You will need a laptop computer with specific software installed prior to the session. When you register for the class, you will receive detailed instructions for software download and installation. R is the one of the most popular machine learning tools in use today. This course focuses on taking concepts in machine learning and applying them in practical ways. Common algorithms such as regression, clustering, and classification are explained, applied, and evaluated using R. Participants will complete exercises to solidify understanding and build skills with the intent of finishing the course with a toolkit that can be used to build R machine learning skills.

Offered by DeepLearning.AI. About TensorFlow TensorFlow is an end-to-end open-source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art in ML, and developers easily build and deploy ML-powered applications. TensorFlow is commonly used for machine learning applications such as voice recognition and detection, Google Translate, image recognition, and natural language processing. About this Specialization Expand your knowledge of the Functional API and build exotic non-sequential model types. Learn how to optimize training in different environments with multiple processors and chip types and get introduced to advanced computer vision scenarios such as object detection, image segmentation, and interpreting convolutions. Explore generative deep learning including the ways AIs can create new content from Style Transfer to Auto Encoding, VAEs, and GANs. About you This Specialization is for software and machine learning engineers with a foundational understanding of TensorFlow who are looking to expand their knowledge and skill set by learning advanced TensorFlow features to build powerful models. Looking for a place to start? Master foundational basics with the DeepLearning.AI TensorFlow Developer Professional Certificate. Ready to deploy your models to the world? Learn how to go live with the TensorFlow: Data and Deployment Specialization.

We in this paper propose a realizable framework TECU, which embeds task-specific strategies into update schemes of coordinate descent, for optimizing multivariate non-convex problems with coupled objective functions. On one hand, TECU is capable of improving algorithm efficiencies through embedding productive numerical algorithms, for optimizing univariate sub-problems with nice properties. From the other side, it also augments probabilities to receive desired results, by embedding advanced techniques in optimizations of realistic tasks. Integrating both numerical algorithms and advanced techniques together, TECU is proposed in a unified framework for solving a class of non-convex problems. Although the task embedded strategies bring inaccuracies in sub-problem optimizations, we provide a realizable criterion to control the errors, meanwhile, to ensure robust performances with rigid theoretical analyses. By respectively embedding ADMM and a residual-type CNN in our algorithm framework, the experimental results verify both efficiency and effectiveness of embedding task-oriented strategies in coordinate descent for solving practical problems.

Scala has emerged as an important tool for performing various data analysis tasks efficiently. This video will help you leverage popular Scala libraries and tools and perform core data analysis tasks with ease. This course will introduce you to Deeplearning4j; you will start with tasks such as integrating with Spark and Linear Regression with Deep Learning. Then you will make use of popular Scala libraries such as Breeze to plot your data. There is also a special focus on using Bokeh to plot your data.

If you are a Python developer who wants to master the world of data science then this book is for you. Some knowledge of data science is assumed. Derive inferences from the analysis by performing inferential statistics Evaluate and apply the linear regression technique to estimate the relationships among variables. Evaluate and apply the linear regression technique to estimate the relationships among variables. Evaluate and apply the linear regression technique to estimate the relationships among variables.