Increasing number of parameters, latency, resources required to train etc have made working with deep learning tricky. Google researchers, in an extensive survey, have found common challenging areas for deep learning practitioners and suggested key checkpoints to mitigate these challenges. According to Gaurav Menghani of Google Research, if one were to deploy a model on smartphones where inference is constrained or expensive due to cloud servers, attention should be paid to inference efficiency. And if a large model has to be trained from scratch with limited training resources, models that are designed for training efficiency would be better off. According to Menghani, practitioners should aim to achieve pareto-optimality i.e. any model we choose should have the best of tradeoffs.

In this section, we will learn about the course structure and the meaning of some key terms associated with HR Analytics.

The DeepLearning.AI TensorFlow: Advanced Techniques Specialization introduces the features of TensorFlow that provide learners with more control over their model architecture, and gives them the tools to create and train advanced ML models. This Specialization is for early and mid-career 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.

We will train a support vector machine to predict whether a new patient has diabetes based on such measurements. In this one hour long project-based course, you will learn the basics of support vector machines using Python and scikit-learn. The dataset we are going to use comes from the National Institute of Diabetes and Digestive and Kidney Diseases, and contains anonymized diagnostic measurements for a set of female patients. We will train a support vector machine to predict whether a new patient has diabetes based on such measurements. By the end of this course, you will be able to model an existing dataset with the goal of making predictions about new data.

You're looking for a complete course on Time Series Forecasting to drive business decisions involving production schedules, inventory management, manpower planning, and many other parts of the business., right? You've found the right Time Series Analysis and Forecasting course. This course teaches you everything you need to know about different forecasting models and how to implement these models in Python. A Verifiable Certificate of Completion is presented to all students who undertake this Marketing Analytics: Forecasting Models with Excel course. If you are a business manager or an executive, or a student who wants to learn and apply forecasting models in real world problems of business, this course will give you a solid base by teaching you the most popular forecasting models and how to implement it.

If you are not living under a rock, then you might've heard of artificial intelligence quite often in the past two decades. Although artificial intelligence is not a new thing, its implications have strongly influenced various sectors in the past couple of years, drawing more attention to the tech radar. Today, starting from virtual assistants to self-driving cars, everything is entrenched by artificial intelligence. If you are looking for a long future in technology, then you should take up AI and ML courses to shape your career. Artificial intelligence and machine learning courses pave the way to keep your goals high as a developer and help you get the highest paying jobs.

In this course, you will see how to use advanced machine learning techniques to build more sophisticated recommender systems. Machine Learning is able to provide recommendations and make better predictions, by taking advantage of historical opinions from users and building up the model automatically, without the need for you to think about all the details of the model. At the end of this course, you will learn how to manage hybrid information and how to combine different filtering techniques, taking the best from each approach. You will know how to use factorization machines and represent the input data accordingly. You will be able to design more sophisticated recommender systems, which can solve the cross-domain recommendation problem.

Establish an AI aggregator of training programs to attract adult learners and encourage lifelong learning. AI algorithms could guide users on whether they need to upskill or reskill for a new profession and shortlist relevant training programs. To develop accurate algorithms, governments would need to collect and organize data on market demand for jobs and skills, as well as data on training programs. Programs listed should include those that teach DELTAs correlated to work-related outcomes. Self-leadership DELTAs could be particularly important given their link to employment.

Are you looking to land a top-paying job in Data Science? Or are you a seasoned AI practitioner who want to take your career to the next level? Or are you an aspiring data scientist who wants to get Hands-on Data Science and Artificial Intelligence? If the answer is yes to any of these questions, then this course is for you! Data Science is one of the hottest tech fields to be in right now! The field is exploding with opportunities and career prospects.

To consistently deliver this integrated experience in turn requires a set of foundational capabilities that include technology, data, talent, and insights on value being created - for the learner, the business and all key stakeholders. Working in sync, these capabilities help make the overall learning experience interactive, fun, immersive and personalized. This drives not just the adoption and usage of learner-centric products and services, but also expand their reach and efficacy, at viable economics. Chegg and Pearson are two examples of firms focused on such learner-centric experiences and capabilities.