They can correctly mitigate the effects of latency without your organisation having to unnecessarily spend money on ever increasingly large bandwidths, WAN Optimisation, SD-WAN and WAN optimisation solutions. Boulton also explains: "SD-WANs allow companies to set up and manage networking functionality, including VPNs, WAN optimisation, VoIP and firewalls, using software to program traffic routing typically conducted by routers and switches. What's certain is that data acceleration makes big data and predictive analytics increasingly viable. On the other hand, data acceleration solutions can create performance increases.
About this course: Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of random variables that interact with each other. They are also a foundational tool in formulating many machine learning problems. It describes the two basic PGM representations: Bayesian Networks, which rely on a directed graph; and Markov networks, which use an undirected graph. The course also presents some important extensions beyond the basic PGM representation, which allow more complex models to be encoded compactly.
About this course: In previous courses in the Specialization, we have discussed how to sequence and compare genomes. In the first half of the course, we would like to ask how an individual's genome differs from the "reference genome" of the species. The approach we will use is based on a powerful machine learning tool called a hidden Markov model. Finally, you will learn how to apply popular bioinformatics software tools applying hidden Markov models to compare a protein against a related family of proteins.
About this course: This one-week accelerated on-demand course provides participants a a hands-on introduction to designing and building machine learning models on Google Cloud Platform. Through a combination of presentations, demos, and hand-on labs, participants will learn machine learning (ML) and TensorFlow concepts, and develop hands-on skills in developing, evaluating, and productionizing ML models. OBJECTIVES This course teaches participants the following skills: Identify use cases for machine learning Build an ML model using TensorFlow Build scalable, deployable ML models using Cloud ML Know the importance of preprocessing and combining features Incorporate advanced ML concepts into their models Productionize trained ML models PREREQUISITES To get the most of out of this course, participants should have: Completed Google Cloud Fundamentals- Big Data and Machine Learning course OR have equivalent experience Basic proficiency with common query language such as SQL Experience with data modeling, extract, transform, load activities Developing applications using a common programming language such Python Familiarity with Machine Learning and/or statistics Notes: • You'll need a Google/Gmail account and a credit card or bank account to sign up for the Google Cloud Platform free trial (Google is currently blocked in China).
About this course: A modern VLSI chip has a zillion parts -- logic, control, memory, interconnect, etc. How do we design these complex chips? Learn how to build thesA modern VLSI chip is a remarkably complex beast: billions of transistors, millions of logic gates deployed for computation and control, big blocks of memory, embedded blocks of pre-designed functions designed by third parties (called "intellectual property" or IP blocks). Topics covered will include: Computational Boolean algebra, logic verification, and logic synthesis (2-level and multi-level).
In this course you will investigate the challenges of working with large datasets: how to implement algorithms that work; how to use databases to manage your data; and how to learn from your data with machine learning tools. Regardless of whether you're already a scientist, studying to become one, or just interested in how modern astronomy works'under the bonnet', this course will help you explore astronomy: from planets, to pulsars to black holes. Course outline: Week 1: Thinking about data - Principles of computational thinking - Discovering pulsars in radio images Week 2: Big data makes things slow - How to work out the time complexity of algorithms - Exploring the black holes at the centres of massive galaxies Week 3: Querying data using SQL - How to use databases to analyse your data - Investigating exoplanets in other solar systems Week 4: Managing your data - How to set up databases to manage your data - Exploring the lifecycle of stars in our Galaxy Week 5: Learning from data: regression - Using machine learning tools to investigate your data - Calculating the redshifts of distant galaxies Week 6: Learning from data: classification - Using machine learning tools to classify your data - Investigating different types of galaxies Each week will also have an interview with a data-driven astronomy expert. Note that some knowledge of Python is assumed, including variables, control structures, data structures, functions, and working with files.
The AI (artificial intelligence) revolution is well and truly upon us, and we are at a significant watershed moment in our lives where AI could become the new electricity – pervasive and touching every aspect of our life. Consequently, fundamental human thinking skills such as entrepreneurship, strategic thinking, social leadership, connected salesmanship, philosophy, and empathy, among others, would be in even greater demand. Globally, policymakers and corporations will need to significantly revamp the education system to address technology gaps. The National Skills Development Corporation will need to evolve into'National Future Skills Development', as we as a civil society prepare to bring the future into the present!
About this course: This course will introduce the learner to information visualization basics, with a focus on reporting and charting using the matplotlib library. The second week will focus on the technology used to make visualizations in python, matplotlib, and introduce users to best practices when creating basic charts and how to realize design decisions in the framework. The third week will describe the gamut of functionality available in matplotlib, and demonstrate a variety of basic statistical charts helping learners to identify when a particular method is good for a particular problem. This course should be taken after Introduction to Data Science in Python and before the remainder of the Applied Data Science with Python courses: Applied Machine Learning in Python, Applied Text Mining in Python, and Applied Social Network Analysis in Python.
About this course: This course will introduce the learner to applied machine learning, focusing more on the techniques and methods than on the statistics behind these methods. Supervised approaches for creating predictive models will be described, and learners will be able to apply the scikit learn predictive modelling methods while understanding process issues related to data generalizability (e.g. By the end of this course, students will be able to identify the difference between a supervised (classification) and unsupervised (clustering) technique, identify which technique they need to apply for a particular dataset and need, engineer features to meet that need, and write python code to carry out an analysis. This course should be taken after Introduction to Data Science in Python and Applied Plotting, Charting & Data Representation in Python and before Applied Text Mining in Python and Applied Social Analysis in Python.
Most recently, Ng led Baidu's Artificial Intelligence Group. We also covered his recommendations for companies that are nearer to the beginning of the journey of implementing artificial intelligence, the emergence of roles like the chief artificial intelligence officer, and the industries that are most likely to be impacted by AI, as well as his comparison between the business cultures in the United States and China, among a variety of other topics. Ng: In previous rises of new technologies, such as the rise of electricity about 100 years ago and the rise of the internet about 20 years ago, many organizations began by hiring one leader to sort out the new technology and figure out how to integrate it into their organization. I wrote an article several months ago titled "Hiring Your First Chief AI Officer" that offers specific recommendations.