Founded in 2004 in Birmingham, UK, Packt's mission is to help the world put software to work in new ways, through the delivery of effective learning and information services to IT professionals. Working towards that vision, we have published over 6,500 books and videos so far, providing IT professionals with the actionable knowledge they need to get the job done - whether that's specific learning on an emerging technology or optimizing key skills in more established tools. As part of our mission, we have also awarded over $1,000,000 through our Open Source Project Royalty scheme, helping numerous projects become household names along the way.
Wydawnictwo Packt Publishing - ebooki
więcej »
Tytuły książek wydawnictwa: Packt Publishing
-
Mastering Machine Learning Algorithms. Expert techniques for implementing popular machine learning algorithms, fine-tuning your models, and understanding how they work - Second Edition
-
Python Feature Engineering Cookbook. Over 70 recipes for creating, engineering, and transforming features to build machine learning models
-
Deep Learning with TensorFlow 2 and Keras. Regression, ConvNets, GANs, RNNs, NLP, and more with TensorFlow 2 and the Keras API - Second Edition
-
Advanced Deep Learning with R. Become an expert at designing, building, and improving advanced neural network models using R
-
Python Machine Learning. Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2 - Third Edition
-
Advanced Deep Learning with Python. Design and implement advanced next-generation AI solutions using TensorFlow and PyTorch
-
Dancing with Qubits. How quantum computing works and how it can change the world
-
Hands-On Machine Learning with TensorFlow.js. A guide to building ML applications integrated with web technology using the TensorFlow.js library
-
Machine Learning for Cybersecurity Cookbook. Over 80 recipes on how to implement machine learning algorithms for building security systems using Python
-
Machine Learning for OpenCV 4. Intelligent algorithms for building image processing apps using OpenCV 4, Python, and scikit-learn - Second Edition
-
Practical Machine Learning with R. Define, build, and evaluate machine learning models for real-world applications
-
Hands-On Deep Learning with Go. A practical guide to building and implementing neural network models using Go
-
Hands-On Deep Learning Algorithms with Python. Master deep learning algorithms with extensive math by implementing them using TensorFlow
-
Hands-On Ensemble Learning with Python. Build highly optimized ensemble machine learning models using scikit-learn and Keras
-
Hands-On Deep Learning for IoT. Train neural network models to develop intelligent IoT applications
-
Deep Learning for Natural Language Processing. Solve your natural language processing problems with smart deep neural networks
-
Machine Learning for Finance. Principles and practice for financial insiders
-
Supervised Machine Learning with Python. Develop rich Python coding practices while exploring supervised machine learning
-
Deep Learning with R for Beginners. Design neural network models in R 3.5 using TensorFlow, Keras, and MXNet
-
Mastering Machine Learning on AWS. Advanced machine learning in Python using SageMaker, Apache Spark, and TensorFlow
-
Advanced Machine Learning with R. Tackle data analytics and machine learning challenges and build complex applications with R 3.5
-
PyTorch Deep Learning Hands-On. Build CNNs, RNNs, GANs, reinforcement learning, and more, quickly and easily
-
Hands-On Deep Learning Architectures with Python. Create deep neural networks to solve computational problems using TensorFlow and Keras
-
Machine Learning for Data Mining. Improve your data mining capabilities with advanced predictive modeling
-
Machine Learning with Scala Quick Start Guide. Leverage popular machine learning algorithms and techniques and implement them in Scala
-
Applied Deep Learning with Keras. Solve complex real-life problems with the simplicity of Keras
-
Hands-On Q-Learning with Python. Practical Q-learning with OpenAI Gym, Keras, and TensorFlow
-
Machine Learning with R. Expert techniques for predictive modeling - Third Edition
-
TensorFlow Reinforcement Learning Quick Start Guide. Get up and running with training and deploying intelligent, self-learning agents using Python
-
Hands-On Neural Networks with Keras. Design and create neural networks using deep learning and artificial intelligence principles
-
Python Machine Learning Cookbook. Over 100 recipes to progress from smart data analytics to deep learning using real-world datasets - Second Edition
-
Hands-On Machine Learning with IBM Watson. Leverage IBM Watson to implement machine learning techniques and algorithms using Python
-
Machine Learning with R Quick Start Guide. A beginner's guide to implementing machine learning techniques from scratch using R 3.5
-
TensorFlow 2.0 Quick Start Guide. Get up to speed with the newly introduced features of TensorFlow 2.0
-
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
-
Deep Learning with Microsoft Cognitive Toolkit Quick Start Guide. A practical guide to building neural networks using Microsoft's open source deep learning framework
-
Applied Unsupervised Learning with R. Uncover hidden relationships and patterns with k-means clustering, hierarchical clustering, and PCA
-
Building Computer Vision Projects with OpenCV 4 and C++. Implement complex computer vision algorithms and explore deep learning and face detection
-
Neural Network Projects with Python. The ultimate guide to using Python to explore the true power of neural networks through six projects
-
Python Machine Learning By Example. Implement machine learning algorithms and techniques to build intelligent systems - Second Edition
-
Neural Networks with Keras Cookbook. Over 70 recipes leveraging deep learning techniques across image, text, audio, and game bots
-
Hands-On Unsupervised Learning with Python. Implement machine learning and deep learning models using Scikit-Learn, TensorFlow, and more
-
Hands-On Java Deep Learning for Computer Vision. Implement machine learning and neural network methodologies to perform computer vision-related tasks
-
Mastering Machine Learning with R. Advanced machine learning techniques for building smart applications with R 3.5 - Third Edition
-
Generative Adversarial Networks Projects. Build next-generation generative models using TensorFlow and Keras
-
Machine Learning Quick Reference. Quick and essential machine learning hacks for training smart data models
-
Ensemble Machine Learning Cookbook. Over 35 practical recipes to explore ensemble machine learning techniques using Python
-
Machine Learning with the Elastic Stack. Expert techniques to integrate machine learning with distributed search and analytics
-
Python Machine Learning Blueprints. Put your machine learning concepts to the test by developing real-world smart projects - Second Edition
-
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
-
R Machine Learning Projects. Implement supervised, unsupervised, and reinforcement learning techniques using R 3.5
-
Generative Adversarial Networks Cookbook. Over 100 recipes to build generative models using Python, TensorFlow, and Keras
-
Hands-On Machine Learning for Algorithmic Trading. Design and implement investment strategies based on smart algorithms that learn from data using Python
-
Keras 2.x Projects. 9 projects demonstrating faster experimentation of neural network and deep learning applications using Keras
-
Hands-On Machine Learning for Cybersecurity. Safeguard your system by making your machines intelligent using the Python ecosystem
-
Computer Vision Projects with OpenCV and Python 3. Six end-to-end projects built using machine learning with OpenCV, Python, and TensorFlow
-
Deep Learning with PyTorch Quick Start Guide. Learn to train and deploy neural network models in Python
-
Python: Advanced Guide to Artificial Intelligence. Expert machine learning systems and intelligent agents using Python
-
Artificial Intelligence and Machine Learning Fundamentals. Develop real-world applications powered by the latest AI advances
-
Hands-On Data Science with R. Techniques to perform data manipulation and mining to build smart analytical models using R
-
Hands-On Image Processing with Python. Expert techniques for advanced image analysis and effective interpretation of image data
-
Go Machine Learning Projects. Eight projects demonstrating end-to-end machine learning and predictive analytics applications in Go
-
Practical Site Reliability Engineering. Automate the process of designing, developing, and delivering highly reliable apps and services with SRE
-
TensorFlow Machine Learning Projects. Build 13 real-world projects with advanced numerical computations using the Python ecosystem
-
Recurrent Neural Networks with Python Quick Start Guide. Sequential learning and language modeling with TensorFlow
-
Machine Learning in Java. Helpful techniques to design, build, and deploy powerful machine learning applications in Java - Second Edition
-
Data Science Algorithms in a Week. Top 7 algorithms for scientific computing, data analysis, and machine learning - Second Edition
-
Hands-On Machine Learning with Azure. Build powerful models with cognitive machine learning and artificial intelligence
-
Advanced Deep Learning with Keras. Apply deep learning techniques, autoencoders, GANs, variational autoencoders, deep reinforcement learning, policy gradients, and more
-
Hands-On Artificial Intelligence for Beginners. An introduction to AI concepts, algorithms, and their implementation
-
Keras Deep Learning Cookbook. Over 30 recipes for implementing deep neural networks in Python
-
Python Deep Learning Projects. 9 projects demystifying neural network and deep learning models for building intelligent systems
-
Machine Learning Projects for Mobile Applications. Build Android and iOS applications using TensorFlow Lite and Core ML
-
Machine Learning for Healthcare Analytics Projects. Build smart AI applications using neural network methodologies across the healthcare vertical market
-
Machine Learning with scikit-learn Quick Start Guide. Classification, regression, and clustering techniques in Python
-
IBM Watson Projects. Eight exciting projects that put artificial intelligence into practice for optimal business performance
-
Python Reinforcement Learning Projects. Eight hands-on projects exploring reinforcement learning algorithms using TensorFlow
-
Hands-On Neural Network Programming with C#. Add powerful neural network capabilities to your C# enterprise applications
-
Applied Data Visualization with R and ggplot2. Create useful, elaborate, and visually appealing plots
-
Mastering Arduino. A project-based approach to electronics, circuits, and programming
-
Hands-On Markov Models with Python. Implement probabilistic models for learning complex data sequences using the Python ecosystem
-
R Programming Fundamentals. Deal with data using various modeling techniques
-
Learning Microsoft Cognitive Services. Use Cognitive Services APIs to add AI capabilities to your applications - Third Edition
-
Hands-On Artificial Intelligence with Java for Beginners. Build intelligent apps using machine learning and deep learning with Deeplearning4j
-
TensorFlow Machine Learning Cookbook. Over 60 recipes to build intelligent machine learning systems with the power of Python - Second Edition
-
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
-
Artificial Intelligence for Robotics. Build intelligent robots that perform human tasks using AI techniques
-
Machine Learning Algorithms. Popular algorithms for data science and machine learning - Second Edition
-
Hands-On Convolutional Neural Networks with TensorFlow. Solve computer vision problems with modeling in TensorFlow and Python
-
R Deep Learning Essentials. A step-by-step guide to building deep learning models using TensorFlow, Keras, and MXNet - Second Edition
-
Hands-On Deep Learning for Images with TensorFlow. Build intelligent computer vision applications using TensorFlow and Keras
-
Python Artificial Intelligence Projects for Beginners. Get up and running with Artificial Intelligence using 8 smart and exciting AI applications
-
Building Machine Learning Systems with Python. Explore machine learning and deep learning techniques for building intelligent systems using scikit-learn and TensorFlow - Third Edition
-
Hands-On Intelligent Agents with OpenAI Gym. Your guide to developing AI agents using deep reinforcement learning
-
Hands-On Ensemble Learning with R. A beginner's guide to combining the power of machine learning algorithms using ensemble techniques
-
Hands-On Natural Language Processing with Python. A practical guide to applying deep learning architectures to your NLP applications
-
Apache Spark Deep Learning Cookbook. Over 80 best practice recipes for the distributed training and deployment of neural networks using Keras and TensorFlow
-
Natural Language Processing and Computational Linguistics. A practical guide to text analysis with Python, Gensim, spaCy, and Keras
-
Hands-On Computer Vision with Julia. Build complex applications with advanced Julia packages for image processing, neural networks, and Artificial Intelligence
-
Java Deep Learning Projects. Implement 10 real-world deep learning applications using Deeplearning4j and open source APIs
-
Hands-On Reinforcement Learning with Python. Master reinforcement and deep reinforcement learning using OpenAI Gym and TensorFlow
-
Machine Learning with Core ML. An iOS developer's guide to implementing machine learning in mobile apps
-
Mastering Machine Learning for Penetration Testing. Develop an extensive skill set to break self-learning systems using Python
-
Beginning Swift. Master the fundamentals of programming in Swift 4
-
Hands-On Data Science with Anaconda. Utilize the right mix of tools to create high-performance data science applications
-
Natural Language Processing with TensorFlow. Teach language to machines using Python's deep learning library
-
Google Cloud AI Services Quick Start Guide. Build intelligent applications with Google Cloud AI services
-
Hands-on Machine Learning with JavaScript. Solve complex computational web problems using machine learning
-
Mastering Machine Learning Algorithms. Expert techniques to implement popular machine learning algorithms and fine-tune your models
-
Artificial Intelligence for Big Data. Complete guide to automating Big Data solutions using Artificial Intelligence techniques
-
Hands-On GUI Programming with C++ and Qt5. Build stunning cross-platform applications and widgets with the most powerful GUI framework
-
Machine Learning Solutions. Expert techniques to tackle complex machine learning problems using Python
-
Hands-On Automated Machine Learning. A beginner's guide to building automated machine learning systems using AutoML and Python
-
Reinforcement Learning with TensorFlow. A beginner's guide to designing self-learning systems with TensorFlow and OpenAI Gym
-
Deep Learning with TensorFlow. Explore neural networks and build intelligent systems with Python - Second Edition
-
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
-
TensorFlow: Powerful Predictive Analytics with TensorFlow. Predict valuable insights of your data with TensorFlow