Patrick R. Nicolas - książki
Tytuły autora: dostępne w księgarni Ebookpoint
-
Deep Learning with MXNet Cookbook. Discover an extensive collection of recipes for creating and implementing AI models on MXNet
-
Architecting Data and Machine Learning Platforms
-
Probabilistic Machine Learning for Finance and Investing
-
Zaufanie do systemów sztucznej inteligencji
-
Machine Learning for High-Risk Applications
-
Deep Learning with TensorFlow and Keras. Build and deploy supervised, unsupervised, deep, and reinforcement learning models - Third Edition
-
Natural Language Processing with TensorFlow. The definitive NLP book to implement the most sought-after machine learning models and tasks - Second Edition
-
Głębokie uczenie przez wzmacnianie. Praca z chatbotami oraz robotyka, optymalizacja dyskretna i automatyzacja sieciowa w praktyce. Wydanie II
-
Practical Simulations for Machine Learning
-
Fundamentals of Deep Learning. 2nd Edition
-
IBM Cloud Pak for Data. An enterprise platform to operationalize data, analytics, and AI
-
Reliable Machine Learning
-
Practical Machine Learning for Computer Vision
-
Wzorce projektowe uczenia maszynowego. Rozwiązania typowych problemów dotyczących przygotowania danych, konstruowania modeli i MLOps
-
PyTorch Pocket Reference
-
Kubeflow Operations Guide
-
Machine Learning Design Patterns
-
Praktyczne uczenie nienadzorowane przy użyciu języka Python
-
Deep Learning for Beginners. A beginner's guide to getting up and running with deep learning from scratch using Python
-
The Deep Learning Workshop. Learn the skills you need to develop your own next-generation deep learning models with TensorFlow and Keras
-
Hands-On Python Deep Learning for the Web. Integrating neural network architectures to build smart web apps with Flask, Django, and TensorFlow
-
Hands-On Deep Learning with R. A practical guide to designing, building, and improving neural network models using R
-
Deep Learning with R Cookbook. Over 45 unique recipes to delve into neural network techniques using R 3.5.x
-
Deep Learning with TensorFlow 2 and Keras. Regression, ConvNets, GANs, RNNs, NLP, and more with TensorFlow 2 and the Keras API - Second Edition
-
Google BigQuery: The Definitive Guide. Data Warehousing, Analytics, and Machine Learning at Scale
-
Practical Automated Machine Learning on Azure. Using Azure Machine Learning to Quickly Build AI Solutions
-
Practical Data Science with SAP. Machine Learning Techniques for Enterprise Data
-
Deep Learning with R for Beginners. Design neural network models in R 3.5 using TensorFlow, Keras, and MXNet
-
PyTorch Deep Learning Hands-On. Build CNNs, RNNs, GANs, reinforcement learning, and more, quickly and easily
-
Machine Learning with R Quick Start Guide. A beginner's guide to implementing machine learning techniques from scratch using R 3.5
-
Ensemble Machine Learning Cookbook. Over 35 practical recipes to explore ensemble machine learning techniques using Python
-
Intelligent Projects Using Python. 9 real-world AI projects leveraging machine learning and deep learning with TensorFlow and Keras
-
Python Deep Learning. Exploring deep learning techniques and neural network architectures with PyTorch, Keras, and TensorFlow - Second Edition
-
Python: Advanced Guide to Artificial Intelligence. Expert machine learning systems and intelligent agents using Python
-
Hands-On Image Processing with Python. Expert techniques for advanced image analysis and effective interpretation of image data
-
Hands-On Artificial Intelligence for Beginners. An introduction to AI concepts, algorithms, and their implementation
-
Hands-On Machine Learning with Azure. Build powerful models with cognitive machine learning and artificial intelligence
-
Deep Learning. Uczenie głębokie z językiem Python. Sztuczna inteligencja i sieci neuronowe
-
Python Reinforcement Learning Projects. Eight hands-on projects exploring reinforcement learning algorithms using TensorFlow
-
Hands-On Markov Models with Python. Implement probabilistic models for learning complex data sequences using the Python ecosystem
-
Hands-On Transfer Learning with Python. Implement advanced deep learning and neural network models using TensorFlow and Keras
-
Hands-On Artificial Intelligence for Search. Building intelligent applications and perform enterprise searches
-
Hands-On Convolutional Neural Networks with TensorFlow. Solve computer vision problems with modeling in TensorFlow and Python
-
Hands-On Intelligent Agents with OpenAI Gym. Your guide to developing AI agents using deep reinforcement learning
-
Deep Learning. Praktyczne wprowadzenie
-
Hands-On Natural Language Processing with Python. A practical guide to applying deep learning architectures to your NLP applications
-
Artificial Intelligence for Big Data. Complete guide to automating Big Data solutions using Artificial Intelligence techniques
-
Splunk 7 Essentials. Demystify machine data by leveraging datasets, building reports, and sharing powerful insights - Third Edition
-
TensorFlow Deep Learning Projects. 10 real-world projects on computer vision, machine translation, chatbots, and reinforcement learning
-
R Deep Learning Projects. Master the techniques to design and develop neural network models in R
-
Deep Learning for Computer Vision. Expert techniques to train advanced neural networks using TensorFlow and Keras
-
Deep Learning. A Practitioner's Approach
-
Python Deep Learning. Next generation techniques to revolutionize computer vision, AI, speech and data analysis
-
Deep Learning with Keras. Implementing deep learning models and neural networks with the power of Python
-
Windows Server 2016 Hyper-V Cookbook. Save time and resources by getting to know the best practices and intelligence from industry experts - Second Edition
-
Test-Driven Machine Learning. Control your machine learning algorithms using test-driven development to achieve quantifiable milestones
-
Scala for Machine Learning. Leverage Scala and Machine Learning to construct and study systems that can learn from data