Key FeaturesImplement various deep learning algorithms in Keras and see how deep learning can be used in gamesSee how various deep learning models and practical use cases can be implemented using KerasA practical, hands on guide with real world examples to give you a strong foundation in KerasBook DescriptionThis book starts by introducing you to supervised learning algorithms such as simple linear regression, the classical multilayer perceptron and sophisticated deep convolutional networks You will also explore image processing with recognition of hand written digit images, classification of images into different categories, and advanced objects recognition with related image annotations An example of identification of salient points for face detection is also provided Next you will be introduced to Recurrent Networks, which are optimized for processing sequence data such as text, audio or time series Following that, you will learn about unsupervised learning algorithms such as Autoencoders and the very popular Generative Adversarial Networks GAN You will also explore non traditional uses of neural networks as Style Transfer.Finally, you will look at Reinforcement Learning and its application to AI game playing, another popular direction of research and application of neural networks.What you will learnOptimize step by step functions on a large neural network using the Backpropagation AlgorithmFine tune a neural network to improve the quality of resultsUse deep learning for image and audio processingUse Recursive Neural Tensor Networks RNTNs to outperform standard word embedding in special casesIdentify problems for which Recurrent Neural Network RNN solutions are suitableExplore the process required to implement AutoencodersEvolve a deep neural network using reinforcement learningWho This Book Is ForIf you re a data scientist with experience in machine learning or an AI programmer with some exposure to neural networks, you will find this book a useful entry point to deep learning with Keras A knowledge of Python is required for this book.Table of ContentsNeural Networks FoundationsKeras Installation and APIDeep Learning with ConvNetsGenerative Adversarial Networks and WaveNetWord EmbeddingsRecurrent Neural Networks RNNsAdditional Deep Learning ModelsAI Game PlayingAntonio GulliAntonio Gulli is a software executive and business leader with a passion for establishing and managing global technological talent, innovation, and execution He is an expert in search engines, online services, machine learning, information retrieval, analytics, and cloud computing So far, he has been lucky enough to gain professional experience in four different countries in Europe and managed people in six different countries in Europe and America Antonio served as CEO, GM, CTO, VP, director, and site lead in multiple fields spanning from publishing Elsevier to consumer internet Ask and Tiscali and high tech R D Microsoft and Google. 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technique that teaches computers to learn by example Learn about deep learning with MATLAB examples and tools Deep Learning Artificial Intelligence Solutions from NVIDIA Get the latest news on deep learning and artificial intelligence solutions and technologies, educational resources, and much Deep Learning in Python DataCamp Deep learning is the machine learning technique behind the most exciting capabilities in diverse areas like robotics, natural language processing, image recognition Deep Learning The Deep Learning textbook is a resource intended to help students and practitioners enter the field of machine learning in general and deep learning in particular Deep Learning MIT Technology Review Deep Learning With massive amounts of computational power, machines can now recognize objects and translate speech in real time Artificial intelligence is Deep Learning Udacity Interested in deep learning Take our free deep learning course and learn how to optimize basic neural networks and design intelligent systems from complex datasets Reading List Deep Learning List of reading lists and survey papers Books Deep Learning, Yoshua Bengio, Ian Goodfellow, Aaron Courville, MIT Press, In preparation Review Papers Representation Learning A Review and New Perspectives, Yoshua Bengio, Aaron Courville, Pascal Vincent, Arxiv, Deep Learning Coursera Deep Learning from deeplearning If you want to break into AI, this Specialization will help you do so Deep Learning is one of the most highly sought after skills in tech Neural Networks and Deep Learning Neural Networks and Deep Learning is a free online book The book will teach you about Neural networks, a beautiful biologically inspired programming paradigm which enables a computer to learn from observational data Why Deep Learning Is Suddenly Changing Your Life Fortune AI and deep machine learning are electrifying the computing industry and will soon transform corporate America The Difference Between AI, Machine Learning, and Deep AI, machine learning, and deep learning are terms that are often used interchangeably But they are not the same things Neural networks and deep learning In the last chapter we learned that deep neural networks are often much harder to train than shallow neural networks That s unfortunate, since we have good reason to believe that if we could train deep nets they d be much powerful than shallow nets MIT S Introduction to Deep Learning Course Description An introductory course on deep learning methods with applications to machine translation, image recognition, game playing, image generation and Deep Learning Goodfellow Deep Learning Deep learning Explore the frontier of AI Unsupervised Feature Learning and Deep Learning Tutorial Description This tutorial will teach you the main ideas of Unsupervised Feature Learning and Deep LearningBy working through it, you will also get to implement several feature learning deep learning algorithms, get to see them work for yourself, and learn how to 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