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  1. Designing for the User Experience in Learning Systems Designing for the User Experience in Learning Systems (Human-Computer Interaction Series) by Evangelos Kapros and Maria Koutsombogera English | PDF | 2018 | 256 Pages | ISBN: 3319947931 | 8,3 MB While the focus of the UX research and design discipline and the Learning Sciences and instructional design disciplines is often similar and almost always tangential, there seems to exist a gap, i.e. a lack of communication between the two fields. Not much has been said about how UX Design can work hand-in-hand with instructional design to advance learning. The goal of this book is to bridge this gap by presenting work that cuts through both fields. To illustrate this gap in more detail, we provide a combined view of UX Research and Design & Educational Technology. While the traditional view has perceived the Learning Experience Design as a field of Instructional Design, we will highlight its connection with UX, an aspect that has become increasingly relevant. Our focus on user experience research and design has a unique emphasis on the human learning experience: we strongly believe that in learning technology the technological part is only mediating the learning experience, and we do not focus on technological advancements per se, as we believe they are not the solution, in themselves, to the problems that education is facing. This book aims to lay out the challenges and opportunities in this field and highlight them through research presented in the various chapters. Thus, it presents a unique opportunity to represent areas of learning technology that go very far beyond the MOOC and the classroom technology. The book provides an outstanding overview and insights in the area and it aims to serve as a significant and valuable source for learning researchers and practitioners. DOWNLOAD (Buy premium account for maximum speed and resuming ability) http://nitroflare.com/view/C8C5588E0E1413B/a6r2j.rar http://rapidgator.net/file/f8c2e4611bdfcd6cfdb55add5bb314ff/a6r2j.rar http://turbobit.net/coi87et5wie4/a6r2j.rar.html
  2. Deep Reinforcement Learning for Wireless Networks Deep Reinforcement Learning for Wireless Networks by F. Richard Yu English | 2019 | ISBN: 3030105458 | 71 Pages | PDF | 3 MB English | 2019 | ISBN: 3030105458 | 71 Pages | PDF | 3 MB This Springerbrief presents a deep reinforcement learning approach to wireless systems to improve system performance. Particularly, deep reinforcement learning approach is used in cache-enabled opportunistic interference alignment wireless networks and mobile social networks. Simulation results with different network parameters are presented to show the effectiveness of the proposed scheme. DOWNLOAD (Buy premium account for maximum speed and resuming ability) http://nitroflare.com/view/E43A7D9F1ACA88E/5ttjq.rar http://rapidgator.net/file/1401e60eeeb65597d2cba277d74e9349/5ttjq.rar http://turbobit.net/ai1k33orf8ut/5ttjq.rar.html
  3. Deep Learning with PyTorch Guide for Beginners and Intermediate Deep Learning with PyTorch: Guide for Beginners and Intermediate by Jerry N. P. English | January 28, 2019 | ASIN: B07N7KP6NJ | 160 pages | AZW3 | 0.30 MB This book is an exploration of deep learning in Python using PyTorch. The author guides you on how to create neural network models using PyTorch in Python. You will know the initial steps of getting started with PyTorch in Python. This involves installing PyTorch and writing your first code. PyTorch works using the concept of graphs. The author helps you know how build neural network graphs in PyTorch. Deep learning in Python with PyTorch simply involves the creation of neural network models. The author helps you understand how to create neural network models with TensorFlow. You are guided on how to train such models with data of various types. Examples of such data include images and text. The process of loading your own data into PyTorch for training neural network models has also been discussed. You will also know how to use the inbuilt data for training your neural network models. This book will help you to understand: - Why PyTorch for Deep Learning? - Getting Started with PyTorch - Building a Neural Network - Loading and Processing Data - Convolutional Neural Networks - Transfer Learning - Developing Distributed Applications - Word Embeddings - Moving a Model from PyTorch to Caffe2 - Custom C Extensions - Neural Transfer with PyTorch Tags: pytorch deep learning, python programming, python, python data science handbook, neural network python, tensorflow python, tensorflow for deep learning, python code programming. DOWNLOAD (Buy premium account for maximum speed and resuming ability) http://nitroflare.com/view/AEC78E90C908428/a71wm.rar http://rapidgator.net/file/a922c60c93268e3b31ebdc32a9915b5f/a71wm.rar http://turbobit.net/yrgi1wc6bu1x/a71wm.rar.html
  4. Deep Learning and Missing Data in Engineering Systems Deep Learning and Missing Data in Engineering Systems by Collins Achepsah Leke English | 2019 | ISBN: 3030011798 | 179 Pages | PDF | 6 MB English | 2019 | ISBN: 3030011798 | 179 Pages | PDF | 6 MB Deep Learning and Missing Data in Engineering Systems uses deep learning and swarm intelligence methods to cover missing data estimation in engineering systems. The missing data estimation processes proposed in the book can be applied in image recognition and reconstruction. To facilitate the imputation of missing data, several artificial intelligence approaches are presented, including: DOWNLOAD (Buy premium account for maximum speed and resuming ability) http://nitroflare.com/view/0878A7520A24B3D/skk19.rar http://rapidgator.net/file/f9be7162ebd37f88b301fffecd992d74/skk19.rar http://turbobit.net/6ps6bigw0ptl/skk19.rar.html
  5. Dataset Shift in Machine Learning Dataset Shift in Machine Learning by Masashi Sugiyama English | 9 Jan. 2009 | ISBN: 0262170051 | 248 Pages | PDF | 4.51 MB An overview of recent efforts in the machine learning community to deal with dataset and covariate shift, which occurs when test and training inputs and outputs have different distributions. Dataset shift is a common problem in predictive modeling that occurs when the joint distribution of inputs and outputs differs between training and test stages. Covariate shift, a particular case of dataset shift, occurs when only the input distribution changes. DOWNLOAD (Buy premium account for maximum speed and resuming ability) http://nitroflare.com/view/58C9D0101FAED98/h5gf0.rar http://rapidgator.net/file/a7ca4e3666c8f096c06d98e200cb487b/h5gf0.rar http://turbobit.net/2lrlhgq5uuf3/h5gf0.rar.html
  6. Cultural Urban Heritage Development, Learning and Landscape Strategies Mladen Obad Šćitaroci, Bojana Bojanić Obad Šćitaroci, "Cultural Urban Heritage: Development, Learning and Landscape Strategies" 2019 | ISBN-10: 303010611X | 475 pages | PDF, EPUB | 115 MB This book presents strategies and models for cultural heritage enhancement from a multidisciplinary perspective. It discusses identifying historical, current and possible future models for the revival and enhancement of cultural heritage, taking into consideration three factors - respect for the inherited, contemporary and sustainable future development. The goal of the research is to contribute to the enhancement of past cultural heritage renovation and enhancement methods, improve the methods of spatial protection of heritage and contribute to the development of the local community through the use of cultural, and in particular, architectural heritage. Cultural heritage is perceived primarily through conservation, but that comes with limitations. If heritage is perceived and experienced solely through conservation, it becomes a static object. It needs to be made an active subject, which implies life in heritage as well as new purposes and new life for abandoned heritage. Heritage can be considered as a resource that generates revenue for itself and for the sustainability of the local community. To achieve this, it should be developed in accordance with contemporary needs and technological achievements, but on scientifically based and professional criteria and on sustainable models. The research presented in this book is based on the approach of Heritage Urbanism in a combination of experiments (case studies) and theory. DOWNLOAD (Buy premium account for maximum speed and resuming ability) http://nitroflare.com/view/1D44DC822D6B834/chog3.rar http://rapidgator.net/file/33e08e4163e1c083d108aa40147b27cf/chog3.rar http://turbobit.net/pcht4ab38qji/chog3.rar.html
  7. Build Deeper The Path to Deep Learning Build Deeper: The Path to Deep Learning by Thimira Amaratunga English | January 9, 2019 | ISBN: 1793223017 | 271 pages | MOBI | 8.48 MB New 2019 Edition! Build Deeper is a complete and practical guide that can help you take the first few steps in deep learning. It will guide you step-by-step, from understanding the basic concepts, to building your first practical application. It covers:What Deep Learning is, and where it fits with Artificial Intelligence and Machine Learning.How Deep Learning came to be, its predecessors, and the path it took to evolve into what it is today.The important milestones it has passed through the years, and the impact they had on the field.What tools are available for us to learn and build deep learning applications, and how to set them up: Python, TensorFlow, Theano, Keras, and more, on any OS of your choosing: Windows, Linux, or Mac OS.Building our first simple deep learning model.The internal workings of a deep learning model.Using more advanced topics such as Data Augmentation, Transfer Learning, Bottleneck Features, and Fine Tuning to build a practical deep learning application.Getting started with Computer Vision.All you need now is a little enthusiasm ... who knows where it will take you! Go a little deeper to discover ... DOWNLOAD (Buy premium account for maximum speed and resuming ability) http://nitroflare.com/view/AD9D4E081B63531/8uno7.rar http://rapidgator.net/file/bc1dabf5123e9caa81d91a523c68adc5/8uno7.rar http://turbobit.net/r1gpr343yvkd/8uno7.rar.html
  8. The Deep Learning Revolution [Audiobook] Terrence J. Sejnowski, Shawn Compton (Narrator), "The Deep Learning Revolution" ASIN: B07MM8F42R, ISBN: 1515946436 | 2019 | [email protected] kbps | ~08:05:00 | 227 MB How deep learning - from Google Translate to driverless cars to personal cognitive assistants - is changing our lives and transforming every sector of the economy. The deep-learning revolution has brought us driverless cars, the greatly improved Google Translate, fluent conversations with Siri and Alexa, and enormous profits from automated trading on the New York Stock Exchange. Deep-learning networks can play poker better than professional poker players and defeat a world champion at Go. In this book, Terry Sejnowski explains how deep learning went from being an arcane academic field to a disruptive technology in the information economy. Sejnowski played an important role in the founding of deep learning, as one of a small group of researchers in the 1980s who challenged the prevailing logic-and-symbol based version of AI. The new version of AI Sejnowski and others developed, which became deep learning, is fueled instead by data. Deep networks learn from data in the same way that babies experience the world, starting with fresh eyes and gradually acquiring the skills needed to navigate novel environments. It took nature many millions of years to evolve human intelligence; AI is on a trajectory measured in decades. Sejnowski prepares us for a deep learning future. DOWNLOAD (Buy premium account for maximum speed and resuming ability) http://nitroflare.com/view/607F0ED345AEB2F/dfogp.rar http://rapidgator.net/file/069d1d2bbf3e2e4036f537b42718a757/dfogp.rar http://turbobit.net/z0msnqk29w3b/dfogp.rar.html
  9. Writing as a Learning Tool Integrating Theory and Practice Writing as a Learning Tool: Integrating Theory and Practice By David R. Olson (auth.), Päivi Tynjälä, Lucia Mason, Kirsti Lonka (eds.) 2001 | 219 Pages | ISBN: 0792369149 | PDF | 8 MB In a brief summary, the debate concerning the nature of writing processes is about whether the essential characteristic of expertise in writing is a matter of mastering problem-solving strategies. In this respect, the role of social and interactive factors, such as writers' familiarity with the particular genre in which they are writing and their relationship with the discourse community in which they are participating, have been pointed out (e.g. Nystrand, 1989). According to the socio-interactive approach, which refers to Vygotsky's theory, the composition process is a dialogue between the writer and the reader made possible by socially shared knowledge. The meaning of a text is a social construct that is negotiated between the reader and the writer through the medium of the text. The importance of motivational aspects has also been highlighted by two main lines of research, studies of the relationship between writing and self-efficacy (e.g. Pajares & Johnson, 1994, 1996) and studies of the role of interest in the production of expository texts (e.g. Albin, Benton & Khramtsova, 1996; Benton, Corkill, Sharp, Downey, Khramtsova, 1995; Hidi & McLaren, 1990, 1991). Self-efficacy, in this context individuals' beliefs about their ability to produce certain types of texts, have been found to be predictive of writing skills, strategy use and writing performance. DOWNLOAD (Buy premium account for maximum speed and resuming ability) http://nitroflare.com/view/F7BEDAA63A0487D/lk6vh.rar http://rapidgator.net/file/c6921eefb1bf4b98b561b108d81ac1c1/lk6vh.rar http://turbobit.net/f5lsrlbrjs1y/lk6vh.rar.html
  10. Workplace learning in health and social care Workplace learning in health and social care: a student's guide by Carolyn Jackson English | 1 Feb. 2011 | ISBN: 0335237509 | 161 Pages | PDF | 1.82 MB This book introduces and explores the concept and realities of learning at and for work in a 'how to' style and is designed to help students make the most of their work based learning experience. DOWNLOAD (Buy premium account for maximum speed and resuming ability) http://nitroflare.com/view/C4DF35183B46BC0/555qr.rar http://rapidgator.net/file/e4454dcc7033be65b509f3eca92f4053/555qr.rar http://turbobit.net/j158ya3jwwzn/555qr.rar.html
  11. Packt Learning Dart Build Your First App with Flutter Packt Learning Dart Build Your First App with Flutter English | Size: 2.81 GB Category: Tutorial Build faster, smoother cross-platform apps for both Android and iOS with just one codebase using Flutter and Dart Flutter is quickly becoming a well-known framework for developing cross-platform apps for both Android and iOS devices. Now developers don't have to learn Java, Kotlin, Objective-C, or Swift to have their apps on Play Store or the App Store. While there are other frameworks for building cross-platform apps, Flutter excels by using a great new language called Dart. With Dart and Flutter, you will develop apps for both stores with just one codebase. It compiles apps into native code without webview. Apps made with Flutter are very fast because of their high rendering power. Hence, they feel super-smooth, thus providing a rich user experience. With this course, you will be able to build app layouts, create widget animations, pull and push data to servers, and compile and release code to both stores. R E L E A S E N O T E S, 1 - Unpack, 2 - Burn or Mount the ISO, 3 - Open the tutorial, 4 - Enjoy G R E E T S C O N T A C T U S DOWNLOAD (Buy premium account for maximum speed and resuming ability) http://nitroflare.com/view/1D7426EB1E40FB3/luf16.Packt.Learning.Dart.Build.Your.First.App.with.Flutter.part1.rar http://nitroflare.com/view/1B760E9EAB024B1/luf16.Packt.Learning.Dart.Build.Your.First.App.with.Flutter.part2.rar http://nitroflare.com/view/694C56F97EF23D3/luf16.Packt.Learning.Dart.Build.Your.First.App.with.Flutter.part3.rar http://nitroflare.com/view/CBC469BAE7D661C/luf16.Packt.Learning.Dart.Build.Your.First.App.with.Flutter.part4.rar http://nitroflare.com/view/E945617F4CC0A1E/luf16.Packt.Learning.Dart.Build.Your.First.App.with.Flutter.part5.rar http://rapidgator.net/file/6968a17e606e63a3d07b8a812373f45e/luf16.Packt.Learning.Dart.Build.Your.First.App.with.Flutter.part1.rar http://rapidgator.net/file/56a0deac60b2d670f1621f31fa9e7f74/luf16.Packt.Learning.Dart.Build.Your.First.App.with.Flutter.part2.rar http://rapidgator.net/file/6fa1579b2c1ee87f72297c06ea9dd855/luf16.Packt.Learning.Dart.Build.Your.First.App.with.Flutter.part3.rar http://rapidgator.net/file/87a6038adc2596a0a47eb2de14c7bb54/luf16.Packt.Learning.Dart.Build.Your.First.App.with.Flutter.part4.rar http://rapidgator.net/file/5bee7c7ae9bb2dcf06fe38b66d065bd4/luf16.Packt.Learning.Dart.Build.Your.First.App.with.Flutter.part5.rar http://turbobit.net/noerep6la75m/luf16.Packt.Learning.Dart.Build.Your.First.App.with.Flutter.part1.rar.html http://turbobit.net/hq9xxlll9yqt/luf16.Packt.Learning.Dart.Build.Your.First.App.with.Flutter.part2.rar.html http://turbobit.net/7cm3n5qo887p/luf16.Packt.Learning.Dart.Build.Your.First.App.with.Flutter.part3.rar.html http://turbobit.net/hcirga2uyiy9/luf16.Packt.Learning.Dart.Build.Your.First.App.with.Flutter.part4.rar.html http://turbobit.net/x5ldxexjfbuc/luf16.Packt.Learning.Dart.Build.Your.First.App.with.Flutter.part5.rar.html
  12. Machine Learning with Python - A Beginner's Guide Machine Learning with Python - A Beginner's Guide .MP4 | Video: 1280x720, 30 fps(r) | Audio: AAC, 48000 Hz, 2ch | 1.50 GB Duration: 4 hours | Genre: eLearning | Language: English Learn the basics of machine learning and implement linear regression using gradient descent and normal equation. Basics of Machine learning Linear Regression Basic Python Learn the basics of machine learning and implement linear regression using gradient descent and normal equation. What you'll learn Basics of Machine learning Types of machine learning algorithms Linear Regression Requirements Basic Python Basic Mathematical operations on matrix Spyder IDE, Python, SKlearn installed in the computer. Description You will the basics of machine learning like what is machine learning, machine learning algorithms, and implement linear regression using normal equation and gradient descent. You will learn the difference between regression and classification. When to use regression and when to use classification. You will implement the linear regression using python. You need to have some knowledge of writing python code using Spyder IDE. Who this course is for: Beginner to Machine Learning DOWNLOAD (Buy premium account for maximum speed and resuming ability) http://nitroflare.com/view/6F9CBC9A3CAA29B/eet9x.Machine.Learning.with.Python..A.Beginners.Guide.part1.rar http://nitroflare.com/view/BBEC7AD932F114D/eet9x.Machine.Learning.with.Python..A.Beginners.Guide.part2.rar http://nitroflare.com/view/9CC718E8EC181E8/eet9x.Machine.Learning.with.Python..A.Beginners.Guide.part3.rar http://nitroflare.com/view/C4FAEBF0EC58A9D/eet9x.Machine.Learning.with.Python..A.Beginners.Guide.part4.rar http://nitroflare.com/view/CC702CD8DC2679A/eet9x.Machine.Learning.with.Python..A.Beginners.Guide.part5.rar http://nitroflare.com/view/CC8E901D8959509/eet9x.Machine.Learning.with.Python..A.Beginners.Guide.part6.rar http://nitroflare.com/view/9B134D61136239D/eet9x.Machine.Learning.with.Python..A.Beginners.Guide.part7.rar http://nitroflare.com/view/78F5C00BDCBF727/eet9x.Machine.Learning.with.Python..A.Beginners.Guide.part8.rar http://rapidgator.net/file/ff5d1baa7e06fb7cb13e92d059671544/eet9x.Machine.Learning.with.Python..A.Beginners.Guide.part1.rar http://rapidgator.net/file/7becdf4861382b9bca65655564d8f476/eet9x.Machine.Learning.with.Python..A.Beginners.Guide.part2.rar http://rapidgator.net/file/b9981ebf73c8d76aa1347aa1b29a55d3/eet9x.Machine.Learning.with.Python..A.Beginners.Guide.part3.rar http://rapidgator.net/file/e1b09cd6f6f5e0de2986c5608d5c6165/eet9x.Machine.Learning.with.Python..A.Beginners.Guide.part4.rar http://rapidgator.net/file/fb31b848726f79f296952740b6eac739/eet9x.Machine.Learning.with.Python..A.Beginners.Guide.part5.rar http://rapidgator.net/file/3ba3161dfe66c4e2c2d48b68873f405e/eet9x.Machine.Learning.with.Python..A.Beginners.Guide.part6.rar http://rapidgator.net/file/7d1b5d4c9e30b87efcf11cd02007f7a5/eet9x.Machine.Learning.with.Python..A.Beginners.Guide.part7.rar http://rapidgator.net/file/edb500e4f3ff238ce7f2030bf8ad8440/eet9x.Machine.Learning.with.Python..A.Beginners.Guide.part8.rar http://turbobit.net/9mh70qtyvdv9/eet9x.Machine.Learning.with.Python..A.Beginners.Guide.part1.rar.html http://turbobit.net/eteaotszjt9w/eet9x.Machine.Learning.with.Python..A.Beginners.Guide.part2.rar.html http://turbobit.net/4i4cbpvl8q07/eet9x.Machine.Learning.with.Python..A.Beginners.Guide.part3.rar.html http://turbobit.net/9332oiqxdr9b/eet9x.Machine.Learning.with.Python..A.Beginners.Guide.part4.rar.html http://turbobit.net/hsxfi1pxp00w/eet9x.Machine.Learning.with.Python..A.Beginners.Guide.part5.rar.html http://turbobit.net/le7mkfybcorq/eet9x.Machine.Learning.with.Python..A.Beginners.Guide.part6.rar.html http://turbobit.net/9t9oplg9zvpp/eet9x.Machine.Learning.with.Python..A.Beginners.Guide.part7.rar.html http://turbobit.net/sbxtk2unr9q1/eet9x.Machine.Learning.with.Python..A.Beginners.Guide.part8.rar.html
  13. Machine Learning Intro for Python Developers Machine Learning Intro for Python Developers .MP4 | Video: 1280x720, 30 fps(r) | Audio: AAC, 48000 Hz, 2ch | 366 MB Duration: 44 mins | Genre: eLearning | Language: English Get started with Machine Learning Algorithms What you'll learn How to apply ML algorithms to your own problems Classify data automatically Group data points automatically Experience with Python Programming What you'll learn Machine Learning Basics with Python How to apply ML algorithms to your own problems How some algorithms work internally (kmeans, decision tree) Classify data automatically Predict prices with algorithms Group data points automatically Requirements Experience with Python Programming Description Learn how to use Machine Learning with Python Scikit! This is a getting started course for Machine Learning. If you are new to Machine Learning, this course is for you! We'll teach you how to get started with Machine Learning including these topics: What is Machine Learning Automatically classify new data Predict prices with regression Group your data automatically with clustering Who this course is for: Python developers curious about Machine Learning DOWNLOAD (Buy premium account for maximum speed and resuming ability) http://nitroflare.com/view/2C283AB25E4DB96/q1pio.Machine.Learning.Intro.for.Python.Developers.part1.rar http://nitroflare.com/view/E34D9DFDC18400B/q1pio.Machine.Learning.Intro.for.Python.Developers.part2.rar http://rapidgator.net/file/e1e5eac0a0db6987969c971b00bc9444/q1pio.Machine.Learning.Intro.for.Python.Developers.part1.rar http://rapidgator.net/file/58d4f2c9a55d23f48addddad9641a745/q1pio.Machine.Learning.Intro.for.Python.Developers.part2.rar http://turbobit.net/eolh3llmwk6g/q1pio.Machine.Learning.Intro.for.Python.Developers.part1.rar.html http://turbobit.net/prwlrapzmom7/q1pio.Machine.Learning.Intro.for.Python.Developers.part2.rar.html
  14. Hands-On Python Deep Learning Hands-On Python Deep Learning MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 2.5 Hours | 647 MB Genre: eLearning | Language: English Deep learning is the next step to a more advanced implementation of machine learning. The course resolves the confusion between machine learning and deep learning by focusing only on deep learning concepts. Deep learning techniques are used in real-world scenarios such as image scanning, face detection, and many more. It is important to know deep learning algorithms as they are currently trending in sectors such as healthcare, finance, and many more. This hands-on course will help you tackle various issues that you come across while building your Deep Learning applications in the healthcare domain. Right from building your neural nets to reinforcement learning and working with different Deep Learning applications such as computer Vision and voice and image recognition, this course will be your guide in tackling different situations and issues and provide the end to end application of deep learning concepts in the healthcare domain. By the end of the course, you will be able to build neural networks and Deep learning models for your own projects. The code bundle for this video course is available at - http://github.com/PacktPublishing/Hands-On-Python-Deep-Learning DOWNLOAD (Buy premium account for maximum speed and resuming ability) http://nitroflare.com/view/82884BEF8922326/6eaj2.HandsOn.Python.Deep.Learning.rar http://rapidgator.net/file/f2f0c46984178a3f165719be94f70684/6eaj2.HandsOn.Python.Deep.Learning.rar http://turbobit.net/83iihhkrgbzc/6eaj2.HandsOn.Python.Deep.Learning.rar.html
  15. Hands-On Deep Learning for Computer Vision Hands-On Deep Learning for Computer Vision .MP4, AVC, 380 kbps, 1920x1080 | English, AAC, 160 kbps, 2 Ch | 2h 4m | 415 MB Instructor: Jakub Konczyk Go from auto encoding to cutting-edge imaging techniques such as YOLO and Neural Doodle with Keras, TensorFlow, OpenCV, and Python Machine Learning, and Deep learning techniques in particular, are changing the way computers see and interact with the World. From augmented and mixed-reality applications to just gathering data, these new techniques are revolutionizing a lot of industries This course is designed to give you a hands-on learning experience by going from the basic concepts to the most current in-depth Deep Learning methods for Computer Vision in use today. In this course, you will be introduced to the concept of deep learning and a variety of popular and effective techniques for image classification, detection, segmentation and generation. You will learn to build your own neural network and classify images accordingly. You will be taken through popular techniques such as Deep Dream (to generate psychedelic, surreal images), Style Transfer (to transfer styles between images), and Neural Doodle, to generate an image that matches a doodled sketch. By the end of this course, you will be able to use computer vision and deep learning to encode, classify, detect, and style images for the real world. What You Will Learn Hands-on experience using deep learning with Python, Keras, TF, and OpenCV Encode, decode, and denoise images with autoencoders Understand the structure and function of neural networks and CNNs/pooling Classify images with OpenCV using smart Deep Learning methods Detect objects in images with You Only Look Once (YOLOv3) Work with advanced imaging tools such as Deep Dream, Style Transfer, and Neural Doodle More Info http://www.packtpub.com/application-development/hands-deep-learning-computer-vision-video DOWNLOAD (Buy premium account for maximum speed and resuming ability) http://nitroflare.com/view/9056F58C761DD2B/4w7qm.HandsOn.Deep.Learning.for.Computer.Vision.rar http://rapidgator.net/file/80d40a14e0811732229ab6e1fe98a358/4w7qm.HandsOn.Deep.Learning.for.Computer.Vision.rar http://turbobit.net/nfc88lwdw6qp/4w7qm.HandsOn.Deep.Learning.for.Computer.Vision.rar.html
  16. Deep Learning with Apache Spark Deep Learning with Apache Spark .MP4, AVC, 380 kbps, 1920x1080 | English, AAC, 160 kbps, 2 Ch | 1h 40m | 344 MB Instructor: Tomasz Lelek Develop fast, efficient distributed deep learning models with Apache Spark Deep Learning is a subset of Machine Learning whereby datasets with several layers of complexity can be processed efficiently. This tutorial brings together two of the most popular buzzwords of today-big data and Artificial Intelligence-by showing you how you can implement Deep Learning solutions using the power of Apache Spark. The tutorial begins by explaining the fundamentals of Apache Spark and deep learning. You will set up a Spark environment to perform deep learning and learn about the different types of neural net and the principles of distributed modeling (model- and data-parallelism, and more). You will then implement deep learning models (such as CNN, RNN, LTSMs) on Spark, acquire hands-on experience of what it takes, and get a general feeling for the complexity we are dealing with. You will also see how you can use libraries such as Deeplearning4j to perform deep learning on a distributed CPU and GPU setup. By the end of this course, you'll have gained experience by implementing models for applications such as object recognition, text analysis, and voice recognition. You will even have designed human expert games. What You Will Learn Get to know basic Apache Spark and deep learning concepts Explore deep learning neural networks such as RBM, RNN, and DBN using some of the most popular industrial deep learning frameworks Learn how to leverage big data to solve real-world problems using deep learning Understand how to formulate real-world prediction problems as machine learning tasks, how to choose the right neural net architecture for a problem, and how to train neural nets using DL4J Get up-and-running and gain an insight into the deep learning library DL4J and its practical uses Design successful solutions with Extreme Learning machines Train and test neural networks to fit your data model http://www.packtpub.com/big-data-and-business-intelligence/deep-learning-apache-spark-video DOWNLOAD (Buy premium account for maximum speed and resuming ability) http://nitroflare.com/view/B9E4AE1B605C27C/sc37t.Deep.Learning.with.Apache.Spark.rar http://rapidgator.net/file/289822189130d6f43e3f82dbd98a4bfe/sc37t.Deep.Learning.with.Apache.Spark.rar http://turbobit.net/ljl7fixr5qi9/sc37t.Deep.Learning.with.Apache.Spark.rar.html
  17. Blockchain and Deep Learning Future of AI MP4 | Video: AVC 1280x720 | Audio: AAC 48KHz 2ch | Duration: 1.5 Hours | Lec: 7 | 558 MB Genre: eLearning | Language: English Jobs Training for the Future This course provides a conceptual overview and technical summary of the two top job growth areas worldwide: blockchain technology and deep learning. The course discusses how these technologies may be used together in deep learning chains. Some of the important application areas are autonomous driving, health care, energy, and finance. DOWNLOAD (Buy premium account for maximum speed and resuming ability) http://nitroflare.com/view/DBF97FCEDAD0046/x2erw.Blockchain.and.Deep.Learning.Future.of.AI.rar http://rapidgator.net/file/5ea013b909cccf3df0ee32d5e61eeebe/x2erw.Blockchain.and.Deep.Learning.Future.of.AI.rar http://turbobit.net/vpbqhqqpkimx/x2erw.Blockchain.and.Deep.Learning.Future.of.AI.rar.html
  18. The Influence of Attention, Learning, and Motivation on Visual Search The Influence of Attention, Learning, and Motivation on Visual Search By Michael D. Dodd, John H. Flowers (auth.), Michael D. Dodd, John H. Flowers (eds.) 2012 | 218 Pages | ISBN: 1461447933 | PDF | 3 MB The Influence of Attention, Learning, and Motivation on Visual Search will bring together distinguished authors who are conducting cutting edge research on the many factors that influence search behavior. These factors will include low-level feature detection; statistical learning; scene perception; neural mechanisms of attention; and applied research in real world settings. DOWNLOAD (Buy premium account for maximum speed and resuming ability) http://nitroflare.com/view/176E294DF5CD882/zeiue.rar http://rapidgator.net/file/43cf891708fe494c53bdb3bccf51370c/zeiue.rar http://turbobit.net/wr60wb6hc4xp/zeiue.rar.html
  19. Technology Enhanced Learning and Cognition Technology Enhanced Learning and Cognition by Itiel E. Dror English | 26 Jan. 2011 | ISBN: 9027222576 | 276 Pages | PDF | 3.11 MB The use of technology in learning has increased dramatically. Training and education is now utilizing and almost integrated with the World Wide Web, podcasts, mobile and distant learning, interactive videos, serious games, and a whole range of e-learning. However, has such technology enhanced learning been effective? DOWNLOAD (Buy premium account for maximum speed and resuming ability) http://nitroflare.com/view/CA26BA8973445C1/sjg9d.rar http://rapidgator.net/file/78890f0bfe6502d880a7ac592bac462c/sjg9d.rar http://turbobit.net/f6ojl21szqlz/sjg9d.rar.html
  20. Tech & Learning Do The Math (Tech and Learning Book 1) Tech & Learning: Do The Math (Tech and Learning Book 1) by David Clarke, Omprakash Sahu English | December 27, 2018 | ASIN: B07M9XLZV1 | 223 pages | AZW3 | 10 MB The New Year means setting up the calendar with a full slate of winter edtech events for our editors to cover. First, there is the British Education and Training Technology exposition (BETT), the world's largest and only truly global event dedicated to emerging education technologies, which was held January 25-28 in London. At the same time, a revamped Future of Education Technology Conference (FETC) show went down in Orlando. Not to be outdone, Educon2.9 convened once again at the Science Leadership Academy in Philadelphia. And then there is the Texas Computer Education Association Conference (TCEA), February 6-10, which is where we celebrate the winners of this year's Awards of Excellence, dozens of the latest and greatest products tested and approved by our esteemed team of advisors. Of course, none of that coverage can be in the issue you hold in your hands (if you know how, let me know!), which is why we created the Tech&Learning Live channel (www. techlearning.com/tltechlive) to follow every product announcement and speaker interview, and show floor selfies at each of these events. DOWNLOAD (Buy premium account for maximum speed and resuming ability) http://nitroflare.com/view/D5EDAC57F44A24E/vwy93.rar http://rapidgator.net/file/05e538f96457b1e0d6581ce4c370311a/vwy93.rar http://turbobit.net/6uef3hkc1jpv/vwy93.rar.html
  21. Teaching for Effective Learning in Higher Education Teaching for Effective Learning in Higher Education By Nira Hativa (auth.) 2000 | 380 Pages | ISBN: 0792368436 | PDF | 16 MB Research on teaching in higher education shows that students who are well taught learn more than students who are poorly taught, and there are some teaching behaviors and strategies that are consistently associated with good teaching. This book identifies these strategies and presents them within a theoretical framework that explains how they promote students' active and meaningful learning. By presenting teaching as a logical structure of interconnected behaviors whose contribution to student learning is based on theory and research, the book promotes teachers' pedagogical knowledge and their perception of teaching as scholarly, intellectual work. The book provides extensive practical advice that is based on the vast experience of the author as an instructional consultant and on research on accomplished teachers, taken from the domains of education, psychology, and speech communication. The practical ideas are separated from the theoretical part in a way that makes them easily identifiable. The book also puts forth the voice of the students through authentic that they wrote in thousands of instructor-evaluation forms that the author collected over many years. DOWNLOAD (Buy premium account for maximum speed and resuming ability) http://nitroflare.com/view/D695AAA654B1B5F/9lvsv.rar http://rapidgator.net/file/d32c0b8ba8faa7734da03c9993278c8f/9lvsv.rar http://turbobit.net/xrlp9mje8yd2/9lvsv.rar.html
  22. Teaching and Learning Methods in Medicine Shabih Zaidi and Mona Nasir, "Teaching and Learning Methods in Medicine" 2015 | ISBN-10: 3319068490 | 334 pages | EPUB | 4 MB This book considers the evolution of medical education over the centuries, presents various theories and principles of learning (pedagogical and andragogical) and discusses different forms of medical curriculum and the strategies employed to develop them, citing examples from medical schools in developed and developing nations. Instructional methodologies and tools for assessment and evaluation are discussed at length and additional elements of modern medical teaching, such as writing skills, communication skills, evidence-based medicine, medical ethics, skill labs and webinars, are fully considered. In discussing these topics, the authors draw upon the personal experience that they have gained in learning, teaching and disseminating knowledge in many parts of the world over the past four decades. Medical Education in Modern Times will be of interest for medical students, doctors, teachers, nurses, paramedics and health and education planners. DOWNLOAD (Buy premium account for maximum speed and resuming ability) http://nitroflare.com/view/83D536357334545/kg2gn.rar http://rapidgator.net/file/c2b9ce341b29d6daa28458415112d429/kg2gn.rar http://turbobit.net/2sggrf81t3e0/kg2gn.rar.html
  23. Teacher Professional Learning in an Age of Compliance Mind the Gap Teacher Professional Learning in an Age of Compliance: Mind the Gap By Nicole Mockler, Susan Groundwater-Smith (auth.) 2009 | 158 Pages | ISBN: 1402094167 | PDF | 1 MB Teacher Professional Learning in an Age of Compliance: Mind the Gap examines ways in which practice-based inquiry in educational settings, in a number of different countries and contexts, can transcend current ways of working and thinking such that authentic professional learning is the result.The authors contend that education policy, under pressure from a number of quarters, is retreating into a standardized, audited, and backward-looking arena, with the advances of more progressive educational philosophy being rolled back. In an age where practitioner inquiry and action research have often been 'hijacked' for the purposes of broad-based policy implementation, this book offers a rationale for reclaiming the critical edge so fundamental to inquiry-based professional learning. It examines the potential of inquiry-based forms of teacher professional learning to contribute to the growth of professional knowledge for and about teachers' work.The authors intend that the book will assist in building new forms of professional knowledge that go beyond the current compliance model - engineered from less enduring materials - to inform a new model with its foundations in a strong ethical and moral framework. They also believe that this new model, if implemented, will help to reverse today's conservative educational trends and make teacher professional development a force for genuine progress once again.They have consciously moved away from the celebratory tone of much of the academic reporting of teacher professional learning, adopting instead a genuinely critical edge. In covering a wide range of policies and practices from across the international spectrum, they have allowed themselves the freedom to engage in serious epistemological arguments about the nature of professional knowledge, as well as how it is constructed and employed. DOWNLOAD (Buy premium account for maximum speed and resuming ability) http://nitroflare.com/view/E6C6B170B0F27F7/5lv7n.rar http://rapidgator.net/file/04d71628f07c93c49a5f819889e3630c/5lv7n.rar http://turbobit.net/4qehp9h9uj7y/5lv7n.rar.html
  24. Teacher Learning and Development The Mirror Maze Peter Aubusson, "Teacher Learning and Development: The Mirror Maze" 2006 | pages: 279 | ISBN: 1402046235 | PDF | 3,0 mb This book synthesises current practice and research developments from internationally recognised scholars and practitioners. In doing so, it provides theoretical and practical knowledge which informs teacher education, development and professional learning. DOWNLOAD (Buy premium account for maximum speed and resuming ability) http://nitroflare.com/view/4E37F9B3440B0D1/mt6g1.rar http://rapidgator.net/file/2e936041629401372cd24282d2d00166/mt6g1.rar http://turbobit.net/ujpal2tryqfj/mt6g1.rar.html
  25. Student Motivation The Culture and Context of Learning Student Motivation: The Culture and Context of Learning By Farideh Salili, Chi-yue Chiu, Ying-yi Hong (auth.), Farideh Salili, Chi Yue Chiu, Ying Yi Hong (eds.) 2001 | 364 Pages | ISBN: 1461354722 | PDF | 10 MB Ever since the advent of the intelligence test we have thought of exceptional achievement in terms of cognitive attributes. We have words and phrases like "genius," "above average intelligence," "average" and "mentally deficient" to describe different levels of cognitive ability. In the United States widespread use of intelligence tests followed the success of the in World War I, and for the next half-century Army Alpha and Beta Tests intelligence tests were the major measures used to predict school and vocational achievement. Learning was primarily studied in laboratories, and the behaviorist theories that were dominant largely dealt with changes in overt behavior. As a result there was relatively little influence of learning research on concepts involving cognition and intelligence. The transition from behaviorism to cognitive psychology that began in the 1940's and 50's came into full flower in the 1970's and 80's, and great progress was made in understanding learning, memory, and thinking. In the decades following World War I there had been many debates about the possible influence of environmental conditions on intelligence, but the cognitive abilities measured by intelligence tests were generally believed to be determined by heredity. The intelligence tests of cognitive abilities correlated substantially with academic performance; so their use in determining which students needed special help in school or which students were capable of university work was widely accepted. As cognitive psychology became dominant, it became apparent that although heredity was important, intelligence consisted of learnable abilities. DOWNLOAD (Buy premium account for maximum speed and resuming ability) http://nitroflare.com/view/118D186EAFA2CA5/z4c5p.rar http://rapidgator.net/file/dd166bbdb8ac3515939785ccf4c15eb4/z4c5p.rar http://turbobit.net/jkmc27a6my6y/z4c5p.rar.html