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The more artificial intelligence and machine learning systems are used in companies, the more important a responsible approach to developing these technologies becomes. However, many organizations find it more difficult to implement the principles of responsible AI in practice rather than just talk about it. This training is intended for people who want to learn how to implement responsible AI in their organization. During it, you will learn how we do it in Google Cloud, and learn proven methods and conclusions from our activities in this area. This will help you develop your own approach to responsible AI.

"As enterprises adopt more artificial intelligence and machine learning, it becomes more important to build these technologies in a responsible way. But for many companies, truly practicing Responsible AI is not easy. If you are interested in learning how to practice Responsible AI in your organization, this course is for you. This course will introduce how Google Cloud is currently implementing Responsible AI, and summarize the best practices and lessons learned so that you can use this as a framework to build your own Responsible AI approach.

This course introduces artificial intelligence (AI) and machine learning (ML) offerings on Google Cloud for building predictive and generative AI projects. It explores the technologies, products, and tools available across the data-to-AI lifecycle, including AI foundations, development, and solutions. It aims to help data scientists, AI developers, and ML engineers advance their skills and knowledge through engaging learning activities and hands-on exercises.

This is a self-paced lab that takes place in the Google Cloud console. In this lab, you use the What-if Tool to identify potential biases in a model trained on mortgage loan applications.

“Learn about the evolution of artificial intelligence investing and online wealth management. Online investing and wealth management have become easier than ever, but will artificial intelligence investing really work? What are the problems? In this course, you'll learn how technology has changed the way we invest. Let’s learn about the principles of artificial intelligence-based online asset management platforms and robo-advisors, and consider why they are successful. Moving from a human-based data-driven investment strategy to a neural network strategy, the investment decision ability of artificial intelligence and the role of artificial intelligence and machine learning. “You are welcome.”

By incorporating machine learning into data pipelines, companies can extract more insights. In this course, we show several ways to add machine learning to data pipelines on Google Cloud Platform, depending on the level of customization required. We explain how to use AutoML when you need little or no customization. For more personalized machine learning capabilities, we show you how to use AI Platform Notebooks and BigQuery Machine Learning. We also explain how to produce machine learning solutions using Kubeflow. Additionally, there will be hands-on activities creating ML models on Google Cloud Platform using QwikLabs.

Machine learning and AI projects require managing diverse data sources, vast data volumes, model and parameter development, and conducting numerous test and evaluation experiments. Overseeing and tracking these aspects of a program can quickly become an overwhelming task. This course will introduce you to Machine Learning Operations tools that manage this workload. You will learn to use the Weights & Biases platform which makes it easy to track your experiments, run and version your data, and collaborate with your team.

Artificial intelligence (AI) and machine learning (ML) represent important developments in information technology that are transforming a wide variety of industries. The ""Innovating with Google Cloud Artificial Intelligence"" course shows how organizations can use AI and ML to transform business processes. As part of the Cloud Digital Leader learning program, the aim of this course is to help you grow professionally and develop the future of your own business.

This is a focused introductory course that aims to explain what AI ethics are, why they are important, and how Google practices AI ethics in its products. It also presents the 7 principles of Google's AI.

Stable diffusion is the world's most popular artificial intelligence image generation tool . Unlike competitors like midjourney or dall-e 2, stable diffusion is free and open source . In addition you can use it locally directly on your computer. To run stable diffusion locally, you must have an nvidia graphics card with at least 4gb of vram. This comprehensive training will allow you to fully master AI image generation with stable diffusion . You will learn all the techniques and tricks to make the best use of stable diffusion. With stable diffusion, you have the ability to create any type of image, limited only by your imagination. Use it to create photos, drawings, and illustrations for your products, blog, or even video games. You can also use it to make posters, goodies, books, and videos, and even use the resulting images for commercial projects. MOST IMPORTANT - Providing with Literally 1 Million prompts to combine with (No one has Access) - Providing step wise guide to Make Money Using Stable Diffusion This comprehensive course will teach you how to: - Install stable diffusion on your local machine - Generate images from text - Optimize the generated images for a professional result - Write text that will be effectively interpreted by stable diffusion's ai - Enlarge and enhance generated images - Create an image based on another image - Utilise various online tools to assist with image generation - Make Commissions Using Stable Diffusions. Stable diffusion is a highly versatile tool with unlimited potential for generating and using images. By completing this creative training, you will become a pro at using stable diffusion and may even find it addictive. The possibilities are endless.

480 Questions designed by a Salesforce Expert with 14 Certificates. - Unlock your path to Salesforce success with our comprehensive Salesforce AI Associate Practice Exams course. Designed to propel you towards achieving your Salesforce Certified Associate AI certification, this test exam offers an in-depth exploration of the key concepts and skills required for the exam. - Welcome to the Salesforce Associate AI Certification practice exam. Please note that these questions are for practical purposes only and should not be considered as the exact questions that will appear on the Salesforce Certified Associate AI exam. This practice exam assesses your knowledge in key areas outlined in the Salesforce Certified AI Associate Exam Guide. Key areas to be evaluated in this certification include, but are not limited to: 1. AI Basics: Understanding fundamental concepts of artificial intelligence. 2. Salesforce Einstein: Knowledge of Salesforce's AI platform and its capabilities. 3. AI Implementation: Ability to implement AI solutions within the Salesforce ecosystem. 4. Data Analysis: Proficiency in analyzing and utilizing data for AI-driven insights. 5. Predictive Modeling: Skills in building predictive models using AI tools. Be sure to study Salesforce's official materials and exam guides from the provided link for comprehensive preparation in these areas. Your success in the actual certification exam will depend on a thorough understanding of these topics. Best of luck!

Recently, we have seen a shift in AI that wasn't very obvious. Generative Artificial Intelligence (GAI) - the part of AI that can generate all kinds of data - started to yield acceptable results, getting better and better. As GAI models get better, questions arise e.g. what will be possible with GAI models? Or, how to utilize data generation for your own projects? In this course, we answer these and more questions as best as possible. There are 3 angles that we take: 1. Application angle: we get to know many GAI application fields, where we then ideate what further projects could emerge from that. Ultimately, we point to good starting points and how to get GAI models implemented effectively. The application list is down below. 2. Tech angle: we see what GAI models exist. We will focus on only relevant parts of the code and not on administrative code that won't be accurate a year from now (it's one google away). Further, there will be an excursion: from computation graphs, to neural networks, to deep neural networks, to convolutional neural networks (the basis for image and video generation). 3. The architecture list is down below. Ethical angle/ Ethical AI: we discuss the concerns of GAI models and what companies and governments do to prevent further harm. Enjoy your GAI journey! List of discussed application fields: - Cybersecurity 2.0 (Adversarial Attack vs. Defense) - 3D Object Generation - Text-to-Image Translation - Video-to-Video Translation - Superresolution - Interactive Image Generation - Face Generation - Generative Art - Data Compression with GANs - Domain-Transfer (i.e. Style-Transfer, Sketch-to-Image, Segmentation-to-Image) - Crypto, Blockchain, NFTs - Idea Generator - Automatic Video Generation and Video Prediction - Text Generation, NLP Models (incl. Coding Suggestions like Co-Pilot) - GAI Outlook - etc. Generative AI Architectures/ Models that we cover in the course (at least conceptually): - (Vanilla) GAN - AutoEncoder - Variational AutoEncoder - Style-GAN - conditional GAN - 3D-GAN - GauGAN - DC-GAN - CycleGAN - GPT-3 - Progressive GAN - BiGAN - GameGAN - BigGAN - Pix2Vox - WGAN - StackGAN - etc.