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Certified AI Fundamentals Professional

4.4 Filled star Filled star Filled star Filled star Empty star
Based on 40 ratings
Aqib Chaudhary

Welcome to the "Certified AI Fundamentals Professional" course. In today's rapidly evolving technological landscape, Artificial Intelligence (AI) is not just a buzzword; it's a transformative force reshaping industries, creating new opportunities, and redefining the future of work. This course is meticulously designed to provide you with a robust and comprehensive understanding of AI's core principles and applications.

Why AI Matters Now More Than Ever

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Welcome to the "Certified AI Fundamentals Professional" course. In today's rapidly evolving technological landscape, Artificial Intelligence (AI) is not just a buzzword; it's a transformative force reshaping industries, creating new opportunities, and redefining the future of work. This course is meticulously designed to provide you with a robust and comprehensive understanding of AI's core principles and applications.

Why AI Matters Now More Than Ever

AI is no longer confined to research labs; it's integrated into our daily lives, from smart assistants and recommendation engines to autonomous vehicles and medical diagnostics. Professionals across all sectors need to understand AI's capabilities, limitations, and ethical implications. This course bridges that gap, empowering you with the knowledge to thrive in an AI-driven world.

What You'll Learn

This program takes you from foundational concepts to practical applications, covering:

  • Core AI Concepts: Understand what AI truly is, its history, and its various subfields.

  • Machine Learning (ML): Dive into the heart of modern AI, exploring supervised, unsupervised, and reinforcement learning paradigms, and key algorithms.

  • Deep Learning Basics: Get an introduction to neural networks and how they power advanced AI capabilities.

  • Natural Language Processing (NLP): Discover how machines understand and generate human language.

  • Computer Vision: Explore how AI enables computers to "see" and interpret images and videos.

  • AI Ethics & Future Trends: Grapple with the societal impact, ethical considerations, and exciting future directions of AI.

Course Uniqueness & Value

Unlike many superficial introductions, this course offers a structured, in-depth learning path that doesn't shy away from technical concepts but presents them in an accessible manner. We focus on building a solid conceptual foundation, ensuring you not only know *what* AI does but *how* and *why*. By the end, you'll be equipped with the fundamental literacy to confidently engage in AI-related discussions, evaluate AI projects, and set yourself up for further specialization in this exciting field. Become a Certified AI Fundamentals Professional and unlock new career possibilities.

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What's inside

Syllabus

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Learners who complete Certified AI Fundamentals Professional will develop knowledge and skills that may be useful to these careers:

Reading list

We've selected 50 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Certified AI Fundamentals Professional.
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Artificial Intelligence
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The definitive benchmark textbook of modern artificial intelligence, rigorously unifying search algorithms, probabilistic reasoning, knowledge representation, and machine learning into an elegant, coherent agent-based framework.
Artificial Intelligence: A Modern Approach
Hardcover
$$$$
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Hands-On Machine Learning with Scikit-Learn, Keras,...
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The premier applied machine learning masterwork, taking practitioners through intuitive concept breakdowns, production-grade Scikit-Learn workflows, and deep neural architecture implementations.
Hands-On Machine Learning with Scikit-Learn, Keras,...
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Hands-On Machine Learning with Scikit-Learn, Keras,...
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Hands-On Machine Learning with Scikit-Learn, Keras,...
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Speech and Language Processing. An Introduction to...
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This widely recognized and comprehensive textbook that provides a deep dive into the subject of language processing. It is suitable for both undergraduate and graduate-level courses and is considered a must-read for gaining a strong theoretical and applied understanding of NLP. The authors are leading researchers in the field, and the book is frequently updated to reflect advancements. The latest draft is available online.
Speech and Language Processing An Introduction to...
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Deep Learning
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The definitive mathematical canon of modern deep learning, establishing foundational theory across numerical computation, backpropagation optimization, autoencoders, and generative modeling.
Deep Learning (Adaptive Computation and Machine...
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Deep Learning (Adaptive Computation and Machine...
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Pattern Recognition and Machine Learning
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A timeless mathematical masterpiece that provides an uncompromising Bayesian and probabilistic foundation for linear classifiers, graphical models, kernel methods, and sampling algorithms.
Pattern Recognition and Machine Learning...
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Pattern Recognition and Machine Learning...
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Pattern Recognition and Machine Learning (text...
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Deep Learning with Python
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Authored by the creator of Keras, this foundational text blends conceptual clarity with hands-on Python implementations, teaching intuitive neural architecture design and gradient-based training.
Deep Learning with Python, Second Edition
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Deep Learning with Python
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(Español) Deep Learning con Python
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(Polski) Deep Learning Praca z jezykiem Python i biblioteka...
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(Deutsch) Deep Learning mit Python und Keras: Das Praxis...
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(Italiano) Deep Learning con Python: Imparare a implementare...
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Introduction To Algorithms
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Classic textbook on algorithms that has been used by generations of computer science students. It covers a wide range of algorithms, from sorting and searching to graph algorithms and computational geometry.
Introduction to Algorithms, fourth edition
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Introduction to Algorithms, 3rd Edition (Mit Press)
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Introduction to Algorithms (Eastern Economy Edition)
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Introduction to Algorithms, Second Edition
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Introduction to Algorithms, fourth edition
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The Elements of Statistical Learning
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Provides a comprehensive overview of statistical learning methods, covering both supervised and unsupervised techniques. It valuable reference for understanding the theoretical underpinnings of many supervised learning algorithms. While mathematically rigorous, it emphasizes concepts over intricate mathematical proofs, making it accessible to those with a solid foundation in statistics and linear algebra. It is widely considered a classic in the field and is often used as a graduate-level textbook.
The Elements of Statistical Learning: Data Mining,...
Hardcover
$$$
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Natural Language Processing with Transformers,...
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This practical book dives into using transformers, the dominant architecture for many state-of-the-art NLP tasks, with the Hugging Face Transformers library. , It's ideal for data scientists and coders who want to learn how to build and scale models for various NLP applications using this popular library. It covers contemporary topics and is highly relevant for current NLP practice.
Natural Language Processing with Transformers,...
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Natural Language Processing with Transformers,...
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Build a Large Language Model (from Scratch)
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Guides deep learning practitioners step-by-step through implementing transformer self-attention, token embeddings, and multi-layer perceptrons in pure PyTorch without relying on high-level wrappers.
Build a Large Language Model (from Scratch)
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Build a Large Language Model (from Scratch)
Kindle Edition
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Python Machine Learning
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Provides a comprehensive guide to machine learning and deep learning with Python, covering essential techniques with clear explanations and practical examples. It serves as both a tutorial and a reference, teaching the principles behind machine learning and how to build models using libraries like scikit-learn and TensorFlow. It's a valuable resource for those looking to implement supervised learning algorithms in Python.
Machine Learning with PyTorch and Scikit-Learn:...
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Machine Learning with PyTorch and Scikit-Learn:...
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Python Machine Learning: Machine Learning and Deep...
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Python Machine Learning - Second Edition: Machine...
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(Italiano) Machine Learning con Python - Nuova edizione:...
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(Deutsch) Machine Learning mit Python und Keras, TensorFlow 2...
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(Español) Machine Learning con PyTorch y Scikit-Learn:...
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(Español) Python Machine Learning
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Machine Learning Engineering
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A distilled practitioner field guide outlining best practices for the entire machine learning lifecycle, from problem formulation and feature engineering to model deployment and monitoring.
Machine Learning Engineering
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Machine Learning Engineering
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Hands-On Large Language Models
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Illuminates modern generative neural architectures through celebrated visual diagrams and code workflows, covering contrastive embeddings, semantic search, and retrieval-augmented generation.
Hands-On Large Language Models: Language...
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Hands-On Large Language Models: Language...
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Designing Machine Learning Systems
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Delivers an end-to-end systems architecture guide for building reliable production ML systems, addressing data engineering trade-offs, real-time feature computation, and model drift mitigation.
Designing Machine Learning Systems: An Iterative...
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Designing Machine Learning Systems
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Deep Learning
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A recent book by a renowned author in the field, this text offers a comprehensive introduction to the central ideas underpinning deep learning. It's intended for both newcomers and those with experience. It focuses on enduring concepts and is structured for self-study or as a textbook, with an emphasis on practical value over abstract theory.
Deep Learning: Foundations and Concepts
Hardcover
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Mathematics for Machine Learning
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Bridges linear algebra, multivariable calculus, vector geometry, and probability directly to fundamental machine learning algorithms like PCA, Gaussian mixture models, and support vector machines.
Mathematics for Machine Learning
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LLM Engineer's Handbook
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Examines production-grade engineering patterns for training and serving large language models, addressing distributed GPU training, synthetic data pipelines, and quantitative model evaluations.
LLM Engineer's Handbook: Master the art of...
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LLM Engineer's Handbook: Master the art of...
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Reinforcement Learning, second edition
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Presents the foundational treatise on Markov decision processes, temporal-difference learning, and policy gradient methods that underpin autonomous decision-making and modern post-training alignment workflows.
Reinforcement Learning, second edition: An...
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Reinforcement Learning, second edition: An...
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Dive into Deep Learning
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Delivers an interactive, comprehensive curriculum teaching multi-layer perceptrons, convolutional neural networks, attention mechanisms, and optimization math with runnable multi-framework code.
Dive into Deep Learning
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Introduction to Machine Learning with Python
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Instructs data scientists on applied model evaluation, cross-validation, hyperparameter tuning, and pipeline construction through Scikit-Learn maintainers Andreas Müller and Sarah Guido.
Introduction to Machine Learning with Python: A...
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Understanding Machine Learning
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This textbook offers a principled introduction to machine learning, bridging the gap between theory and algorithms. It provides a theoretical account of fundamental ideas and mathematical derivations for practical algorithms. Designed for advanced undergraduates or beginning graduate students, it makes the fundamentals accessible with a focus on understanding the 'why' behind the algorithms.
Understanding Machine Learning: From Theory to...
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Understanding Machine Learning: From Theory to...
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Algorithmics
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Comprehensive treatment of algorithmics, from the foundations to advanced topics such as randomized algorithms and approximation algorithms. It is written in a clear and concise style, and it includes many examples and exercises.
Algorithmics: The Spirit of Computing
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Algorithms, Part II
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Comprehensive introduction to the design and analysis of algorithms, written in a clear and concise style. It covers a wide range of algorithms, from sorting and searching to graph algorithms and computational geometry.
Algorithms, Part II
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Generative Deep Learning
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Instructs engineers on building generative neural models, from variational autoencoders and generative adversarial networks to diffusion models and multimodal text-to-image synthesis pipelines.
Generative Deep Learning: Teaching Machines To...
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Generative Deep Learning: Teaching Machines to...
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Generative Deep Learning: Teaching Machines To...
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(Français) Deep learning génératif
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(Deutsch) Generatives Deep Learning: Maschinen das Malen,...
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(中文) Generative deep learning(Chinese Edition)
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Multiple View Geometry in Computer Vision
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Considered a classic in the field, this book delves deep into the geometry of multiple cameras and 3D reconstruction. It's essential for those looking to deepen their understanding of the mathematical foundations of computer vision, particularly for topics like autonomous driving and 3D mapping. It rigorous text often used at the graduate level.
Multiple View Geometry in Computer Vision
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Multiple View Geometry in Computer Vision
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Foundations of Statistical Natural Language...
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This foundational text offers a comprehensive introduction to statistical methods in NLP. It covers the necessary theory and algorithms for building NLP tools, with a rigorous treatment of mathematical and linguistic foundations. While published in 1999, it remains highly relevant for understanding the statistical underpinnings of many NLP techniques and valuable reference for researchers and students alike.
Foundations of Statistical Natural Language...
Hardcover
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Foundations of Statistical Natural Language...
Kindle Edition
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Deep Learning for Computer Vision
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Examines deep learning techniques specifically for computer vision tasks, including CNNs, object detection, and image segmentation. It provides a comprehensive understanding of concepts and algorithms, making it helpful for practitioners and researchers in this area. It includes practical examples using TensorFlow and Keras.
Deep Learning for Computer Vision: Expert...
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Computer Vision
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Provides a comprehensive and rigorous treatment of computer vision, covering a wide range of topics from image formation to object recognition. It's suitable for advanced undergraduates and graduate students seeking a deep understanding of the field. It is often used as a primary textbook in university courses.
Computer Vision: A Modern Approach
Hardcover
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Applied Predictive Modeling
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Focuses on the process of building predictive models, a core application of supervised learning. It covers various techniques and practical considerations for developing accurate and robust models. It valuable resource for practitioners and students interested in the applied aspects of supervised learning and predictive analytics.
Applied Predictive Modeling
Hardcover
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Applied Predictive Modeling
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LLM Systems in Production
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Delivers battle-tested operational patterns for serving foundation models, addressing GPU cluster throughput, semantic caching, automated evaluation benchmarks, and continuous latency minimization.
LLM Systems in Production
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LLM Systems in Production
Kindle Edition
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Machine Learning in Production
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Investigates the engineering realities of running automated ML pipelines in production, addressing data drift detection, automated retraining loops, telemetry infrastructure, and continuous validation.
Machine Learning in Production
Paperback
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Machine Learning in Production
Kindle Edition
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Natural Language Processing with Python
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Serves as a practical introduction to NLP with a strong focus on using the Natural Language Toolkit (NLTK) library in Python. It's excellent for beginners and those who want a hands-on approach to learning NLP concepts through programming examples. It's widely used as a textbook and provides a solid foundation for implementing basic NLP tasks.
Natural Language Processing with Python: Analyzing...
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(中文) Python Natural Language Processing(Chinese Edition)
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Natural Language Processing with Python: Analyzing...
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Neural Networks and Deep Learning
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This textbook covers both classical and modern models in deep learning, placing an emphasis on understanding the relationship between traditional machine learning and neural networks. It includes discussions on various network architectures and advanced topics. Written for graduate students, researchers, and practitioners, it serves as a solid reference with an application-centric view. It is suitable as a textbook.
Neural Networks and Deep Learning
Paperback
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Neural Networks and Deep Learning
Kindle Edition
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Computer Vision
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Focuses on the mathematical and statistical underpinnings of computer vision, providing a solid theoretical foundation. It's well-suited for those who want to understand the principles behind various algorithms and models. It can be a valuable reference for researchers and graduate students.
Computer Vision
Hardcover
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Computer Vision: Models, Learning, and Inference
Kindle Edition
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Computer Vision
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Presents advanced computer vision methods, with a strong emphasis on recent machine and deep learning techniques. It covers topics like generative adversarial networks and deep reinforcement learning, suitable for researchers and advanced students.
Computer Vision: Principles, Algorithms,...
Hardcover
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Advanced Methods and Deep Learning in Computer...
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Computer and Machine Vision: Theory, Algorithms,...
Kindle Edition
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Computer and Machine Vision by E. R. Davies (13-May...
Unknown Binding
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(中文) Computer vision: principle. algorithm. application...
Paperback
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The Art of Chunking in AI Systems
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Focuses on token segmentation, attention context preservation, and hierarchical chunking, giving deep learning engineers targeted techniques to maximize transformer retrieval accuracy.
The Art of Chunking in AI Systems
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The Art of Chunking in AI Systems
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Deep Learning for Vision Systems
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Covers deep learning concepts and their application to computer vision, explaining how computers can understand images. It explores image classification, object detection, and generative models. It's a good resource for those looking to apply deep learning to vision tasks.
Deep Learning for Vision Systems
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Deep Learning for Vision Systems
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Deep Learning for Coders with fastai and PyTorch
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Takes a code-first approach to deep learning using the fastai library, which is built on PyTorch. It focuses on practical applications and achieving state-of-the-art results with minimal code. It's well-suited for coders who want to quickly get into building and deploying deep learning models. It's a great resource for practical implementation.
Deep Learning for Coders with Fastai and PyTorch:...
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Deep Learning for Coders with fastai and PyTorch:...
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Local Models That Don't Feel Dumb
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Details an applied architectural methodology for running high-performance open-weights models on local workstations, tackling quantization fidelity, memory bandwidth limits, and prompt assembly trade-offs.
Local Models That Don't Feel Dumb
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Local Models That Don't Feel Dumb
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DevOps for AI-Native Platforms
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Details cloud-native DevOps architectures for orchestrating automated machine learning lifecycles, integrating CI/CD triggers, model registry synchronization, and automated hyperparameter sweep workers.
DevOps for AI-Native Platforms
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DevOps for AI-Native Platforms
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Practical Statistics for Data Scientists
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Bridges the gap between statistics and data science, covering essential statistical concepts relevant to machine learning, including supervised learning. It provides practical guidance and examples in both R and Python. It valuable resource for data scientists who need a solid understanding of the statistical foundations of supervised learning.
Practical Statistics for Data Scientists: 50+...
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Practical Statistics for Data Scientists: 50...
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Practical Statistics for Data Scientists: 50+...
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Practical Statistics for Data Scientists: 50+...
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Introduction to Natural Language Processing
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This textbook offers a technical perspective on NLP, synthesizing classical methods with contemporary machine learning techniques. , It covers topics from basic textual analysis to structured representations and semantic analysis, making it suitable for advanced undergraduate and graduate courses. It requires a background in programming and college-level mathematics.
Introduction to Natural Language Processing...
Hardcover
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Introduction to Natural Language Processing...
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Probabilistic Graphical Models
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Offers a rigorous theoretical foundation for reasoning under uncertainty, combining graph theory and probability distributions to model complex high-dimensional dependencies across autonomous intelligent systems.
Probabilistic Graphical Models: Principles and...
Hardcover
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Probabilistic Graphical Models: Principles and...
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Deep Learning with PyTorch
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Provides a comprehensive guide to deep learning using the PyTorch framework. It covers building, training, and tuning neural networks with PyTorch tools. It's a practical book suitable for those who want to learn deep learning specifically with PyTorch and understand its core concepts.
Deep Learning with PyTorch: Build, train, and tune...
Kindle Edition
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Building Transformer Models with PyTorch 2.0
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Demonstrates how to leverage PyTorch 2.0 compiler optimizations and torch.compile to accelerate transformer training, attention layer fusion, and distributed model parallelism.
Building Transformer Models with PyTorch 2.0: NLP,...
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Building Transformer Models with PyTorch 2.0: NLP,...
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Machine Learning with PyTorch and Scikit-Learn
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Covers machine learning and deep learning with Python, specifically utilizing PyTorch and Scikit-Learn. It provides a good balance of theory and practical implementation, including neural network concepts. It's suitable for those who want to learn both traditional machine learning and deep learning using these popular libraries.
Machine Learning with PyTorch and Scikit-Learn:...
Kindle Edition
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The Algorithm Design Manual
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Practical guide to designing and implementing efficient algorithms. It covers a wide range of algorithm design techniques, from divide-and-conquer to dynamic programming.
The Algorithm Design Manual (Texts in Computer...
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The Algorithm Design Manual
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(The Algorithm Design Manual) [By: Skiena, Steven S...
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The Algorithm Design Manual (Texts in Computer...
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The Algorithm Design Manual
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Learning OpenCV 4 Computer Vision with Python 3
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Offers a practical, hands-on introduction to computer vision using the widely popular OpenCV library and Python. It's excellent for beginners and those who want to implement computer vision techniques. It covers various practical applications and useful reference for coding projects.
Learning OpenCV 4 Computer Vision with Python 3
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Learning OpenCV 4 Computer Vision with Python 3:...
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Modern Computer Vision with PyTorch
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Offers a hands-on approach to solving computer vision problems using PyTorch, a popular deep learning framework. It's well-suited for those who want to apply deep learning to vision tasks using PyTorch.
Modern Computer Vision with PyTorch: A practical...
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Modern Computer Vision with PyTorch: Explore deep...
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Modern Computer Vision with PyTorch: A practical...
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Modern Computer Vision with PyTorch: Explore deep...
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Aqib Chaudhary
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English
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