[FREE EBOOKS] AI Explained, Excel Insights & Four More Best Selling Titles - Related to &, a, predictions, four, ebooks]

[FREE EBOOKS] AI Explained, Excel Insights & Four More Best Selling Titles

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AI Explained: Uncovering the Reality, Risks, and Rewards of Artificial Intelligence ($[website] Value) FREE for a Limited Time This book leads readers on a comprehensive journey through artificial intelligence, beginning with an introduction to its foundational concepts. Early on, readers are guided through what AI is and why it’s becoming a transformative force in today’s world. They’ll then explore the technical processes behind AI creation, learning how data and algorithms power intelligent systems, making complex technology accessible and understandable. Further on, the book delves into practical applications and ethical considerations. Readers discover the positive ways AI is improving our lives, from breakthroughs in healthcare to innovations in everyday industries. Following this, it presents a balanced view of AI’s potential risks, discussing the ethical concerns and societal challenges that arise with misuse, encouraging thoughtful reflection on AI’s impact. Towards the end, the book tackles critical future challenges and solutions. It discusses the threat of rogue AI behaviors and highlights strategies to prevent AI from acting unpredictably. The final section offers an optimistic outlook on the responsible development of AI, encouraging readers to envision a future where AI can drive positive change. By journey’s end, readers will feel equipped with a nuanced understanding of AI, its real-world significance, and its ethical implications. Get it FREE!

Excel Insights: A Microsoft MVP Guide to the Best Parts of Excel ($[website] FREE for a Limited Time Dive into the world of advanced Excel techniques designed to elevate your data analysis skills. Start with mastering custom number formatting, efficient data entry, and powerful formulas like INDEX MATCH. Explore Excel’s evolving functions, including dynamic arrays and new data types, ensuring you stay at the forefront of the latest tools. The course then guides you through creating impactful charts for presentations and advanced filtering techniques. You’ll also discover the transformative power of Power Query, allowing you to manipulate and combine data with ease. With chapters on financial modeling and creative Excel model development, you’ll learn to solve complex problems and develop innovative solutions. Finally, the course introduces you to VBA, teaching you how to automate tasks and create custom worksheet functions, equipping you with the skills to enhance your workflows. By the end of the course, you’ll have a robust understanding of Excel’s advanced functions, empowering you to handle any data challenge with confidence and creativity. Get it FREE!

Minds Over Meetings: A Personal Perspective on Wellness in the Workplace ($[website] Value) FREE for a Limited Time How to manage your mental health at work and create supportive environments for teams you lead. Drawing on popular influencer Kody Green’s inspirational story as a diagnosed person with schizophrenia who succeeded in finding and maintaining a job across diverse industry sectors including production, distribution, office settings, and management,Minds Over Meetings: A Personal Perspective on Wellness in the Workplaceprovides practical strategies for fostering an understanding, empathetic, and supportive and inclusive work environment as well as self-care and coping strategies to help all professionals manage their mental health advanced at work. Backed by the latest research findings in the field and emphasizing the vital role of open and honest communication, Green explores topics including: Finding available accommodations through employee assistance programs (EAP) and the Family and Medical Leave Act (FMLA). Retaining talent in the workplace by promoting education and fighting stigma. Building relationships between friends and colleagues to strengthen teamwork. Minds Over Meetings: A Personal Perspective on Wellness in the Workplaceearns a well-deserved spot on the bookshelves of all individuals seeking to thrive in the workplace through effective management of their mental health, along with all managers and business leaders seeking to create an open and supportive environment to improve worker wellbeing. Get it FREE!

Flex: Smart Strategies to Build A Successful Career Around Your Life ($[website] Value) FREE for a Limited Time Build A Career That Works For You: Smart Strategies For Career Success. Flex: Smart Strategies to Build a Successful Career Around Your Life, by Elizabeth Willetts is a dynamic guide for anyone wanting to build a successful career – on their terms! This book, written by a seasoned recruitment entrepreneur and career coach, offers a wealth of strategies, real-life success stories, and expert advice to empower you to navigate the complexities of the modern workplace and advance your career, even when life throws you curveballs. Whether you’re looking to pivot to a new field, climb the corporate ladder, or integrate more flexibility into your current role, Willetts’ insightful and actionable advice is designed to help you maintain momentum and advance your career in a way that works for you. Through captivating case studies of women who’ve successfully navigated the modern workplace this guide highlights the non-linear nature of career progression and demonstrates how to assess and use your skills effectively, overcome common obstacles, and make bold moves with confidence. You’ll find: Strategies to help you gain clarity on your career and identify a job you’ll love – great if you want to switch careers. Advice forrebooting your career after a lengthy maternity or family leave. Actionable tips on writing your CV, creating a LinkedIn profile and nailing job interviews. Techniques tohelp you make a flexible working request your employer is more likely to say YES to. Real-world stories from women who havecreated a career they’ve adored – whatever life has thrown at them. Flex: Smart Strategies to Build a Successful Career Around Your Lifeby Elizabeth Willetts is more than just a career manual; it’s your personal blueprint for crafting a fulfilling career that truly fits your life. Packed with actionable strategies and inspiring stories, this book is the go-to resource for anyone ready to tackle the challenges of the modern workplace with confidence. Whether you’re pivoting careers, climbing the corporate ladder, or seeking that perfect work-life balance,Flexoffers you the tools you need to thrive on your terms. Elizabeth Willetts is the founder of Investing in Women, an organisation that helps businesses reach candidates looking for flexible and part-time work. She is passionate about educating businesses on the benefits of flexible working and attracting top female talent. She also provides one-to-one coaching to job seekers looking to advance their careers. Get ready to transform the way you approach your career withFlex, and start building the successful professional life you deserve. Get it FREE!

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Psychological Safety as a Competitive Edge

Psychological safety isn’t about fluffy “niceness” — it is the foundation of agile teams that innovate, adapt, and deliver.

When teams fearlessly debate ideas, admit mistakes, challenge norms, and find ways to make progress, they can outperform most competitors. Yet, many organizations knowingly or unknowingly sabotage psychological safety — a short-sighted and dangerous attitude in a time when knowledge is no longer the moat it used to be. Read on to learn how to keep your competitive edge.

The Misinterpretation of Psychological Safety.

I’ve noticed a troubling trend: While “psychological safety” is increasingly embraced as an idea, it is widely misunderstood. Too often, it is conflated with comfort, an always-pleasant environment where hard conversations are avoided and consensus is prized over candor. This confusion isn’t just conceptually muddy; it actively undermines the very benefits that psychological safety is meant to enable.

So, let’s set the record straight. Actual psychological safety is not about putting artificial harmony over healthy conflict. It is not a “feel-good” abstraction or a license for unfiltered venting. At its core, psychological safety means creating an environment of mutual trust and respect that enables candid communication, calculated risk-taking, and the open sharing of ideas — even and especially when those ideas challenge the status quo. (There is a reason why three out of five Scrum Values — openness, respect, and courage — foster an environment where psychological safety flourishes.).

When Amy Edmondson of Harvard first introduced the term, she defined it as a “shared belief held by members of a team that the team is safe for interpersonal risk-taking.” Digging deeper, she clarified that psychological safety is about giving candid feedback, openly admitting mistakes, and learning from each other.

Note the key elements here: candor, risk-taking, and learning. Psychological safety doesn’t mean we shy away from hard truths or sweep tensions under the rug. Instead, it gives us the relational foundation to surface those tensions and transform them into growth. It is the baseline of trust that allows us to be vulnerable with each other and do our best work together.

When teams misunderstand psychological safety, they tend to fall into one of two dysfunctional patterns:

Artificial harmony. Conflict is avoided at all costs. Dissenting opinions are softened or withheld to maintain an illusion of agreement. On the surface, things seem rosy – but underneath, resentments fester, mediocre ideas slip through unchecked, and the elephants in the room live happily ever after. False bravado. The team mistakes psychological safety for an excuse for unfiltered “brutal honesty.” Extroverts voice critiques without care for their impact, bullying the introverts, thus eroding the trust and mutual respect that proper psychological safety depends on.

Both failure modes arise from the same fundamental misunderstanding: psychological safety prioritizes comfort over candor or honesty over care. In reality, true psychological safety dismisses these false dilemmas. It involves discovering how to engage in direct, even challenging, conversations in a way that enhances rather than undermines relationships and trust.

This is where the concept of “radical candor” comes in. Coined by Kim Scott, radical candor means giving frank, actionable feedback while showing that you care about the person on the receiving end. It is a way of marrying honesty and empathy, recognizing that truly constructive truthtelling requires a bedrock of interpersonal trust.

This combination of directness and care is at the heart of psychological safety, and it is utterly essential for agile teams. Agile’s promise of responsiveness to change, creative problem-solving, and harnessing collective intelligence depends on team members’ willingness to speak up, take smart risks, and challenge established ways of thinking. This requires an environment where people feel safe not just supporting each other but productively sparring.

Consider the Daily Scrum or stand-up, a hallmark Agile event. The whole point is for team members to surface obstacles, ask for help, and realign around shifting goals. But that is hard to do if people feel pressured to “seem fine” or avoid rocking the boat. Actual psychological safety creates space for people to say, “I’m stuck and need help,” “I don’t know,” or “I disagree with that approach” without fear of judgment or retribution.

Or take the Retrospective, which is also dedicated to surfacing and learning from failure. (Of course, we also learn from successes.) If people think that talking openly about mistakes will be held against them, they’ll naturally ignore, massage, or sanitize what happened. (This is also the main reason a team should not include members with a reporting hierarchy between them.) Psychological safety shifts that calculus. It says, “We’re in this together, win or lose,” which paradoxically gives teams the courage to scrutinize their losses more rigorously to learn from failure.

Zoom out, and you’ll see psychological safety running like a golden thread through all the core Agile principles: “individuals and interactions over processes and tools,” “customer collaboration over contract negotiation,” and “responding to change over following a plan.” Enacting these values in the wild requires team environments of enormous interpersonal trust and openness. That is the singular work of psychological safety — and it is not about being “soft” or avoiding hard things — quite the opposite. (Think Scrum Values; see above).

The research displays that psychological safety isn’t just a kumbaya aspiration — it is a performance multiplier. Google’s comprehensive Project Aristotle, which studied hundreds of teams, found that psychological safety was the single most significant predictor of team effectiveness. Teams with high psychological safety consistently delivered superior results, learned faster, and navigated change more nimbly. They also tended to have more fun in the process.

Moreover, teams with high psychological safety are more likely to create value for people, contribute to the bottom line, retain top talent, and generate breakthrough innovations — the ultimate competitive advantage. In other words, psychological safety isn’t a nice-to-have; it is a strategic necessity and a profitable asset.

So, how do we cultivate authentic psychological safety in our teams? A few key practices:

Frame the work as learning. Position every project as an experiment and every failure as vital data. Publicly celebrate smart risks, regardless of the outcome. Make it explicit that missteps aren’t just tolerated—they’re eagerly mined for gold. Model fallibility. As a leader, openly acknowledge your own mistakes and growth edges. Share stories of times you messed up and what you learned. Demonstrating vulnerability is a powerful signal that it is safe for others to let their guards down, too. (Failure nights are a great way of spreading this message.) Ritualize reflection. Take Retrospectives seriously to candidly reflect on what’s working and what’s not. Using structured prompts and protocols helps equalize airtime so that all voices are heard (Think, for example, of Liberating Structures’ Conversation Café). The more habitual reflection becomes, the more psychological safety will deepen. If necessary, consider employing anonymous surveys to give everyone a voice. Teach tactful candor. Train the team in frameworks for giving constructive feedback, such as the SBI (situation-behavior-impact) model or non-violent communication. Emphasize that delivering hard truths with clarity and care is the ultimate sign of respect — for the individual and the shared work. Make space for being a mensch. Kickoff meetings with quick personal check-ins. Encourage people to bring their whole messy, wonderful selves to work. Share gratitude, crack jokes, and celebrate the small wins. Psychological safety isn’t sterile; it is liberatingly human.

Most importantly, recognize that building and sustaining psychological safety is an ongoing practice — not a one-and-done box to check. It requires a daily recommitment to choosing courage over comfort, purpose over posturing, and the hard and necessary truths over the easy fake-outs.

Like any meaningful discipline, it is not always comfortable. Working and relating in a psychologically safe way can sometimes feel bumpy and exposing. We may give clumsy feedback, stumble into miscommunications and hurt feelings, and face hard facts we’d rather avoid.

But that is the point: genuine psychological safety transforms uncomfortable moments from threats into opportunities. It allows us to keep showing up and learning together, especially when we feel most vulnerable. It fosters a team culture that is resilient enough to endure the necessary friction of honest collaboration and turns them into something impactful and clarifying.

That is the promise of psychological safety. More than just another buzzword or checklist item, it is about cultivating the soil for enduringly healthy and productive human relationships at work. It is about creating the conditions that support us in growing into them together. Put simply, without psychological safety, Agile can’t deliver on its potential. With psychological safety, Agile can indeed come alive as a force for creativity, innovation, and, yes, joy at work.

Start by looking honestly at your team: How safe do people feel taking risks and telling hard truths? What is the one conversation, the one elephant in the room, you have been avoiding that might unlock the next level of performance and trust? Challenge yourself to initiate that talk next week — and watch the ripple effects unfold.

Embracing this authentic version of psychological safety won’t be a walk in the park. You and your team will face uncomfortable moments of friction and vulnerability. Team members may drop out, feeling too stressed about it. But leaning into that discomfort is precisely how you will unleash your true potential. Psychological safety is about building a resilient team to navigate tough challenges and have difficult conversations because you know you have each other’s backs. That foundation will allow you to embrace agility as it is meant to be.

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Loss Functions: The Key to Improving AI Predictions

We can put an actual number on it. In machine learning, a loss function tracks the degree of error in the output from an AI model by quantifying the difference or the loss between a predicted value and the actual value. If the model’s predictions are accurate, the difference between these two numbers — the loss — is small. If the predictions are inaccurate, the loss is larger.

For example, a colleague built an AI model to forecast how many views his videos would receive on YouTube. The model was fed YouTube titles and forecasted the number of views the video would receive in its first week. When comparing the model’s forecasts to the actual number of views, the predictions were not very accurate. The model predicted that the cold brew video would bomb and that the pour-over guide video would be a hit, but this wasn’t the case. This is a hard problem to solve, and loss functions can help improve the model.

Loss functions define how well a model is performing mathematically. By calculating loss, we can adjust model parameters to see if the loss increases (worsens) or decreases (improves). A machine learning model is considered sufficiently trained when the loss is minimized below a predefined threshold. At a high level, loss functions fall into two categories: regression loss functions and classification loss functions.

Regression loss functions measure errors in continuous value predictions, such as house prices, temperature, or YouTube video views. These functions must be sensitive to both whether the forecast is correct and the degree to which it diverges from the ground truth.

The most common regression loss function is Mean Squared Error (MSE), calculated as the average squared difference between the predicted and true values across all training examples.

Squaring the error gives large mistakes a disproportionately heavy impact on overall loss, strongly penalizing outliers.

MAE, on the other hand, measures the average absolute difference between the predicted and actual values. Unlike MSE, MAE does not square the errors, making it less sensitive to outliers.

Choosing between MSE and MAE depends on the nature of the data. If there are a few extreme outliers, such as temperature ranges in July in the southern [website], MSE is a good choice since it heavily penalizes large deviations. However, if the data contains outliers that should not overly influence the model, such as occasional surges in product sales, MAE is a advanced option.

Hubber Loss provides a compromise between MSE and MAE, acting like MSE for small errors and MAE for large errors. This makes it useful when penalizing large errors is necessary, but not too harshly.

For the YouTube example, the MAE value summed up to an average prediction error of 16,000 views per video. The MSE loss function skyrocketed to over 400 million due to the squaring of large errors. The Huber loss also indicated poor predictions but provided a more balanced perspective, penalizing large errors less severely than MSE. However, these loss values are only meaningful when used to adjust model parameters and observe improvements.

Classification loss functions, in contrast to regression loss functions, measure accuracy in categorical predictions. These functions assess how well predicted probabilities or labels match actual categories, such as determining whether an email is spam or not.

Cross-entropy is the most widely used classification loss function, measuring how uncertain a model’s predictions are compared to actual outcomes. Entropy, in this context, represents uncertainty — a coin flip has low entropy, while rolling a six-sided die has higher entropy. Cross-entropy loss compares the certainty of the model’s predictions to the certainty of the ground truth labels.

Another classification loss function is hinge loss, which is commonly used in support vector machines (SVMs). Hinge loss encourages correct predictions with confidence, aiming to maximize the margin between classes. This makes it particularly useful in binary classification tasks where distinctions between classes must be clear.

Calculating the loss function serves as a guide for improving the model. Loss values indicate how far off predictions are from actual results, enabling adjustments through optimization. The loss function acts as a feedback mechanism, directing the learning process. Lower loss indicates advanced alignment between predictions and true outcomes. After adjusting the YouTube prediction model, new forecasts resulted in lower loss values across all three functions, with the greatest improvement in MSE, as the model reduced the large prediction error for the pour-over video.

Loss functions not only evaluate model performance but also influence model training through optimization techniques like gradient descent. Gradient descent calculates the slope of the loss function with respect to each model parameter, determining the optimal direction to minimize loss. The model updates weight and bias terms iteratively until the loss is sufficiently minimized.

In summary, a loss function serves as both a scorekeeper that measures model performance and a guide that directs learning. Thanks to loss functions, my colleague can continue tweaking his YouTube AI model to minimize loss and improve prediction accuracy.

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