TensorFlow Deep Learning Projects: 10 real-world projects

TensorFlow Deep Learning Projects: 10 real-world projects Leverage the power of Tensorflow to design deep learning systems for a variety of real world scenariosKey FeaturesBuild efficient deep learning pipelines using the popular Tensorflow frameworkTrain neural networks such as ConvNets, generative models, and LSTMsIncludes projects related to Computer Vision, stock prediction, chatbots and Book DescriptionTensorFlow is one of the most popular frameworks used for machine learning and,recently, deep learning It provides a fast and efficient framework for training different kinds of deep learning models, with very high accuracy This book is your guide to master deep learning with TensorFlow with the help ofreal world projectsTensorFlow Deep Learning Projects starts with setting up the right TensorFlow environment for deep learning Learn to train different types of deep learning models using TensorFlow, including Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, and Generative Adversarial Networks While doing so, you will build end to end deep learning solutions to tackle different real world problems in image processing, recommendation systems, stock prediction, and building chatbots, to name a few You will also develop systems that perform machine translation, and use reinforcement learning techniques to play gamesBy the end of this book, you will have mastered all the concepts of deep learning and their implementation with TensorFlow, and will be able to build and train your own deep learning models with TensorFlow confidentlyWhat you will learnSet up the TensorFlow environment for deep learningConstruct your own ConvNets for effective image processingUse LSTMs for image caption generationForecast stock prediction accurately with an LSTM architectureLearn what semantic matching is by detecting duplicate Quora questionsSet up an AWS instance with TensorFlow to train GANsTrain and set up a chatbot to understand and interpret human inputBuild an AI capable of playing a video game by itself and win it Who This Book Is ForThis book is for data scientists, machine learning developers as well as deep learning practitioners, who want to build interesting deep learning projects that leverage the power of Tensorflow Some understanding of machine learning and deep learning, and familiarity with the TensorFlow framework is all you need to get started with this bookTable of ContentsRecognizing traffic signs using ConvnetsAnnotating Images with Object Detection APICaption generation for imagesBuilding GANs for Conditional Image CreationStock Price Prediction with LSTM Create Train Machine Translation SystemsTrain and set up a Chatbot, able to discuss like a humanDetecting Duplicate Quora QuestionsBuilding a TensorFlow Recommender Systems Video Games by Reinforcement learningLuca Massaron is a data scientist and marketing research director specialized in multivariate statistical analysis, machine learning, and customer insight, withyears experience of solving real world problems and generating value for stakeholders using reasoning, statistics, data mining, and algorithms Passionate about everything on data analysis and demonstrating the potentiality of data driven knowledge discovery to both experts and non experts, he believes that a lot can be achieved by understanding in simple terms and practicing the essentials of any disciplineAlberto Boschetti is a data scientist with strong expertise in signal processing and statistics He holds a PhD in telecommunication engineering and lives and works in London In his work, he faces daily challenges spanning natural language processing, machine learning, and distributed processing He is very passionate about his job and always tries to stay up to date on the latest development in data science technologies, attending meetups, conferences, and other eventsAlexey Grigorev is a skilled data scientist, machine learning engineer, and software developer withthanyears of professional experience He started his career as a Java developer working at a number of large and small companies, but after a while he switched to data science Right now, Alexey works as a data scientist at Simplaex, where, in his day to day job, he actively uses Java and Python for data cleaning, data analysis, and modeling His areas of expertise are machine learning and text miningAbhishek Thakur is a data scientist His focus is mainly on applied machine learning and deep learning, rather than theoretical aspects He completed his master s in computer science at the University of Bonn in earlySince then, he has worked in various industries, with a research focus on automatic machine learningRajalingappaa Shanmugamani is currently a deep learning lead at SAP, Singapore Previously, he worked and consulted at various startups, developing computer vision products He has a master s from IIT Madras, his thesis having been based on the applications of computer vision in manufacturing He has published articles in peer reviewed journals, and spoken at conferences, and applied for a few patents in machine learning In his spare time, he coaches programming and machine learning to school students and engineers

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