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⭐ This is a 10-week online course for toddlers (13-24months) and their parents/carers. Baby signing can benefit ANY family with a baby or toddler. Clear communication can especially reduce frustrations for families with toddlers, helping you avoid some of the toddler turbulence🌪️ before it begins.

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El siguiente curso es una especialidad en el libro de Apocalipsis de la Biblia. Se trata sobre informar a los siervos de Dios de lo que va a suceder pronto para que estén preparados para la venida de Cristo.

Teaching4you is a tuition company that works to encourage and build confidence in students nationwide.

Duration 3 Days 18 CPD hours This course is intended for This course is geared for attendees with Intermediate IT skills who wish to learn Computer Vision with tensor flow 2 Overview This 'skills-centric' course is about 50% hands-on lab and 50% lecture, with extensive practical exercises designed to reinforce fundamental skills, concepts and best practices taught throughout the course. Working in a hands-on learning environment, led by our Computer Vision expert instructor, students will learn about and explore how to Build, train, and serve your own deep neural networks with TensorFlow 2 and Keras Apply modern solutions to a wide range of applications such as object detection and video analysis Run your models on mobile devices and web pages and improve their performance. Create your own neural networks from scratch Classify images with modern architectures including Inception and ResNet Detect and segment objects in images with YOLO, Mask R-CNN, and U-Net Tackle problems faced when developing self-driving cars and facial emotion recognition systems Boost your application's performance with transfer learning, GANs, and domain adaptation Use recurrent neural networks (RNNs) for video analysis Optimize and deploy your networks on mobile devices and in the browser Computer vision solutions are becoming increasingly common, making their way into fields such as health, automobile, social media, and robotics. Hands-On Computervision with TensorFlow 2 is a hands-on course that thoroughly explores TensorFlow 2, the brandnew version of Google's open source framework for machine learning. You will understand how to benefit from using convolutional neural networks (CNNs) for visual tasks. This course begins with the fundamentals of computer vision and deep learning, teaching you how to build a neural network from scratch. You will discover the features that have made TensorFlow the most widely used AI library, along with its intuitive Keras interface. You'll then move on to building, training, and deploying CNNs efficiently. Complete with concrete code examples, the course demonstrates how to classify images with modern solutions, such as Inception and ResNet, and extract specific content using You Only Look Once (YOLO), Mask R-CNN, and U-Net. You will also build generative dversarial networks (GANs) and variational autoencoders (VAEs) to create and edit images, and long short-term memory networks (LSTMs) to analyze videos. In the process, you will acquire advanced insights into transfer learning, data augmentation, domain adaptation, and mobile and web deployment, among other key concepts Computer Vision and Neural Networks Computer Vision and Neural Networks Technical requirements Computer vision in the wild A brief history of computer vision Getting started with neural networks TensorFlow Basics and Training a Model TensorFlow Basics and Training a Model Technical requirements Getting started with TensorFlow 2 and Keras TensorFlow 2 and Keras in detail The TensorFlow ecosystem Modern Neural Networks Modern Neural Networks Technical requirements Discovering convolutional neural networks Refining the training process Influential Classification Tools Influential Classification Tools Technical requirements Understanding advanced CNN architectures Leveraging transfer learning Object Detection Models Object Detection Models Technical requirements Introducing object detection A fast object detection algorithm YOLO Faster R-CNN ? a powerful object detection model Enhancing and Segmenting Images Enhancing and Segmenting Images Technical requirements Transforming images with encoders-decoders Understanding semantic segmentation Training on Complex and Scarce Datasets Training on Complex and Scarce Datasets Technical requirements Efficient data serving How to deal with data scarcity Video and Recurrent Neural Networks Video and Recurrent Neural Networks Technical requirements Introducing RNNs Classifying videos Optimizing Models and Deploying on Mobile Devices Optimizing Models and Deploying on Mobile Devices Technical requirements Optimizing computational and disk footprints On-device machine learning Example app ? recognizing facial expressions

Duration 4 Days 24 CPD hours This course is intended for This course is geared for attendees with Intermediate IT skills who wish to learn Computer Vision with tensor flow 2 Overview This 'skills-centric' course is about 50% hands-on lab and 50% lecture, with extensive practical exercises designed to reinforce fundamental skills, concepts and best practices taught throughout the course. Working in a hands-on learning environment, led by our Computer Vision expert instructor, students will learn about and explore how to Build, train, and serve your own deep neural networks with TensorFlow 2 and Keras Apply modern solutions to a wide range of applications such as object detection and video analysis Run your models on mobile devices and web pages and improve their performance. Create your own neural networks from scratch Classify images with modern architectures including Inception and ResNet Detect and segment objects in images with YOLO, Mask R-CNN, and U-Net Tackle problems faced when developing self-driving cars and facial emotion recognition systems Boost your application's performance with transfer learning, GANs, and domain adaptation Use recurrent neural networks (RNNs) for video analysis Optimize and deploy your networks on mobile devices and in the browser Computer vision solutions are becoming increasingly common, making their way into fields such as health, automobile, social media, and robotics. Hands-On Computervision with TensorFlow 2 is a hands-on course that thoroughly explores TensorFlow 2, the brand-new version of Google's open source framework for machine learning. You will understand how to benefit from using convolutional neural networks (CNNs) for visual tasks. This course begins with the fundamentals of computer vision and deep learning, teaching you how to build a neural network from scratch. You will discover the features that have made TensorFlow the most widely used AI library, along with its intuitive Keras interface. You'll then move on to building, training, and deploying CNNs efficiently. Complete with concrete code examples, the course demonstrates how to classify images with modern solutions, such as Inception and ResNet, and extract specific content using You Only Look Once (YOLO), Mask R-CNN, and U-Net. You will also build generative adversarial networks (GANs) and variational autoencoders (VAEs) to create and edit images, and long short-term memory networks (LSTMs) to analyze videos. In the process, you will acquire advanced insights into transfer learning, data augmentation, domain adaptation, and mobile and web deployment, among other key concepts. Computer Vision and Neural Networks Computer Vision and Neural Networks Technical requirements Computer vision in the wild A brief history of computer vision Getting started with neural networks TensorFlow Basics and Training a Model TensorFlow Basics and Training a Model Technical requirements Getting started with TensorFlow 2 and Keras TensorFlow 2 and Keras in detail The TensorFlow ecosystem Modern Neural Networks Modern Neural Networks Technical requirements Discovering convolutional neural networks Refining the training process Influential Classification Tools Influential Classification Tools Technical requirements Understanding advanced CNN architectures Leveraging transfer learning Object Detection Models Object Detection Models Technical requirements Introducing object detection A fast object detection algorithm ? YOLO Faster R-CNN ? a powerful object detection model Enhancing and Segmenting Images Enhancing and Segmenting Images Technical requirements Transforming images with encoders-decoders Understanding semantic segmentation Training on Complex and Scarce Datasets Training on Complex and Scarce Datasets Technical requirements Efficient data serving How to deal with data scarcity Video and Recurrent Neural Networks Video and Recurrent Neural Networks Technical requirements Introducing RNNs Classifying videos Optimizing Models and Deploying on Mobile Devices Optimizing Models and Deploying on Mobile Devices Technical requirements Optimizing computational and disk footprints On-device machine learning Example app ? recognizing facial expressions

Canelo Publishing Masterclasses. Book your space to peek behind the scenes of a trade publisher. Learn about the different roles and departments, how books are made and published, and how publishers interact with readers and booksellers. Hear what it takes to ensure your book gets published and becomes a hit. Learn about the key things a publisher looks for when they consider submissions or publish books. Canelo will cover questions commonly asked, as well as answering your questions live. The winners of the I Am In Print Novel Award 2023 will also be announced!

Teaching4you is a tuition company that works to encourage and build confidence in students nationwide.

Course Overview The DeepSeek Masterclass: A Complete DeepSeek Zero to Hero! provides a comprehensive exploration of the DeepSeek AI ecosystem, designed to guide learners from fundamental understanding to advanced applications. This course equips individuals with the knowledge to navigate the capabilities of DeepSeek across various domains such as software development, business analysis, and education. Whether you're looking to stay current in a rapidly evolving technological landscape or explore how AI transforms diverse sectors, this course offers a structured pathway. By the end of the programme, learners will be able to understand DeepSeek’s architecture, apply its tools across multiple contexts, and critically evaluate its potential within both technical and professional settings. Course Description This course delves into the foundational principles and progressive applications of DeepSeek, starting from basic concepts in artificial intelligence through to its tailored use in education, business, and software environments. Learners will explore how to configure and interpret DeepSeek outputs, understand the structure of AI decision-making, and evaluate its integration across various workflows. Key modules address the needs of developers, educators, students, and professionals seeking efficient AI-driven solutions. Throughout the course, learners will be introduced to scenario-based uses of DeepSeek, helping them build a contextual understanding of its functions. The curriculum is designed to support strategic thinking, digital literacy, and informed adoption of AI tools in both academic and professional environments. Course Modules Module 01: Getting Started Module 02: Foundations of Artificial Intelligence (AI) Module 03: Setting up DeepSeek AI for Beginners Module 04: DeepSeek for Software Developers Module 05: DeepSeek for Business Professionals Module 06: DeepSeek Smart Solutions for Students Module 07: The Power of DeepSeek Module 08: DeepSeek for Teaching Professionals (See full curriculum) Who is this course for? Individuals seeking to build an informed understanding of DeepSeek and AI. Professionals aiming to implement AI solutions within their field. Beginners with an interest in artificial intelligence and its uses. Educators, students, and technologists exploring innovative learning and working tools. Career Path AI Integration Analyst Business Intelligence Associate Education Technology Consultant Software Solution Strategist Digital Transformation Specialist AI Literacy Educator