Every year a large amount of data is generated which needs to be stored and analyzed. Apache Spark allows you to process such big data. The real power and value proposition of Apache Spark is its speed and platform to execute data science tasks. Spark's unique use case is that it combines ETL, batch analytic, real-time stream analysis, machine learning, graph processing, and visualizations to allow data scientists to tackle the complexities that come with raw unstructured data sets. Spark embraces this approach and has the vision to make the transition from working on a single machine to working on a cluster, something that makes data science tasks a lot more agile.

Matrix calculations, derivatives, eigenvalues, Set Theory, functions, vectors, linear transformations, etc. are extremely important to understand the theory behind statistical methods and programming. Therefore, before starting your next MOOC or Machine Learning book it's crucial to review all those concepts again. Most schools request students to be proficient at these methods in order to graduate, but the silver lining is that it won't require too much of your time to refresh or obtain this knowledge. There are plenty of resources to start, but what worked for me was The Manga Guide to Linear Algebra, which is very simple, graphic and provides a great foundation prior getting into more complex stuff. My suggestion is to schedule some weeks to review these concepts and to use the Feynman Technique to be able to explain in simple terms each of these topics. One of the issues people face today when trying to get into a field such as Data Science is Information Overload, a term used when talking in relation to the effect of having too many resources at the disposal.

Tech giant Google has now introduced a new easy-to-learn platform called'Learn with Google AI', which are a set of educational resources developed by Machine Learning experts at the company. This platform will help people learn about concepts, develop skills and apply artificial intelligence to problems in real life. The company mentioned in a blog, "To help everyone understand how AI can solve challenging problems, we've created a resource called Learn with Google AI. This site provides ways to learn about core ML concepts, develop and hone your ML skills, and apply ML to real-world problems. From deep learning experts looking for advanced tutorials and materials on TensorFlow, to "curious cats" who want to take their first steps with AI, anyone looking for educational content from ML experts at Google can find it here."

Unity 2017 provides game and app developers with a variety of tools to implement Artificial Intelligence. Leveraging these tools via Unity's API or built-in features allows limitless possibilities when it comes to creating your game's worlds and characters. This third edition with Unity will help you break down Artificial Intelligence into simple concepts to give you a fundamental understanding of the topic to build upon. Using a variety of examples, the book then takes those concepts and walks you through actual implementations designed to highlight key concepts, and features related to game AI in Unity 5. Further on you will learn to distinguish the state machine pattern and implement one of your own. This is followed by learning how to implement a basic sensory system for your AI agent and coupling it with a Finite State Machine (FSM).

Today we are going to discuss a fundamental developer, engineer, and computer scientist skill -- command line arguments. Command line arguments are an elementary skill that you must learn how to use, especially if you are trying to apply more advanced computer vision, image processing, or deep learning concepts. If you are new to command line arguments or do not know how to use them that's okay! But you still need to take the time to educate yourself on how to use them -- this post will help you do exactly that. By the end of today's post you will have a strong understanding of command line arguments, how they work, and how to use them. Each day I receive 3-5 emails or comments from PyImageSearch readers who are struggling with command line arguments.

If you still don't get what artificial intelligence is all about, you may want to start by exploring these Google AI experiments. That may just interest you enough to take the next big step: learning more about AI. Google has designed a free online course to teach you the fundamentals of machine learning, and it's accessible to anyone with an internet connection. Google's free Machine Learning course doesn't ask you to jump straight in--you can use the filters at the beginning to narrow your focus according to your needs, including the type of content you would like to learn from and the stage of development you would like to start with. According to Google, anyone looking for educational content from Google's machine learning experts can find it here, whether you're looking fo advanced tutorials and materials on TensorFlow or just curious about the basics of AI.

In deep learning it is common to see a lot of discussion around tensors as the cornerstone data structure. Tensor even appears in name of Google's flagship machine learning library: "TensorFlow". Tensors are a type of data structure used in linear algebra, and like vectors and matrices, you can calculate arithmetic operations with tensors. A Gentle Introduction to Tensors for Machine Learning with NumPy Photo by Daniel Lombraña González, some rights reserved. Take my free 7-day email crash course now (with sample code).

Data science is an inter-disciplinary field which contains methods and techniques from fields like statistics, machine learning, Bayesian etc. They all aim to generate specific insights from the data. In this article, we are listing down some excellent data science books which cover the wide variety of topics under Data Science. This data science book is a great blend of lectures in the modern theoretical course in data science. This tutorial aims to get you familiar with the main ideas of Unsupervised Feature Learning and Deep Learning.

Bitcoin and other digital currencies are surging as the market continues to recover. The price of every big digital currency has risen by significant amounts. Bitcoin, the biggest of them, has risen nearly 13 per cent over the last day and is nearly at $10,000. Cryptocurrencies are continuing to make back many of the losses sustained when the market plunged in recent weeks. They are still far from the heights of December – when bitcoin nearly hit $20,000 – but are a long way from the dramatic lows hit when the price dropped after that.

Recurrent Neural Networks are the state of the art algorithm for sequential data and among others used by Apples Siri and Googles Voice Search. This is because it is the first algorithm that remembers its input, due to an internal memory, which makes it perfectly suited for Machine Learning problems that involve sequential data. It is one of the algorithms behind the scenes of the amazing achievements of Deep Learning in the past few years. In this post, you will learn the basic concepts of how Recurrent Neural Networks work, what the biggest issues are and how to solve them. Recurrent Neural Networks (RNN) are a powerful and robust type of neural networks and belong to the most promising algorithms out there at the moment because they are the only ones with an internal memory.