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Leverage the power of Tensorflow to design deep learning systems for a variety of real-world scenarios About This Book Build efficient deep learning pipelines using the popular Tensorflow framework Train neural networks such as ConvNets, generative models, and LSTMs Includes projects related to Computer Vision, stock prediction, chatbots and more Who This Book Is For This 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 book. What You Will Learn Set up the TensorFlow environment for deep learning Construct your own ConvNets for effective image processing Use LSTMs for image caption generation Forecast stock prediction accurately with an LSTM architecture Learn what semantic matching is by detecting duplicate Quora questions Set up an AWS instance with TensorFlow to train GANs Train and set up a chatbot to understand and interpret human input Build an AI capable of playing a video game by itself ?and win it! In Detail TensorFlow is one of the most popular frameworks used for machine learning and, more 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 of 10 real-world projects. TensorFlow 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 games. By 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 confidently. Style and approach This book contains 10 unique, end-to-end projects covering all aspects of ...

Artificial intelligence.

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Machine learning is een vorm van kunstmatige intelligentie die zich bezighoudt met de ontwikkeling van algoritmes en technieken waarmee computers kunnen leren. Het gebruik van machine learning door bedrijven neemt wereldwijd explosief toe. Als je vertrouwd wil raken met de basisbegrippen van machine learning en het wil gebruiken om praktische taken uit te voeren, is Machine learning voor Dummies de broodnodige handleiding voor jou! Leer hoe dagelijkse activiteiten worden aangedreven door machine learning en krijg inzicht in de programmeertalen en tools die je nodig hebt. Of je nu gek bent van de wiskunde achter machine learning, of bezorgd over kunstmatige intelligentie, deze gids maakt het gemakkelijker om machine learning naadloos te begrijpen en te implementeren.

kunstmatige intelligentie --- artificiële intelligentie --- Kunstmatige intellegentie.

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Become an efficient data science practitioner by understanding Python's key concepts About This Book Quickly get familiar with data science using Python 3.5 Save time (and effort) with all the essential tools explained Create effective data science projects and avoid common pitfalls with the help of examples and hints dictated by experience Who This Book Is For If you are an aspiring data scientist and you have at least a working knowledge of data analysis and Python, this book will get you started in data science. Data analysts with experience of R or MATLAB will also find the book to be a comprehensive reference to enhance their data manipulation and machine learning skills. What You Will Learn Set up your data science toolbox using a Python scientific environment on Windows, Mac, and Linux Get data ready for your data science project Manipulate, fix, and explore data in order to solve data science problems Set up an experimental pipeline to test your data science hypotheses Choose the most effective and scalable learning algorithm for your data science tasks Optimize your machine learning models to get the best performance Explore and cluster graphs, taking advantage of interconnections and links in your data In Detail Fully expanded and upgraded, the second edition of Python Data Science Essentials takes you through all you need to know to suceed in data science using Python. Get modern insight into the core of Python data, including the latest versions of Jupyter notebooks, NumPy, pandas and scikit-learn. Look beyond the fundamentals with beautiful data visualizations with Seaborn and ggplot, web development with Bottle, and even the new frontiers of deep learning with Theano and TensorFlow. Dive into building your essential Python 3.5 data science toolbox, using a single-source approach that will allow to to work with Python 2.7 as well. Get to grips fast with data munging and preprocessing, and all the techniques you need to load, analyse, and process your data. Finally, get a complete overview of principal machine learning algorithms, graph analysis techniques, and all the visualization and deployment instruments that make it easier to present your results to an audience of both data science experts and business users. Style and approach The book is structured as a data science project. You will always benefit from clear code and simplified examples to help you understand the underlying mechanics and real-world datasets.

Database management. --- Python (Computer program language) --- Scripting languages (Computer science) --- Data base management --- Data services (Database management) --- Database management services --- DBMS (Computer science) --- Generalized data management systems --- Services, Database management --- Systems, Database management --- Systems, Generalized database management --- Electronic data processing

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Move up the Kaggle leaderboards and supercharge your data science and machine learning career by analyzing famous competitions and working through exercises. Purchase of the print or Kindle book includes a free eBook in PDF format. Key Features Challenge yourself to start thinking like a Kaggle Grandmaster Fill your portfolio with impressive case studies that will come in handy during interviews Packed with exercises and notes pages for you to enhance your skills and record key findings Book Description More than 80,000 Kaggle novices currently participate in Kaggle competitions. To help them navigate the often-overwhelming world of Kaggle, two Grandmasters put their heads together to write The Kaggle Book, which made plenty of waves in the community. Now, they've come back with an even more practical approach based on hands-on exercises that can help you start thinking like an experienced data scientist. In this book, you'll get up close and personal with four extensive case studies based on past Kaggle competitions. You'll learn how bright minds predicted which drivers would likely avoid filing insurance claims in Brazil and see how expert Kagglers used gradient-boosting methods to model Walmart unit sales time-series data. Get into computer vision by discovering different solutions for identifying the type of disease present on cassava leaves. And see how the Kaggle community created predictive algorithms to solve the natural language processing problem of subjective question-answering. You can use this workbook as a supplement alongside The Kaggle Book or on its own alongside resources available on the Kaggle website and other online communities. Whatever path you choose, this workbook will help make you a formidable Kaggle competitor. What you will learn Take your modeling to the next level by analyzing different case studies Boost your data science skillset with a curated selection of exercises Combine different methods to create better solutions Get a deeper insight into NLP and how it can help you solve unlikely challenges Sharpen your knowledge of time-series forecasting Challenge yourself to become a better data scientist Who this book is for If you're new to Kaggle and want to sink your teeth into practical exercises, start with The Kaggle Book, first. A basic understanding of the Kaggle platform, along with knowledge of machine learning and data science is a prerequisite. This book is suitable for anyone starting their Kaggle journey or veterans trying to get better at it. Data analysts/scientists who want to do better in Kaggle competitions and secure jobs with tech giants will find this book helpful.

Big data. --- Machine learning. --- Machine learning --- Big data

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Your secret weapon to understanding--and using!--one of the most powerful influences in the world today From your Facebook News Feed to your most recent insurance premiums--even making toast!--algorithms play a role in virtually everything that happens in modern society and in your personal life. And while they can seem complicated from a distance, the reality is that, with a little help, anyone can understand--and even use--these powerful problem-solving tools! In Algorithms For Dummies, you'll discover the basics of algorithms, including what they are, how they work, where you can find them (spoiler alert: everywhere!), who invented the most important ones in use today (a Greek philosopher is involved), and how to create them yourself. You'll also find: Dozens of graphs and charts that help you understand the inner workings of algorithms Links to an online repository called GitHub for constant access to updated code Step-by-step instructions on how to use Google Colaboratory, a zero-setup coding environment that runs right from your browser Whether you're a curious internet user wondering how Google seems to always know the right answer to your question or a beginning computer science student looking for a head start on your next class, Algorithms For Dummies is the can't-miss resource you've been waiting for.

Computer algorithms.