About this course: The Large Hadron Collider (LHC) is the largest data generation machine for the time being. It doesn't produce the big data, the data is gigantic. Just one of the four experiments generates thousands gigabytes per second. The intensity of data flow is only going to be increased over the time. So the data processing techniques have to be quite sophisticated and unique.

About this course: The goal of this course is to give learners basic understanding of modern neural networks and their applications in computer vision and natural language understanding. The course starts with a recap of linear models and discussion of stochastic optimization methods that are crucial for training deep neural networks. Learners will study all popular building blocks of neural networks including fully connected layers, convolutional and recurrent layers. Learners will use these building blocks to define complex modern architectures in TensorFlow and Keras frameworks. In the course project learner will implement deep neural network for the task of image captioning which solves the problem of giving a text description for an input image.

One of the most common problems learners have when jumping into Machine Learning and Data Science is the steep learning curve, and when you add to this the complexity of learning programming languages like Python or R you can get demotivated and lose interest fast. In this course you will learn the basic concepts of machine learning using a visual tool. Where you can just drag drop machine learning algorithms and all other functionality hiding the ugliness of code, making it much more easier to grasp the fundamental concepts. I will "hand-hold" you as we build from scratch 2 different types of supervised machine learning algorithms used in the real world, across several industries and I will explain where and how they are used. The course will teach you those fundamental concepts of machine learning by implementing practical exercises which are based on live examples.

The teaching of this course is done making the use of the "inverted classroom" model. This in simple terms means that instead of being introduced to the related material in a large lecture hall that limits itself to one-way communication, one can first watch the lecture that has been recorded by Geoffrey Hinton as a set of about 3 short videos at home before the commencement of the class, and then in class, takes place a much more dynamic discussion about it. If one is already registered for the class, you will be able to view all these videos on the Coursera website. Further details of how to do this will be given in the first lecture period.

In Data visualization with R course you will learn about Data visualization in a very systematic and easy way. R is a very powerful option in many software development domains. At its core, R is a statistical programming language that provides impressive tools for data mining and analysis, creating high-level graphics, and machine learning. R gives aspiring analysts and data scientists the ability to represent complex sets of data in an impressive way. By the end of the course, you will have enough knowledge and skill full of different visualization techniques, with the capacity to apply these abilities to real-world data sets.

Google's brainchild TensorFlow, in its first year, has more than 6000 open source repositories online. TensorFlow, an open source software library, is extensively used for numerical computation using data flow graphs.The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API. So if you're looking forward to acquiring knowledge on machine learning and deep learning with this powerful TensorFlow library, then go for this Learning Path. Packt's Video Learning Paths are a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before it. Let's take a look at your learning journey. You will start by exploring unique features of the library such as data flow graphs, training, visualization of performance with TensorBoard – all within an example-rich context using problems from multiple industries.

Artificial intelligence (AI) is all the rage these days. Recently, the American tech major Nvidia brought together the best minds in research, academia and industry across Hyderabad, Chennai, Mumbai, Pune, Delhi and Bengaluru. The six-city developer roadshow saw over 5,000 attendees who experienced some of the best demonstrations of AI and deep learning tools, designed to meet the challenges big data presents. "The artificial intelligence revolution is here and developers who understand AI and its application in commercial applications are in demand today," said Vishal Dhupar, managing director, Nvidia – South Asia. "The first edition of Developer Connect 2017 demonstrated the passion and desire for learning within our community of highly qualified developers and it is our responsibility at Nvidia to equip our Indian tech talent to take a leading role in the AI revolution and we stay committed to this task," he added.

Get your team access to Udemy's top 2,000 courses anytime, anywhere. Cancer Genomics Neural Networks vs k-NN Classifiers: Machine Learning for Python Hackers is a crash course in Data Science and Cancer Genomics for anyone interested in cancer research. The course starts out with loading up a cancer dataset to split train and test. This course is unique in Data Science in that it uses the mglearn library for better visualization and is dedicated to providing details as such so the student can follow along with no ambiguity.

Computational analogy and case-based reasoning (CBR) are closely related research areas. Both employ prior cases to reason in complex situations with incomplete information. Analogy research often focuses on modeling human cognitive processes, the structural alignment between a base/source and target, and adaptation/abstraction of the analogical source content. While CBR research also deals with alignment and adaptation, the field tends to focus more on retrieval, case-base maintenance, and pragmatic solutions to real-world problems. However, despite their obvious overlap in research goals and approaches, cross communication and collaboration between these areas has been progressively diminishing. CBR and computational analogy researchers stand to benefit greatly from increased exposure to each other's work and greater cross-pollination of ideas. The objective of this workshop is to promote such communication by bringing together researchers from the two areas, to foster new collaborative endeavors, to stimulate new ideas and avoid reinventing old ones.

Have you always wanted to mine twitter data? Then this course is for you. This course presents example of text mining with R. Twitter text of @pycon and @udemy is used as the data to analyze. It starts by extracting text from Twitter. The extracted text is then transformed to a corpus and then a document-term matrix.