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As AI moves beyond the cloud, on-device inference is rapidly expanding to smartphones, IoT devices, robots, AR/VR headsets, and more. Billions of mobile and other edge devices are ready to run optimized AI models. This course equips you with key skills to deploy AI on device: 1. Explore how deploying models on device reduces latency, enhances efficiency, and preserves privacy. 2. Go through key concepts of on-device deployment such as neural network graph capture, on-device compilation, and hardware acceleration. 3. Convert pretrained models from PyTorch and TensorFlow for on-device compatibility. 4. Deploy a real-time image segmentation model on device with just a few lines of code. 5. Test your model performance and validate numerical accuracy when deploying to on-device environments 6. Quantize and make your model up to 4x faster and 4x smaller for higher on-device performance. 7. See a demonstration of the steps for integrating the model into a functioning Android app. Learn from Krishna Sridhar, Senior Director of Engineering at Qualcomm, who has played a pivotal role in deploying over 1,000 models on devices and, with his team, has created the infrastructure used by over 100,000 applications. By learning these techniques, you’ll be positioned to develop and deploy AI to billions of devices and optimize your complex models to run efficiently on the edge.

The aim of this course is to provide students with knowledge of AI data processing approaches for breast cancer detection. Students will complete quizzes and participate in discussions to reinforce key concepts taught in the modules. Reading assignments will be provided, including journal articles, to help them understand the module topics. This course is designed for students who are interested in pursuing a career in AI product development and would like to learn how AI can be applied to mammography. The course content focuses on the AI ​​data science paradigm along with knowledge of breast imaging. This approach to the course is unique and allows students to gain a broad understanding of AI rather than focusing on a specific implementation method. Students who complete this course will not only be able to use the knowledge they gain to obtain entry-level jobs in the AI ​​field, but will also be able to successfully complete projects due to their deep understanding of the AI ​​data science paradigm.

The goal of this course is to provide general users with a friendly, non-technical understanding of generative AI. It emphasizes the importance of transparency in AI systems, helping students understand how AI decisions are made. By emphasizing the importance of user awareness, transparency, and informed decision-making, students will be better prepared to make informed choices and interact with AI responsibly and confidently. The strategies and ideas presented will help students unleash the creative potential of generative AI while ensuring that they are ethical and protected from potential risks. The course encourages active participation and emphasizes the collective responsibility of users in shaping the future of AI. This course is designed for any employee or leader in a business that uses or plans to use AI and generative AI, or for anyone who wants to expand their knowledge in this area. The course is designed to give students a basic understanding of the topic and some of the nuances of using AI. There are no specific prerequisites for this course. A basic understanding of computers and business is helpful, but not required. An open mind and curiosity about the broader impact of AI on society will enhance the learning experience.

This course is an introduction to Vertex AI Notebooks, which are Jupyter notebook-based environments that provide a unified platform for the entire machine learning workflow, from data preparation to model deployment and monitoring. The course covers the following topics: (1) The different types of Vertex AI Notebooks and their features and (2) How to create and manage Vertex AI Notebooks.

In AI Agentic Design Patterns with AutoGen you’ll learn how to build and customize multi-agent systems, enabling agents to take on different roles and collaborate to accomplish complex tasks using AutoGen, a framework that enables development of LLM applications using multi-agents. In this course you’ll create: 1. A two-agent chat that shows a conversation between two standup comedians, using “ConversableAgent,” a built-in agent class of AutoGen for constructing multi-agent conversations. 2. A sequence of chats between agents to provide a fun customer onboarding experience for a product, using the multi-agent collaboration design pattern. 3. A high-quality blog post by using the agent reflection framework. You’ll use the “nested chat” structure to develop a system where reviewer agents, nested within a critic agent, reflect on the blog post written by another agent. 4. A conversational chess game where two agent players can call a tool and make legal moves on the chessboard, by implementing the tool use design pattern. 5. A coding agent capable of generating the necessary code to plot stock gains for financial analysis. This agent can also integrate user-defined functions into the code. 6. Agents with coding capabilities to complete a financial analysis task. You’ll create two systems where agents collaborate and seek human feedback. The first system will generate code from scratch using an LLM, and the second will use user-provided code. You can use the AutoGen framework with any model via API call or locally within your own environment. By the end of the course, you’ll have hands-on experience with AutoGen’s core components and a solid understanding of agentic design patterns. You’ll be ready to effectively implement multi-agent systems in your workflows.

Embark on a transformative learning journey to enhance your copywriting skills and master the art of AI-powered content creation. In this comprehensive project, you will forge a partnership with cutting-edge AI technology, leveraging the power of SurferSEO, Upword, and Anyword to create high-quality, engaging, and SEO-optimized content that gets results. Through hands-on tasks (setting up accounts, interacting with AI platforms, and using their capabilities to create engaging and SEO-optimized content) and hands-on exercises, you will gain experience crafting compelling copy tailored to a specific audience and content format. By the end of the project, you will have the skills and knowledge to create high-impact AI-powered content that drives conversions, increases brand awareness, and takes your copywriting to new heights. All you need is a basic understanding of content creation and copywriting principles to succeed in this project.

Generative AI is rapidly changing industries and the skills students need to enter the workforce. As the pace of technological change accelerates, universities must innovate to stay competitive and provide students with the skills they need to compete. At the same time, generative AI has the potential to transform how universities operate, from automating course planning and content creation to streamlining expense reporting and administrative functions. This course provides university leaders with a foundation to understand the key fundamentals of how generative AI works and how it can help them make effective strategic decisions. The course provides insight into the innovative potential of generative AI across all disciplines and the opportunity for higher education to lead the way in changing how students learn and how we work in the future. The course highlights what is happening in industry and the strategic opportunities that institutions should focus on to help their students succeed in the future. The course also provides the foundation an institution needs to begin innovating with generative AI, from policy to platform. The course covers key platforms and infrastructure for effectively integrating generative AI into a university environment, offering advice on prioritizing investments, effectively allocating resources, and identifying key champions to create a cross-disciplinary culture of innovation on campus.

This IBM course will teach you how to implement, train, and evaluate generative AI models for natural language processing (NLP). The course will help you acquire knowledge of NLP applications including document classification, language modeling, language translation, and fundamentals for building small and large language models. You will learn about converting words to features. You will understand one-hot encoding, bag-of-words, embedding, and embedding bags. You also will learn how Word2Vec embedding models are used for feature representation in text data. You will implement these capabilities using PyTorch. The course will teach you how to build, train, and optimize neural networks for document categorization. In addition, you will learn about the N-gram language model and sequence-to-sequence models. This course will help you evaluate the quality of generated text using metrics, such as BLEU. You will practice what you learn using Hands-on Labs and perform tasks such as implementing document classification using torchtext in PyTorch. You will gain the skills to build and train a simple language model with a neural network to generate text and integrate pre-trained embedding models, such as word2vec, for text analysis and classification. In addition, you will apply your new skills to develop sequence-to-sequence models in PyTorch and perform tasks such as language translation.

The company has been developing and implementing cloud computing solutions for decades, and is currently working on cloud computing solutions. The company has been working on cloud computing solutions for over 100 countries and regions, and is currently using Google Cloud Platform. The idea of ​​using the word "sweet" to describe the situation in the past is to use it as a basis for the development of the concept of "sweet" in the future. AutoML, a data-driven machine learning tool, and AI Platform Notebooks, BigQuery Machine Learning, and more. The cloud computing industry has been developing and implementing new technologies to improve the cloud computing experience. The cloud computing industry is powered by Qwiklabs and Google Cloud Platform, and is currently under development by the Chinese government.

In this 1-hour long project-based course, you will learn how to Create AI-generated art using NightCafe, Apply styles and techniques to customize artwork, and Produce a digital art portfolio showcasing AI creativity. By the end of this project, learners will have learned to create distinctive AI-powered artworks using NightCafe, employing diverse styles and advanced editing tools. They will have mastered generating personalized creations with custom models. Additionally, learners will be able to curate and present a professional art portfolio, showcasing their journey and skills in AI-driven artistic innovation.

Artificial Intelligence is rapidly redrawing the boundaries of the creative landscape, presenting both groundbreaking opportunities and formidable challenges, particularly in the realm of filmmaking. Today's narratives around technology suggest that the threat to our roles comes not directly from AI itself, but from those creatives who have harnessed its might to elevate their art. This is a call to action for industry professionals, sparking the need to be well-versed in AI applications. This comprehensive course is meticulously curated to empower you to comprehend this pivotal paradigm shift thoroughly and utilise AI's transformative power to enhance your creative output effectively. Specifically conceived for the people working in the creative sectors, this course demystifies the intricacies of Artificial Intelligence in the world of filmmaking. It offers a deep dive into how to wield these emergent technologies to amplify the impact of your work. Over the course of three immersive sessions, Stephen provides a comprehensive blend of foundational knowledge and pragmatic instruction. This includes exploring a wide array of AI advancements and contemplating their profound implications on the future of content creation. We will dissect the ways in which AI is revolutionising storytelling, from conception to final cut, and collaboratively develop strategies to ensure you not only adapt but thrive within this new technological renaissance. By the conclusion of this course, you'll possess not only a robust understanding of AI's implications for filmmaking but also the essential skills to navigate this new terrain with confidence. You will be well-prepared to refine your techniques, increase efficiency in your projects, and discover new horizons for your work with an AI-augmented approach. This course extends a warm invitation to independent filmmakers, screenwriters, producers, and creative freelancers of all levels. Whether you're seeking to initiate your journey in AI or looking to deepen your current understanding, no previous experience with AI is required to reap the benefits of this enriching learning experience. Join us to future-proof your skills, stay competitive, and embark on the path to becoming an AI-savvy creative in the film industry.

Prepare yourself for the Microsoft Azure AI Fundamentals - AI-900 exam with our comprehensive practice exams course designed to give you a distinct advantage. The questions in this course closely replicate the tone and format of the actual exam, providing you with a realistic test environment. Each question comes with a detailed explanation, including important "keywords" to enhance your understanding. To ensure thorough coverage of all exam domains, extensive references to Microsoft documentation have been incorporated into the explanations. Consider this course as your final pit-stop, propelling you confidently across the finish line to achieve AI-900 certification. Trust the process; you're in capable hands. Rest assured that all questions are meticulously crafted with comprehensive explanations, diagrams, and reference links. The course is continually updated with additional questions to further enhance your preparation over time. Get ready to conquer the AI-900 exam with confidence! Skills measured - Describe Artificial Intelligence workloads and considerations (15–20%) - Describe fundamental principles of machine learning on Azure (20–25%) - Describe features of computer vision workloads on Azure (15–20%) - Describe features of Natural Language Processing (NLP) workloads on Azure (15–20%) - Describe features of generative AI workloads on Azure (15–20%) About Exam This examination serves as a chance to showcase proficiency in machine learning (ML) and artificial intelligence (AI) principles, alongside a grasp of relevant Microsoft Azure services. Those undertaking this exam are expected to be acquainted with the self-paced or instructor-led learning resources provided for Exam AI-900. Designed for individuals with varying technical and non-technical backgrounds, this exam does not necessitate prior experience in data science or software engineering. While not mandatory, having a basic understanding of cloud fundamentals and client-server applications can prove advantageous.