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  • Learning Functional Programming : Managing Code Complexity by Thinking Functionally
    Learning Functional Programming : Managing Code Complexity by Thinking Functionally

    Learn how to think and write code like a functional programmer.With this practical guide, software developers familiar with object-oriented programming will dive into the core concepts of functional programming and learn how to use both functional and OOP features together on large or complex software projects. Author Jack Widman uses samples from Java, Python, C#, Scala, and JavaScript to help you gain a new perspective and a set of tools for managing the complexity in your problem domain.You'll be able to write code that's simpler, reusable, easier to test and modify, and more consistently correct.This book also shows you how to use patterns from category theory to help bridge the gap between OOP and functional programming. Learn functional programming fundamentals and explore the way functional programmers approach problemsUnderstand how FP differs from object-oriented and imperative programmingUse a set of practical, applicable design patterns that model reality in a functional wayLearn how to incorporate FP and OOP features into software projectsApply functional design patterns appropriately and use them to write correct, robust, and easily modifiable code

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  • Non-Linear Perspectives on Teacher Development : Complexity in Professional Learning and Practice
    Non-Linear Perspectives on Teacher Development : Complexity in Professional Learning and Practice

    Despite the multifaceted complexity of teaching, dominant perspectives conceptualize teacher development in linear, dualistic, transactional, human-centric ways.The authors in this book offer non-linear alternatives by drawing on a continuum of complex perspectives, including CHAT, complexity theory, actor network theory, indigenous studies, rhizomatics, and posthuman/neomaterialisms.The chapters included here illuminate how different ways of thinking can help us better examine how teachers learn (relationally, with human, material, and discursive elements) and offer ways to understand the entangled nature of the relationship between that learning and what emerges in classroom instructional practice.They also present situated illustrations of what those entanglements or assemblages look like in the preservice, induction, and inservice phases, from early childhood to secondary settings, and across multiple continents.Authors provide evidence that research on teacher development should focus on process as much (if not more than) product and show that complexity perspectives can support forward-thinking, assets-based pedagogies.Methodologically, the chapters encourage conceptual creativity and expansion, and support an argument for blurring theory-method and normalising methodological hybridity.Ultimately, this book provides conceptual, theoretical, and methodological tools to understand current educational conditions in late capitalism and imagine otherwise.It was originally published as a special issue of the journal Professional Development in Education.

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  • Leadership Development in Practice : A Complexity Approach
    Leadership Development in Practice : A Complexity Approach

    In an unpredictable world, how do we go about supporting leaders to develop more democratic and inclusive ways of working and living?The second edition of Leadership Development in Practice: A Complexity Approach draws on autoethnographic accounts of experience from practitioners across three continents to explore the leadership development approaches that best support managers to work with uncertainty by taking their experience seriously.It offers an alternative perspective on leadership and organisation for business schools, consultancies, and corporate training functions to adopt in their development of leaders. Additions to this second edition include as follows:A new chapter on creating large group dialogueA more explicit emphasis on what it means to take gender, diversity, and social justice seriouslyA review of the burgeoning interest in complexity perspectives on leadership and leadership development since publication of the first editionThis book is essential reading for leadership and organisational development professionals, researchers, and students.It will also be of interest to managers looking for an approach to leadership development that works with how things are rather than with idealisations of how things ought to be.

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  • Think Complexity : Complexity Science and Computational Modeling
    Think Complexity : Complexity Science and Computational Modeling

    Complexity science uses computation to explore the physical and social sciences.In Think Complexity, you’ll use graphs, cellular automata, and agent-based models to study topics in physics, biology, and economics.Whether you’re an intermediate-level Python programmer or a student of computational modeling, you’ll delve into examples of complex systems through a series of worked examples, exercises, case studies, and easy-to-understand explanations.In this updated second edition, you will: Work with NumPy arrays and SciPy methods, including basic signal processing and Fast Fourier Transform Study abstract models of complex physical systems, including power laws, fractals and pink noise, and Turing machines Get Jupyter notebooks filled with starter code and solutions to help you re-implement and extend original experiments in complexity; and models of computation like Turmites, Turing machines, and cellular automata Explore the philosophy of science, including the nature of scientific laws, theory choice, and realism and instrumentalism Ideal as a text for a course on computational modeling in Python, Think Complexity also helps self-learners gain valuable experience with topics and ideas they might not encounter otherwise.

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  • Is learning programming and software development very challenging?

    Learning programming and software development can be challenging for some people, as it requires logical thinking, problem-solving skills, and attention to detail. However, with dedication, practice, and the right resources, it is definitely achievable. Breaking down complex concepts into smaller, more manageable parts and seeking help from online tutorials, courses, and communities can make the learning process easier and more enjoyable. Ultimately, the level of challenge will vary depending on the individual's background, experience, and learning style.

  • Can complexity be objectively measured?

    Complexity can be objectively measured to some extent, especially in the context of information theory and algorithmic complexity. In information theory, complexity can be measured using metrics such as entropy and Kolmogorov complexity, which provide objective measures of the amount of information or computational resources required to describe a system. However, when it comes to measuring the complexity of real-world systems or phenomena, there is often a subjective element involved, as different observers may prioritize different aspects of complexity. Therefore, while certain aspects of complexity can be objectively measured, the overall assessment of complexity may still involve some degree of subjectivity.

  • What is the complexity of Mergesort?

    The time complexity of Mergesort is O(n log n) in the worst-case scenario, where n is the number of elements in the array. This complexity arises from the fact that Mergesort divides the array into halves recursively and then merges them back together in sorted order. The space complexity of Mergesort is O(n) due to the need for additional space to store the divided subarrays during the sorting process. Overall, Mergesort is an efficient sorting algorithm that performs well on large datasets.

  • How can one get rid of complexity?

    One can get rid of complexity by breaking down the problem or situation into smaller, more manageable parts. This can help to identify the root causes of the complexity and address them individually. Additionally, simplifying processes, communication, and decision-making can help reduce complexity. It is also important to prioritize and focus on the most important aspects, while letting go of unnecessary details. Finally, seeking input and collaboration from others can provide fresh perspectives and help to streamline complex situations.

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  • Think Complexity: Complexity Science and Computational Modeling
    Think Complexity: Complexity Science and Computational Modeling

    Think Complexity: Complexity Science and Computational Modeling

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  • Simply Complexity : A Clear Guide to Complexity Theory
    Simply Complexity : A Clear Guide to Complexity Theory

    What do traffic jams, stock market crashes, and wars have in common?They are all explained using complexity, an unsolved puzzle that many researchers believe is the key to predicting – and ultimately solving—everything from terrorist attacks and pandemic viruses right down to rush hour traffic congestion. Complexity is considered by many to be the single most important scientific development since general relativity and it promises to make sense of no less than the very heart of the Universe.Using it, scientists can find order emerging from seemingly random interactions of all kinds, from something as simple as flipping coins through to more challenging problems such as the patterns in modern jazz, the growth of cancer tumours, and predicting shopping habits.

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  • Ecological Complexity
    Ecological Complexity

    Complexity has received substantial attention from scientists and philosophers alike.There are numerous, often conflicting, accounts of how complexity should be defined and how it should be measured.Much less attention has been paid to the epistemic implications of complexity, especially in Ecology.How does the complex nature of ecological systems affect ecologists' ability to study them?This Element argues that ecological systems are complex in a rather special way: they are causally heterogeneous.Not only are they made up of many interacting parts, but their behaviour is variable across space or time.Causal heterogeneity is responsible for many of the epistemic difficulties that ecologists face, especially when making generalisations and predictions.Luckily, ecologists have the tools to overcome these difficulties, though these tools have historically been considered suspect by philosophers of science.The author presents an updated philosophical account with an optimistic outlook of the methods and status of ecological research.

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  • Programming Machine Learning : From Coding to Deep Learning
    Programming Machine Learning : From Coding to Deep Learning

    You've decided to tackle machine learning - because you're job hunting, embarking on a new project, or just think self-driving cars are cool.But where to start? It's easy to be intimidated, even as a software developer.The good news is that it doesn't have to be that hard.Master machine learning by writing code one line at a time, from simple learning programs all the way to a true deep learning system.Tackle the hard topics by breaking them down so they're easier to understand, and build your confidence by getting your hands dirty.Peel away the obscurities of machine learning, starting from scratch and going all the way to deep learning.Machine learning can be intimidating, with its reliance on math and algorithms that most programmers don't encounter in their regular work.Take a hands-on approach, writing the Python code yourself, without any libraries to obscure what's really going on.Iterate on your design, and add layers of complexity as you go.Build an image recognition application from scratch with supervised learning.Predict the future with linear regression. Dive into gradient descent, a fundamental algorithm that drives most of machine learning.Create perceptrons to classify data. Build neural networks to tackle more complex and sophisticated data sets.Train and refine those networks with backpropagation and batching.Layer the neural networks, eliminate overfitting, and add convolution to transform your neural network into a true deep learning system.Start from the beginning and code your way to machine learning mastery.What You Need: The examples in this book are written in Python, but don't worry if you don't know this language: you'll pick up all the Python you need very quickly.Apart from that, you'll only need your computer, and your code-adept brain.

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  • What is the complexity of composing two functions?

    Composing two functions has a complexity of O(1), as it involves simply applying one function to the output of the other. The time complexity does not depend on the size of the input, as the functions are applied sequentially. Therefore, the complexity of composing two functions is constant and does not increase with the size of the input.

  • What are the Landau symbols for the time complexity?

    The Landau symbols for time complexity are commonly used to describe the upper and lower bounds of an algorithm's running time. The most commonly used Landau symbols for time complexity are O (big O) for upper bound, Ω (big omega) for lower bound, and Θ (big theta) for both upper and lower bounds. These symbols are used to express the growth rate of an algorithm's running time in terms of the input size. For example, if an algorithm has a time complexity of O(n^2), it means that the running time of the algorithm grows no faster than n^2 as the input size increases.

  • What are the Big O notations for time complexity?

    The Big O notations for time complexity are used to describe the upper bound on the growth rate of an algorithm's running time as the input size increases. Some common Big O notations include O(1) for constant time complexity, O(log n) for logarithmic time complexity, O(n) for linear time complexity, O(n^2) for quadratic time complexity, and O(2^n) for exponential time complexity. These notations help in analyzing and comparing the efficiency of different algorithms.

  • How do you determine the complexity of a function?

    The complexity of a function can be determined by analyzing its time and space requirements. This can be done by examining the number of operations the function performs and the amount of memory it uses. Additionally, the complexity can be influenced by the size of the input data and the efficiency of the algorithm used in the function. By considering these factors, one can determine the complexity of a function, which is often expressed using Big O notation.

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