Build and deploy high-performance deep learning models using C++ for real-time applications where speed and efficiency matter. Free with your book: DRM-free PDF version + access to Packt's next-gen Reader\*Key FeaturesBuild deep learning models in C++ with PyTorch C++ API and CUDAImplement CNNs, RNNs, LSTMs, GANs, and Transformers in C++ for real-world applicationsOptimize and deploy machine learning models to production with scalable C++ pipel...

This book of the bestselling and widely acclaimed Python Machine Learning series is a comprehensive guide to machine and deep learning using PyTorch s simple to code framework. Purchase of the print or Kindle book includes a free eBook in PDF format.Key FeaturesLearn applied machine learning with a solid foundation in theoryClear, intuitive explanations take you deep into the theory and practice of Python machine learningFully updated and expan...

Understand, evaluate, and visualize dataAbout This BookLearn basic steps of data analysis and how to use Python and its packagesA step-by-step guide to predictive modeling including tips, tricks, and best practicesEffectively visualize a broad set of analyzed data and generate effective resultsWho This Book Is ForThis book is for Python Developers who are keen to get into data analysis and wish to visuali...

Learn to solve challenging data science problems by building powerful machine learning models using PythonAbout This BookUnderstand which algorithms to use in a given context with the help of this exciting recipe-based guideThis practical tutorial tackles real-world computing problems through a rigorous and effective approachBuild state-of-the-art models and develop personalized recommendations to perform machine learning at scal...

Learn to use IPython and Jupyter Notebook for your data analysis and visualization work. Key FeaturesLeverage the Jupyter Notebook for interactive data science and visualizationBecome an expert in high-performance computing and visualization for data analysis and scientific modelingA comprehensive coverage of scientific computing through many hands-on, example-driven recipes with detailed, step-by-step explanationsBook Description...

Here is the perfect comprehensive guide for readers with basic to intermediate level knowledge of machine learning and deep learning. It introduces tools such as NumPy for numerical processing, Pandas for panel data analysis, Matplotlib for visualization, Scikit-learn for machine learning, and Pytorch for deep learning with Python. It also serves as a long-term reference manual for the practitioners who will find solutions to commonly occurring scenarios.The book is divided into th...

Discover how to leverage Keras, the powerful and easy-to-use open source Python library for developing and evaluating deep learning modelsKey FeaturesGet to grips with various model evaluation metrics, including sensitivity, specificity, and AUC scoresExplore advanced concepts such as sequential memory and sequential modelingReinforce your skills with real-world development, screencasts, and knowledge checksBook DescriptionNe...

Dive deeper into neural networks and get your models trained, optimized with this quick reference guideKey Features\[\*\]A quick reference to all important deep learning concepts and their implementations\[\*\]Essential tips, tricks, and hacks to train a variety of deep learning models such as CNNs, RNNs, LSTMs, and more\[\*\]Supplemented with essential mathematics and theory, every chapter provides best practices and safe choices for training ...

Practical, accessible guide to becoming a data scientist, updated to include the latest advances in data science and related fields.Becoming a data scientist is hard. The job focuses on mathematical tools, but also demands fluency with software engineering, understanding of a business situation, and deep understanding of the data itself. This book provides a crash course in data science, combining all the necessary skills into a unified discipline.The focus...

Enhance the power of NumPy and start boosting your scientific computing capabilitiesKey FeaturesGrasp all aspects of numerical computing and understand NumPyExplore examples to learn exploratory data analysis (EDA), regression, and clusteringAccess NumPy libraries and use performance benchmarking to select the right toolBook DescriptionNumPy is one of the most important scientific computing libraries available for Python. Mas...

An easy-to-follow, step-by-step guide for getting to grips with the real-world application of machine learning algorithmsKey FeaturesExplore statistics and complex mathematics for data-intensive applicationsDiscover new developments in EM algorithm, PCA, and bayesian regressionStudy patterns and make predictions across various datasetsBook DescriptionMachine learning has gained tremendous popularity for its powerful and fast ...

Methodologies and recipes to regularize any machine learning and deep learning model using cutting-edge technologies such as stable diffusion, Dall-E and GPT-3 Purchase of the print or Kindle book includes a free PDF eBookKey FeaturesLearn to diagnose the need for regularization in any machine learning modelRegularize different ML models using a variety of techniques and methodsEnhance the functionality of your models using state of the art com...