FDA requires that all computer systems used to produce, manage and report on GxP (GMP, GLC, GCP) related products be validated and maintained in accordance with specific rules. This webinar will leave you with the information needed to plan, execute and document a computer system validation effort that meets FDA compliance standards. You'll learn about the various computer system validation deliverables and how to document them through the entire process. You will learn about what must be done to ensure the system remains in a validated state. In addition, you'll learn all about how to create and maintain good FDA-compliant documentation using a strategic approach based on the System Development Life Cycle (SDLC) Methodology.

Today I'm pleased to announce the finalized details to an event I've been working on behind the scenes for quite some time: Keep reading to learn why you should attend PyImageConf. PyImageConf 2018 will take place on August 26-28th in San Francisco, CA at the Regency Hyatt. Figure 1: PyImageConf 2018 speakers include Adrian Rosebrock, François Chollet, Katherine Scott, Davis King, Satya Mallick, Joseph Howse, Adam Geitgey, Jeff Bass, and more. PyImageConf has put together the biggest names in computer vision, deep learning, and OpenCV education to give you the best possible live, hands-on training and lectures. Each speaker is respectively known for their writing, teaching, online courses, and contributions to open source projects. If this sounds like you, rest assured, this conference will be well worth your investment of time, finances, and travel.

Data Science is an interdisciplinary field that employs techniques to extract knowledge from data. As one of the fast growing fields in technology, the interest for Data Science is booming, and the demand for specialized talent is on the rise. This course takes a practical approach to Data Science, presenting solutions for common and not-so-common problems in the form of recipes. This video will begin from exploring your data using the different methods like data acquisition, data cleaning, data mining, machine learning, and data visualization, applied to a variety of different data types like structured data or free-form text. It will show how to deal with text using different methods like text normalization and calculating word frequencies.

There are publications, journals, books, week-long conferences (like CES) and endless amounts of information written about all of the incredible advancements in technology and innovation. For non-technical business owners, it's enough to make your head spin. This is not an academic, exhaustive review, but rather a quick skim at a very simplified level to give you an overview of what's top of the tech list for 2018. And there are always opportunities to learn more. Everything from this point out will be of, about and for data.

The first step is often the hardest to take, and when given too much choice in terms of direction it can often be debilitating. This post aims to take a newcomer from minimal knowledge of machine learning in Python all the way to knowledgeable practitioner in 7 steps, all while using freely available materials and resources along the way. The prime objective of this outline is to help you wade through the numerous free options that are available; there are many, to be sure, but which are the best? What is the best order in which to use selected resources? It would probably be helpful to have some basic understanding of one or both of the first 2 topics, but even that won't be necessary; some extra time spent on the earlier steps should help compensate.

To perform tasks specified by natural language instructions, autonomous agents need to extract semantically meaningful representations of language and map it to visual elements and actions in the environment. This problem is called task-oriented language grounding. We propose an end-to-end trainable neural architecture for task-oriented language grounding in 3D environments which assumes no prior linguistic or perceptual knowledge and requires only raw pixels from the environment and the natural language instruction as input. The proposed model combines the image and text representations using a Gated-Attention mechanism and learns a policy to execute the natural language instruction using standard reinforcement and imitation learning methods. We show the effectiveness of the proposed model on unseen instructions as well as unseen maps, both quantitatively and qualitatively. We also introduce a novel environment based on a 3D game engine to simulate the challenges of task-oriented language grounding over a rich set of instructions and environment states.

The combination of Deep Learning with Apache Spark has the potential for tremendous impact in many sectors of the industry. Apache Spark has quickly become a critical technology for data scientists to explore, understand and transform massive datasets and to build and train advanced machine learning models. Machine learning and AI has begun to unlock new possibilities that are creating a competitive advantage for companies. However, companies continue to struggle to increase the productivity of data scientists. The biggest hurdle to accelerate innovation has been the time to train, validate and deploy models.

About this course: This course will cover the major techniques for mining and analyzing text data to discover interesting patterns, extract useful knowledge, and support decision making, with an emphasis on statistical approaches that can be generally applied to arbitrary text data in any natural language with no or minimum human effort. Detailed analysis of text data requires understanding of natural language text, which is known to be a difficult task for computers. However, a number of statistical approaches have been shown to work well for the "shallow" but robust analysis of text data for pattern finding and knowledge discovery. You will learn the basic concepts, principles, and major algorithms in text mining and their potential applications.

What is machine learning / ai? How to lean machine learning in practice? Some people conceive it the "steam engine" of our century and one thing is certain: It will drastically change the world. Neural Networks (often referred to as deep learning) are particular interesting. But there are several questions to answer.

About this course: Bayesian methods are used in lots of fields: from game development to drug discovery. They give superpowers to many machine learning algorithms: handling missing data, extracting much more information from small datasets. Bayesian methods also allow us to estimate uncertainty in predictions, which is a really desirable feature for fields like medicine. When Bayesian methods are applied to deep learning, it turns out that they allow you to compress your models 100 folds, and automatically tune hyperparametrs, saving your time and money. In six weeks we will discuss the basics of Bayesian methods: from how to define a probabilistic model to how to make predictions from it.