is a “supervised” learning technique in which the system learns to identify members of a known ...