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Paperback. Zustand: new. Paperback. What You Will Learn in This BookMaster the Mathematical Foundations: Go beyond theory to implement the core mathematical operations of linear algebra, calculus, and probability that form the bedrock of all modern neural networks, using Python and NumPy.Build a Neural Network From Scratch: Gain an intuitive understanding of how models learn by constructing a simple neural network from first principles, giving you a solid grasp of concepts like activation functions, loss, and backpropagation.Engineer a Complete Data Pipeline: Learn the critical and often overlooked steps of sourcing, cleaning, and pre-processing the massive text datasets that fuel LLMs, while navigating the ethical considerations of bias and fairness.Implement a Subword Tokenizer: Solve the "vocabulary problem" by building a Byte-Pair Encoding (BPE) tokenizer from scratch, learning precisely how raw text is converted into a format that models can understand.Construct a Transformer Block, Piece by Piece: Deconstruct the "black box" of the Transformer by implementing its core components in code. You will build the scaled dot-product attention mechanism, expand it to multi-head attention, and assemble a complete, functional Transformer block.Differentiate and Understand Key Architectures: Clearly grasp the differences and use cases for the foundational LLM designs, including encoder-only (like BERT), decoder-only (like GPT), and encoder-decoder models (like T5).Write a Full Pre-training Loop: Move from theory to practice by writing the complete code to pre-train a small-scale GPT-style model from scratch, including setting up the language modeling objective and monitoring loss curves.Understand the Economics and Scale of Training: Learn the "scaling laws" that govern the relationship between model size, dataset size, and performance, and understand the hardware and distributed computing strategies (e.g., model parallelism, ZeRO) required for training at scale.Adapt Pre-trained Models with Fine-Tuning: Learn to take a powerful, general-purpose LLM and adapt it for specific, real-world tasks using techniques like instruction tuning and standard fine-tuning.Grasp Advanced Alignment and Evaluation Techniques: Gain a conceptual understanding of how Reinforcement Learning from Human Feedback (RLHF) aligns models with human intent, and learn how to properly evaluate model quality using benchmarks like MMLU and SuperGLUE.Explore State-of-the-Art and Future Architectures: Survey the cutting edge of LLM research, including methods for model efficiency (quantization, Mixture of Experts), the shift to multimodality (incorporating images and audio), and the rise of agentic AI systems. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

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Paperback. Zustand: new. Paperback. What You Will Learn in This BookNavigate the Excel Environment: Gain proficiency in understanding and utilizing the core components of Excel, including the Ribbon, Quick Access Toolbar, and Formula Bar, to efficiently manage your data.Master Worksheet Fundamentals: Learn to effectively select and manipulate cells, rows, and columns, and accurately input various data types such as text, numbers, and dates.Enhance Data Presentation: Develop skills in basic formatting to improve the readability and visual appeal of your spreadsheets, including applying fonts, colors, borders, and text alignment.Perform Calculations with Formulas: Understand the foundational concepts of Excel formulas, enabling you to perform essential arithmetic operations and leverage cell references for dynamic calculations.Utilize Essential Functions: Become adept at using crucial built-in Excel functions like SUM, AVERAGE, MAX, MIN, COUNT, and COUNTA to quickly analyze and summarize your data.Implement Logical Decision-Making: Learn to apply the IF function to introduce conditional logic into your spreadsheets, automating decisions and outcomes based on specified criteria.Organize and Manage Data Efficiently: Discover techniques for sorting, filtering, and searching your data, along with using features like "Find and Replace" and "Freezing Panes" to streamline your workflow.Create Structured Tables: Understand the benefits of formatting data as tables, including automatic formatting, simplified sorting, and the use of structured references for improved data management.Visualize Data with Charts: Gain the ability to create and customize basic charts (pie, bar, line) to effectively represent your data visually and communicate insights.Prepare and Share Your Work: Learn how to set print areas, adjust page orientation, and export your Excel work to PDF, ensuring your spreadsheets are ready for professional sharing and printing. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Paperback. Zustand: new. Paperback. What You Will Learn in This BookGrasp the Fundamentals of Artificial Intelligence: Understand what AI really is-beyond science fiction-and how it mimics certain aspects of human intelligence to perform useful tasks.Differentiate AI from Related Technologies: Learn the key distinctions between artificial intelligence, machine learning, and deep learning through simple explanations and real-life analogies.Discover the History and Evolution of AI: Trace the journey of AI from early theoretical ideas to today's practical applications, including major milestones and turning points in its development.Recognize the Impact of AI in Everyday Life: See how AI is already woven into your daily experiences-from smartphones and social media to online shopping and smart homes.Understand How AI Learns from Data: Explore how data powers AI systems, why the quality and relevance of data matter, and what it means for a machine to "learn" from experience.Explore Machine Learning in Plain Language: Get introduced to core machine learning approaches-supervised, unsupervised, and reinforcement learning-without any need for coding or math.Demystify Neural Networks and Deep Learning: Learn how neural networks are inspired by the human brain and how deep learning enables machines to recognize complex patterns in data.Appreciate Key AI Capabilities: Understand how AI enables computers to interpret images (computer vision), understand language (natural language processing), and personalize experiences (recommendation systems).Gain Awareness of AI in Business and Industry: Discover how AI is revolutionizing fields like healthcare, finance, manufacturing, agriculture, and more through automation and smarter decision-making.Evaluate the Benefits and Challenges of AI: Understand both the promises of AI-such as solving real-world problems-and the limitations, such as lack of common sense and difficulty operating outside known data.Identify and Challenge Common AI Myths: Learn to separate fact from fiction regarding AI's capabilities, accuracy, and relationship with jobs and robots.Consider the Ethical Implications of AI: Reflect on important issues such as fairness, bias, privacy, accountability, and the ethical responsibilities of those who design and deploy AI systems.Understand AI's Role in the Changing Job Market: Explore how AI is reshaping the workforce, creating new opportunities, and emphasizing the importance of adaptability and lifelong learning.Recognize the Limitations of AI Systems: Learn what current AI cannot do well, such as understanding deep context, applying common sense, or exhibiting true creativity or empathy.Explore Simple, Hands-On AI Experiences: Discover accessible ways to interact with AI tools and demos online-no programming required-so you can see AI in action for yourself.Learn How to Keep Up with the AI Revolution: Get curated suggestions for beginner-friendly courses, books, podcasts, and online communities to help you stay informed and engaged with AI developments.Become Confident in AI Conversations: Build a solid foundation that empowers you to understand, question, and discuss AI confidently-even with more experienced peers or professionals.Develop AI Literacy for the Modern World: Walk away with the essential knowledge needed to navigate an AI-powered society thoughtfully and responsibly.Embrace a Lifelong Curiosity About AI: Gain not only understanding, but the con Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Paperback. Zustand: new. Paperback. You'll Learn: Understand the Core Concepts of AI: Grasp the fundamental definitions of Artificial Intelligence, Machine Learning, and Deep Learning, and understand the historical context and different types of AI (ANI, AGI, ASI).Set Up Your AI Development Environment: Learn to install and configure essential tools like Python, Anaconda, and key libraries such as NumPy, Pandas, and Matplotlib to create your personal "AI workbench" for hands-on practice.Master the Role of Data in AI: Discover why data is the foundation of AI systems, learn to handle different data types, and gain practical skills in loading, exploring, and visualizing datasets using the Pandas library.Distinguish Different AI Learning Paradigms: Differentiate between rule-based systems and machine learning, and gain a clear understanding of the three core machine learning paradigms: supervised, unsupervised, and reinforcement learning.Build and Evaluate Predictive Models: Implement and train classic supervised learning models like Linear Regression for predicting numbers and Logistic Regression and Decision Trees for classification tasks using the Scikit-learn library.Discover Patterns in Unlabeled Data: Learn the principles of unsupervised learning and apply the K-Means clustering algorithm to group similar data points and find hidden structures within datasets.Assess and Improve Model Performance: Go beyond basic accuracy by learning about crucial evaluation metrics like the Confusion Matrix, Precision, Recall, and F1-Score. You'll also learn techniques like cross-validation and basic hyperparameter tuning to build more robust models.Build and Train Simple Neural Networks: Gain an intuitive understanding of how neural networks mimic the brain, learning about perceptrons, layers, and neurons. You'll then use a powerful library like Keras to build and train your first neural network classifier.Explore Key AI Applications: Get a foundational overview of specialized AI fields like Computer Vision (enabling machines to "see") and Natural Language Processing (enabling machines to understand language), including a brief introduction to Convolutional Neural Networks (CNNs).Understand the Ethical Implications of AI: Critically examine important considerations like algorithmic bias, privacy, and fairness in AI systems, and explore the future societal impact and career opportunities in the field. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Paperback. Zustand: new. Paperback. What You Will Learn in This BookTranslate Core AI Concepts into a Clinical Context: Move beyond the jargon to understand the practical principles of machine learning, deep learning, natural language processing (NLP), and generative AI, and how they apply to real-world medical problems.Master the Fundamentals of Medical Data and Ethics: Learn why high-quality, representative data is the lifeblood of any reliable AI system and grasp the critical ethical frameworks for addressing algorithmic bias, ensuring patient privacy, and establishing accountability.Evaluate AI Tools with Clinical Rigor: Acquire the skills to critically appraise a medical AI study or product. You will learn to look beyond simple accuracy and assess performance using key metrics like sensitivity, specificity, and AUROC, and understand the importance of prospective clinical validation.Understand AI's Impact Across Medical Specialties: Gain a comprehensive overview of how AI is being applied in diagnostics (radiology, pathology, genomics), therapeutics (oncology, surgery), and patient management (predictive risk scoring, chronic disease monitoring).Integrate AI Insights into Your Decision-Making: Learn how AI-generated predictions and recommendations are integrated into clinical workflows through the EHR and how to interpret them effectively to augment, not replace, your own clinical judgment.Identify Hallmarks of Safe and Effective Implementation: Recognize the difference between a research algorithm and a well-integrated clinical tool, focusing on human-centered design, workflow integration, and post-deployment monitoring.Articulate the Evolving Role of the Modern Clinician: Define how your role will be enhanced by AI, shifting focus from information recall to the uniquely human skills of complex problem-solving, sense-making, patient communication, and empathy.Anticipate the Future of Augmented Medicine: Look beyond current applications to understand the next wave of innovation, including privacy-preserving federated learning, causal AI, and the concept of patient digital twins. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Paperback. Zustand: new. Paperback. What You Will Learn in This BookMaster the Mathematical Foundations: Go beyond theory to implement the core mathematical operations of linear algebra, calculus, and probability that form the bedrock of all modern neural networks, using Python and NumPy.Build a Neural Network From Scratch: Gain an intuitive understanding of how models learn by constructing a simple neural network from first principles, giving you a solid grasp of concepts like activation functions, loss, and backpropagation.Engineer a Complete Data Pipeline: Learn the critical and often overlooked steps of sourcing, cleaning, and pre-processing the massive text datasets that fuel LLMs, while navigating the ethical considerations of bias and fairness.Implement a Subword Tokenizer: Solve the "vocabulary problem" by building a Byte-Pair Encoding (BPE) tokenizer from scratch, learning precisely how raw text is converted into a format that models can understand.Construct a Transformer Block, Piece by Piece: Deconstruct the "black box" of the Transformer by implementing its core components in code. You will build the scaled dot-product attention mechanism, expand it to multi-head attention, and assemble a complete, functional Transformer block.Differentiate and Understand Key Architectures: Clearly grasp the differences and use cases for the foundational LLM designs, including encoder-only (like BERT), decoder-only (like GPT), and encoder-decoder models (like T5).Write a Full Pre-training Loop: Move from theory to practice by writing the complete code to pre-train a small-scale GPT-style model from scratch, including setting up the language modeling objective and monitoring loss curves.Understand the Economics and Scale of Training: Learn the "scaling laws" that govern the relationship between model size, dataset size, and performance, and understand the hardware and distributed computing strategies (e.g., model parallelism, ZeRO) required for training at scale.Adapt Pre-trained Models with Fine-Tuning: Learn to take a powerful, general-purpose LLM and adapt it for specific, real-world tasks using techniques like instruction tuning and standard fine-tuning.Grasp Advanced Alignment and Evaluation Techniques: Gain a conceptual understanding of how Reinforcement Learning from Human Feedback (RLHF) aligns models with human intent, and learn how to properly evaluate model quality using benchmarks like MMLU and SuperGLUE.Explore State-of-the-Art and Future Architectures: Survey the cutting edge of LLM research, including methods for model efficiency (quantization, Mixture of Experts), the shift to multimodality (incorporating images and audio), and the rise of agentic AI systems. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Paperback. Zustand: new. Paperback. What You Will Learn in This BookGain a foundational understanding of Artificial Intelligence (AI), including its core concepts, historical development, and the distinct characteristics of various AI technologies like Machine Learning, Deep Learning, Natural Language Processing (NLP), and Computer Vision.Identify and evaluate practical AI opportunities across diverse industries such as healthcare, finance, retail, and manufacturing, understanding how AI can solve common business problems and enhance daily life.Develop a strategic roadmap for AI implementation within your organization, learning how to align AI initiatives with business objectives, assess readiness, and prioritize projects for phased adoption.Master the principles of data management for AI, encompassing effective data collection, crucial preprocessing techniques, robust data storage solutions, and best practices for data security and privacy.Acquire the knowledge to build, deploy, and maintain AI models, including selecting appropriate tools and technologies, navigating the model development workflow, integrating AI into existing systems, and ensuring continuous performance monitoring and iteration.Analyze real-world AI case studies to understand successful applications in areas like customer service enhancement, predictive maintenance, personalized marketing, and fraud detection, extracting valuable lessons for your own initiatives.Discover how to leverage AI to enhance your personal life, from maximizing smart assistants and wearable devices to utilizing AI for health, wellness, and personalized learning.Prepare for the future of AI, understanding emerging trends like Explainable AI (XAI) and Generative AI, adapting to the evolving workforce, and navigating the ethical and regulatory landscape of artificial intelligence.Develop the critical thinking skills necessary to engage with AI responsibly, recognizing AI-generated content and advocating for ethical AI practices. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Paperback. Zustand: new. Paperback. What You Will Learn in This BookMaster core data structures and algorithms: Gain a deep understanding of essential concepts like arrays, linked lists, trees, graphs, heaps, sorting, searching, dynamic programming, and more, crucial for any software engineering role.Develop effective problem-solving strategies: Learn a systematic 5-step framework to approach any coding problem, identify common patterns, and optimize your solutions for both time and space efficiency.Solve curated coding interview questions: Get hands-on practice with a wide range of problems categorized by topic and difficulty, complete with detailed solutions, complexity analysis, and alternative approaches.Navigate the technical interview landscape: Understand the different types of interviews (phone screens, coding challenges, system design, behavioral) and what top tech companies like FAANG expect from candidates.Build a robust study plan: Discover how to create a personalized preparation timeline (1-month, 3-month, or 6-month) and effectively balance learning, practice, and mock interviews.Cultivate an interview-ready mindset: Overcome anxiety, build confidence, and learn to effectively communicate your thought process to interviewers, even when facing challenging problems.Prepare for behavioral interviews: Master the STAR method to confidently answer common behavioral questions, showcasing your leadership, teamwork, and problem-solving skills.Grasp system design fundamentals: Get introduced to key system design concepts like scalability, reliability, and availability, and learn to design basic systems for common interview scenarios.Optimize your code with advanced techniques: Explore methods for reducing time complexity, space optimization strategies, and common pitfalls to avoid in your solutions.Execute a successful interview day: Learn crucial pre-interview preparations, what to expect, and how to handle the logistics for both in-person and virtual interviews.Strategize post-interview actions and career growth: Understand how to follow up, evaluate job offers, negotiate salary, and lay the groundwork for continuous learning and long-term success in your software engineering career. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Paperback. Zustand: new. Paperback. Master the Fundamentals of Machine Learning: Grasp the core concepts of supervised, unsupervised, and reinforcement learning, and understand the complete machine learning workflow from problem definition to deployment.Set Up a Robust Python Environment: Learn how to install and configure essential libraries like Pandas, Scikit-Learn, NumPy, and Matplotlib in a virtual environment for your machine learning projects.Become Proficient in Data Handling with Pandas: Develop strong skills in loading, cleaning, preparing, and exploring tabular data using Pandas, including handling missing values, duplicates, and different data types.Visualize and Analyze Your Data: Use Matplotlib and Seaborn to create insightful visualizations like histograms, scatter plots, and heatmaps to understand data distributions and relationships.Implement Core Supervised Learning Algorithms: Build and train practical models for both classification (e.g., Logistic Regression, Decision Trees, Random Forests, KNN) and regression (e.g., Linear Regression).Evaluate Your Models Effectively: Go beyond simple accuracy by learning to use crucial evaluation metrics like the Confusion Matrix, Precision, Recall, F1-Score, RMSE, and R-squared to assess your model's performance.Apply Unsupervised Learning Techniques: Discover how to find hidden patterns in unlabeled data using clustering (K-Means) and simplify complex datasets with dimensionality reduction (PCA).Optimize Your Machine Learning Workflow: Learn critical preprocessing steps like feature scaling and categorical encoding, and use Scikit-Learn Pipelines to streamline your model-building process.Tune Models for Better Performance: Understand the difference between parameters and hyperparameters, and use Grid Search to systematically find the best settings for your models.Build Two End-to-End Projects: Apply all the skills you've learned to build and evaluate two complete, real-world projects: a classification model to predict Titanic survival and a regression model to predict house prices. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

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Zustand: New. Print on Demand.

Zustand: New. Print on Demand.

Zustand: New. Print on Demand.