Hands-on projects cover all the key deep learning methods built step-by-step in PyTorchKey FeaturesInternals and principles of PyTorchImplement key deep learning methods in PyTorch: CNNs, GANs, RNNs, reinforcement learning, and moreBuild deep learning workflows and take deep learning models from prototyping to productionBook DescriptionPyTorch Deep Learning Hands-On is a book for engineers who want a fast-paced guide to doing deep learning work with Pytorch.
Delve into the world of real-world financial applications using deep learning, artificial intelligence, and production-grade data feeds and technology with PythonKey FeaturesUnderstand how to obtain financial data via Quandl or internal systemsAutomate commercial banking using artificial intelligence and Python programsImplement various artificial intelligence models to make personal banking easyBook DescriptionRemodeling your outlook on banking begins with keeping up to date with the latest and most effective approaches, such as artificial intelligence (AI).
Implement intelligent agents using PyTorch to solve classic AI problems, play console games like Atari, and perform tasks such as autonomous driving using the CARLA driving simulatorKey FeaturesExplore the OpenAI Gym toolkit and interface to use over 700 learning tasksImplement agents to solve simple to complex AI problemsStudy learning environments and discover how to create your ownBook DescriptionMany real-world problems can be broken down into tasks that require a series of decisions to be made or actions to be taken.
Grasp the fundamentals of Artificial Intelligence and build your own intelligent systems with easeKey FeaturesEnter the world of AI with the help of solid concepts and real-world use casesExplore AI components to build real-world automated intelligenceBecome well versed with machine learning and deep learning conceptsBook DescriptionVirtual Assistants, such as Alexa and Siri, process our requests, Google's cars have started to read addresses, and Amazon's prices and Netflix's recommended videos are decided by AI.
Implement supervised and unsupervised machine learning algorithms using C++ libraries such as PyTorch C++ API, Caffe2, Shogun, Shark-ML, mlpack, and dlib with the help of real-world examples and datasetsKey FeaturesBecome familiar with data processing, performance measuring, and model selection using various C++ librariesImplement practical machine learning and deep learning techniques to build smart modelsDeploy machine learning models to work on mobile and embedded devicesBook DescriptionC++ can make your machine learning models run faster and more efficiently.
Build and deploy intelligent applications for natural language processing with Python by using industry standard tools and recently popular methods in deep learningKey FeaturesA no-math, code-driven programmer's guide to text processing and NLPGet state of the art results with modern tooling across linguistics, text vectors and machine learningFundamentals of NLP methods from spaCy, gensim, scikit-learn and PyTorchBook DescriptionNLP in Python is among the most sought after skills among data scientists.
Deep learning simplified by taking supervised, unsupervised, and reinforcement learning to the next level using the Python ecosystemKey FeaturesBuild deep learning models with transfer learning principles in Pythonimplement transfer learning to solve real-world research problemsPerform complex operations such as image captioning neural style transferBook DescriptionTransfer learning is a machine learning (ML) technique where knowledge gained during training a set of problems can be used to solve other similar problems.
Discover a project-based approach to mastering machine learning concepts by applying them to everyday problems using libraries such as scikit-learn, TensorFlow, and KerasKey FeaturesGet to grips with Python's machine learning libraries including scikit-learn, TensorFlow, and KerasImplement advanced concepts and popular machine learning algorithms in real-world projectsBuild analytics, computer vision, and neural network projects Book DescriptionMachine learning is transforming the way we understand and interact with the world around us.
This book gives a comprehensive introduction to natural language processing (NLP) and its applications, covering the topics of multimodal data processing, Chinese word segmentation, new word discovery, named entity recognition, keyword analysis, and knowledge graph construction in terms of semantic analysis.
Simplesmente em profundidade Este livro tem como objetivo apresentar as técnicas fundamentais de processamento de linguagem natural, desenvolver uma compreensão dos limites dessas técnicas e dos problemas de pesquisa atuais e avaliar algumas aplicações atuais e potenciais.
Master advanced spaCy techniques, including custom pipelines, LLM integration, and model training, to build NLP solutions efficientlyKey FeaturesBuild end-to-end NLP workflows, from local development to production, using Weasel and FastAPIMaster no-training NLP development with spacy-llm, covering everything from prompt engineering to custom tasksCreate advanced NLP solutions, including custom components and neural coreference resolutionPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionMastering spaCy, Second Edition is your comprehensive guide to building sophisticated NLP applications using the spaCy ecosystem.
It has been estimated that over a billion people are using or learning English as a second or foreign language, and the numbers are growing not only for English but for other languages as well.
Begin your generative AI journey with Python as you explore large language models, understand responsible generative AI practices, and apply your knowledge to real-world applications through guided tutorialsKey FeaturesGain expertise in prompt engineering, LLM fine-tuning, and domain adaptationUse transformers-based LLMs and diffusion models to implement AI applicationsDiscover strategies to optimize model performance, address ethical considerations, and build trust in AI systemsPurchase of the print or Kindle book includes a free PDF eBookBook DescriptionThe intricacies and breadth of generative AI (GenAI) and large language models can sometimes eclipse their practical application.
The computational approach of this book is aimed at simulating the human ability to understand various kinds of phrases with a novel metaphoric component.
The computational approach of this book is aimed at simulating the human ability to understand various kinds of phrases with a novel metaphoric component.
Data Mining: A Tutorial-Based Primer, Second Edition provides a comprehensive introduction to data mining with a focus on model building and testing, as well as on interpreting and validating results.
"e;Ultra Low Bit-Rate Speech Coding"e; focuses on the specialized topic of speech coding at very low bit-rates of 1 Kbits/sec and less, particularly at the lower ends of this range, down to 100 bps.
Many industry experts consider unsupervised learning the next frontier in artificial intelligence, one that may hold the key to general artificial intelligence.
Many industry experts consider unsupervised learning the next frontier in artificial intelligence, one that may hold the key to general artificial intelligence.
If you want to build an enterprise-quality application that uses natural language text but arent sure where to begin or what tools to use, this practical guide will help get you started.
If you want to build an enterprise-quality application that uses natural language text but arent sure where to begin or what tools to use, this practical guide will help get you started.
Natural Language Processing (NLP) provides boundless opportunities for solving problems in artificial intelligence, making products such as Amazon Alexa and Google Translate possible.
Natural Language Processing (NLP) provides boundless opportunities for solving problems in artificial intelligence, making products such as Amazon Alexa and Google Translate possible.
Much of the data available today is unstructured and text-heavy, making it challenging for analysts to apply their usual data wrangling and visualization tools.
