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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.

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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

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

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

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!

Inspiring, interactive and unique 4-hour CPD certified Communication and Co-production training.

If you are interested in improving your vocal abilities while singing Schubert, Schumann or Mendelssohn, our classical singing courses for adults is the perfect choice for you. Enhance both your technical and musical abilities thanks to the support of master singing instructors.

DOG BEHAVIOUR At Cheshire Dog Services, emphasis is placed on modification of the dog’s behaviour through non-coercive and non-aversive methods. While basic training is a component of any behaviour modification program, it is likely you require assistance in devising a course of action that will help you and your dog successfully overcome any problems that arise during your dogs’ lifetime. Plenty of the problem behaviours that dogs’ develop can be a result of an owner’s misunderstanding of their dogs’ behaviour and can make matters worse when responding in an inappropriate way. Harsh methods can deteriorate your relationship with your dog and build distrust on both sides so using aggression to cure aggression does not work. Many nuisance behaviours or aggression problems are often very normal dog behaviours, therefore using harsh or aversive tactics to remove these undesirable actions are unwarranted and only delay in “fixing” the problem in the first place. Helping you understand dog psychology can give you the foundation to a happier life together! Glyn has a diploma (merit pass) in Advanced Canine Behaviour from the esteemed British College of Canine Studies. Are you experiencing problems with your dog such as: Jumping up Object guarding or stealing Aggression to other dogs, children & strangers Over guarding of you or your house Fear / nervousness / anxiety Excessive barking Getting into the car Barking at people or cars Behaviour Modification takes into account: Ensuring the Health & physical wellbeing of your dog. (physical health – diet, rest, exercise, medical illness ) & emotional wellbeing. Setting the dog up for Success – Avoiding putting the dog in a situation where he/she is likely to fail. Desensitisation & Counter Conditioning – presenting less threatening versions of the triggering stimulus & pairing it with things the dog enjoys. Positive Reinforcement / Differential reinforcement of Alternative Behaviour – teaching other skills the dog can do instead of the unwanted behaviour. Please contact us with the behaviour problems you are encountering and we’ll review what’s involved to help you. We would normally like to carry out a visit to your home, discuss the issue on full, meet your dog and set out what we need to do to help you. At this stage, we’ll set out costs involved as I’m sure you can understand that some behavioural issues can take longer to resolve than others. We would normally like to carry out at least one follow up visit and will provide you with a written report too. Please contact us for prices (they vary depending on what’s required)