Yuxi (Hayden) Liu, Pablo Maldonado - książki
Tytuły autora: dostępne w księgarni Ebookpoint
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Uczenie maszynowe w języku R. Tworzenie i doskonalenie modeli - od przygotowania danych po dostrajanie, ewaluację i pracę z big data. Wydanie IV
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Before Machine Learning Volume 1 - Linear Algebra for A.I. The Fundamental Mathematics for Data Science and Artificial Intelligence
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Databricks ML in Action. Learn how Databricks supports the entire ML lifecycle end to end from data ingestion to the model deployment
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Uczenie maszynowe w Pythonie. Receptury. Od przygotowania danych do deep learningu. Wydanie II
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Machine Learning: Make Your Own Recommender System. Build Your Recommender System with Machine Learning Insights
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Machine Learning with Python. Unlocking AI Potential with Python and Machine Learning
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Bayesian Analysis with Python. A practical guide to probabilistic modeling - Third Edition
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MLOps with Red Hat OpenShift. A cloud-native approach to machine learning operations
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MATLAB for Machine Learning. Unlock the power of deep learning for swift and enhanced results - Second Edition
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Deep Learning for Finance
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Delta Lake: Up and Running
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TensorFlow Developer Certificate Guide. Efficiently tackle deep learning and ML problems to ace the Developer Certificate exam
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Przetwarzanie języka naturalnego w praktyce. Przewodnik po budowie rzeczywistych systemów NLP
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Podręcznik architekta rozwiązań. Poznaj reguły oraz strategie projektu architektury i rozpocznij niezwykłą karierę. Wydanie II
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Zaufanie do systemów sztucznej inteligencji
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Data Augmentation with Python. Enhance deep learning accuracy with data augmentation methods for image, text, audio, and tabular data
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Computer Vision on AWS. Build and deploy real-world CV solutions with Amazon Rekognition, Lookout for Vision, and SageMaker
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The Kaggle Workbook. Self-learning exercises and valuable insights for Kaggle data science competitions
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Uczenie maszynowe. Elementy matematyki w analizie danych
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Practicing Trustworthy Machine Learning
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Inżynieria danych na platformie AWS. Jak tworzyć kompletne potoki uczenia maszynowego
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Głębokie uczenie. Wprowadzenie
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Machine Learning on Kubernetes. A practical handbook for building and using a complete open source machine learning platform on Kubernetes
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Practical Simulations for Machine Learning
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Fundamentals of Deep Learning. 2nd Edition
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The Kaggle Book. Data analysis and machine learning for competitive data science
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Automated Machine Learning on AWS. Fast-track the development of your production-ready machine learning applications the AWS way
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Machine Learning with PyTorch and Scikit-Learn. Develop machine learning and deep learning models with Python
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Intelligent Workloads at the Edge. Deliver cyber-physical outcomes with data and machine learning using AWS IoT Greengrass
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Agile Machine Learning with DataRobot. Automate each step of the machine learning life cycle, from understanding problems to delivering value
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Machine Learning for Financial Risk Management with Python
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Azure Data Scientist Associate Certification Guide. A hands-on guide to machine learning in Azure and passing the Microsoft Certified DP-100 exam
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Uczenie głębokie i sztuczna inteligencja. Interaktywny przewodnik ilustrowany
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Machine Learning for Time-Series with Python. Forecast, predict, and detect anomalies with state-of-the-art machine learning methods
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Practical Weak Supervision
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Exploring GPT-3. An unofficial first look at the general-purpose language processing API from OpenAI
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Wzorce projektowe uczenia maszynowego. Rozwiązania typowych problemów dotyczących przygotowania danych, konstruowania modeli i MLOps
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Graph Machine Learning. Take graph data to the next level by applying machine learning techniques and algorithms
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Machine Learning with the Elastic Stack. Gain valuable insights from your data with Elastic Stack's machine learning features - Second Edition
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Automated Machine Learning with AutoKeras. Deep learning made accessible for everyone with just few lines of coding
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Algorytmy sztucznej inteligencji. Ilustrowany przewodnik
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Przetwarzanie języka naturalnego w akcji
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Python. Machine learning i deep learning. Biblioteki scikit-learn i TensorFlow 2. Wydanie III
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Machine Learning Using TensorFlow Cookbook. Create powerful machine learning algorithms with TensorFlow
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Odsłaniamy SQL Server 2019: Klastry Big Data i uczenie maszynowe
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Kubeflow Operations Guide
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Python dla DevOps. Naucz się bezlitośnie skutecznej automatyzacji
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Microsoft Power BI Quick Start Guide. Bring your data to life through data modeling, visualization, digital storytelling, and more - Second Edition
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Machine Learning Design Patterns
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Kubeflow for Machine Learning
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Machine Learning and Data Science Blueprints for Finance
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Deep Learning for Beginners. A beginner's guide to getting up and running with deep learning from scratch using Python
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The Natural Language Processing Workshop. Confidently design and build your own NLP projects with this easy-to-understand practical guide
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The Deep Learning Workshop. Learn the skills you need to develop your own next-generation deep learning models with TensorFlow and Keras
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The Deep Learning with Keras Workshop. Learn how to define and train neural network models with just a few lines of code
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The Unsupervised Learning Workshop. Get started with unsupervised learning algorithms and simplify your unorganized data to help make future predictions
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Hands-On Simulation Modeling with Python. Develop simulation models to get accurate results and enhance decision-making processes
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Hands-On Python Deep Learning for the Web. Integrating neural network architectures to build smart web apps with Flask, Django, and TensorFlow
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Mobile Deep Learning with TensorFlow Lite, ML Kit and Flutter. Build scalable real-world projects to implement end-to-end neural networks on Android and iOS
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Człowiek na rozdrożu. Sztuczna inteligencja 25 punktów widzenia
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Tłumaczenie wspomagane komputerowo
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The Supervised Learning Workshop. Predict outcomes from data by building your own powerful predictive models with machine learning in Python - Second Edition
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Hands-On Music Generation with Magenta. Explore the role of deep learning in music generation and assisted music composition
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Mastering Machine Learning Algorithms. Expert techniques for implementing popular machine learning algorithms, fine-tuning your models, and understanding how they work - Second Edition
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Advanced Deep Learning with R. Become an expert at designing, building, and improving advanced neural network models using R
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Python Machine Learning. Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2 - Third Edition
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Dancing with Qubits. How quantum computing works and how it can change the world
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Głębokie uczenie z TensorFlow. Od regresji liniowej po uczenie przez wzmacnianie
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Deep Learning
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Machine Learning for OpenCV 4. Intelligent algorithms for building image processing apps using OpenCV 4, Python, and scikit-learn - Second Edition
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Practical Machine Learning with R. Define, build, and evaluate machine learning models for real-world applications
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Algorytmy uczenia maszynowego. Zaawansowane techniki implementacji
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Hands-On Deep Learning for IoT. Train neural network models to develop intelligent IoT applications
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Uczenie maszynowe z językiem JavaScript. Rozwiązywanie złożonych problemów
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Deep Learning for Natural Language Processing. Solve your natural language processing problems with smart deep neural networks
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Deep Learning with R for Beginners. Design neural network models in R 3.5 using TensorFlow, Keras, and MXNet
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Applied Deep Learning with Keras. Solve complex real-life problems with the simplicity of Keras
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Hands-On Q-Learning with Python. Practical Q-learning with OpenAI Gym, Keras, and TensorFlow
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Machine Learning with R. Expert techniques for predictive modeling - Third Edition
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Python Machine Learning Cookbook. Over 100 recipes to progress from smart data analytics to deep learning using real-world datasets - Second Edition
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TensorFlow Reinforcement Learning Quick Start Guide. Get up and running with training and deploying intelligent, self-learning agents using Python
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Mastering OpenCV 4 with Python. A practical guide covering topics from image processing, augmented reality to deep learning with OpenCV 4 and Python 3.7
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Applied Unsupervised Learning with R. Uncover hidden relationships and patterns with k-means clustering, hierarchical clustering, and PCA
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Python. Uczenie maszynowe. Wydanie II
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Uczenie maszynowe w Pythonie. Receptury
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Hands-On Unsupervised Learning with Python. Implement machine learning and deep learning models using Scikit-Learn, TensorFlow, and more
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Machine Learning with the Elastic Stack. Expert techniques to integrate machine learning with distributed search and analytics
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Python Machine Learning Blueprints. Put your machine learning concepts to the test by developing real-world smart projects - Second Edition
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Natural Language Processing with PyTorch. Build Intelligent Language Applications Using Deep Learning
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Keras 2.x Projects. 9 projects demonstrating faster experimentation of neural network and deep learning applications using Keras
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Python: Advanced Guide to Artificial Intelligence. Expert machine learning systems and intelligent agents using Python
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Hands-On Data Science with R. Techniques to perform data manipulation and mining to build smart analytical models using R
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Machine Learning in Java. Helpful techniques to design, build, and deploy powerful machine learning applications in Java - Second Edition
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Hands-On Machine Learning with Azure. Build powerful models with cognitive machine learning and artificial intelligence
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Python Deep Learning Projects. 9 projects demystifying neural network and deep learning models for building intelligent systems
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Keras Reinforcement Learning Projects. 9 projects exploring popular reinforcement learning techniques to build self-learning agents
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Hands-On Markov Models with Python. Implement probabilistic models for learning complex data sequences using the Python ecosystem
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Hands-On Transfer Learning with Python. Implement advanced deep learning and neural network models using TensorFlow and Keras
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Machine Learning Algorithms. Popular algorithms for data science and machine learning - Second Edition
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Hands-On Convolutional Neural Networks with TensorFlow. Solve computer vision problems with modeling in TensorFlow and Python
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Building Machine Learning Systems with Python. Explore machine learning and deep learning techniques for building intelligent systems using scikit-learn and TensorFlow - Third Edition
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Hands-On Deep Learning for Images with TensorFlow. Build intelligent computer vision applications using TensorFlow and Keras
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Hands-On Ensemble Learning with R. A beginner's guide to combining the power of machine learning algorithms using ensemble techniques
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Deep Learning. Praktyczne wprowadzenie
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Hands-On Natural Language Processing with Python. A practical guide to applying deep learning architectures to your NLP applications
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Natural Language Processing and Computational Linguistics. A practical guide to text analysis with Python, Gensim, spaCy, and Keras
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Mastering Machine Learning for Penetration Testing. Develop an extensive skill set to break self-learning systems using Python
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Beginning Swift. Master the fundamentals of programming in Swift 4
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Hands-on Machine Learning with JavaScript. Solve complex computational web problems using machine learning
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Mastering Machine Learning Algorithms. Expert techniques to implement popular machine learning algorithms and fine-tune your models
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Hands-On Automated Machine Learning. A beginner's guide to building automated machine learning systems using AutoML and Python
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Splunk 7 Essentials. Demystify machine data by leveraging datasets, building reports, and sharing powerful insights - Third Edition
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TensorFlow Deep Learning Projects. 10 real-world projects on computer vision, machine translation, chatbots, and reinforcement learning
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Deep Learning Quick Reference. Useful hacks for training and optimizing deep neural networks with TensorFlow and Keras
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TensorFlow for Deep Learning. From Linear Regression to Reinforcement Learning
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Machine Learning with Swift. Artificial Intelligence for iOS
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Deep Learning with PyTorch. A practical approach to building neural network models using PyTorch
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R Deep Learning Projects. Master the techniques to design and develop neural network models in R
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Machine Learning for Time-Series with Python. Use Python to forecast, predict, and detect anomalies with state-of-the-art machine learning methods - Second Edition
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Applied Deep Learning on Graphs. Leveraging Graph Data to Generate Impact Using Specialized Deep Learning Architectures