Your cart is currently empty!
Tag: Concepts
How to Speak Tech: The Non-Techie’s Guide to Key Technology Concepts
Price:$32.99– $16.79
(as of Jan 26,2025 03:23:58 UTC – Details)
Publisher : Apress; 2nd edition (March 27, 2019)
Language : English
Paperback : 194 pages
ISBN-10 : 1484243234
ISBN-13 : 978-1484243237
Item Weight : 10.4 ounces
Dimensions : 6.1 x 0.45 x 9.25 inchesCustomers say
Customers find the book’s language simple and easy to understand. They find it useful for non-technical readers, providing a quick primer on tech concepts. The book is described as an excellent resource for those with little or no experience.
AI-generated from the text of customer reviews
Are you a non-techie who struggles to understand all the jargon and technical terms thrown around in the world of technology? Don’t worry, we’ve got you covered! In this post, we’ll break down some key technology concepts in simple, easy-to-understand language so you can navigate the tech world with confidence. From the basics of coding to the cloud and cybersecurity, we’ll help you speak tech like a pro. Stay tuned for our beginner-friendly guide coming soon! #techjargon #non-techies #technologyconcepts
#Speak #Tech #NonTechies #Guide #Key #Technology #Concepts,business 101 for data professionalsSHRM CP/SCP Exam Prep: The Complete Guide with 5 Full-Length Tests to Master HR Concepts and Kickstart Your Career
Price:$24.97– $23.72
(as of Jan 23,2025 17:20:47 UTC – Details)
ASIN : B0DQGWLV8V
Publisher : Independently published (December 13, 2024)
Language : English
Paperback : 231 pages
ISBN-13 : 979-8303606005
Item Weight : 1.5 pounds
Dimensions : 8.5 x 0.53 x 11 inches
Are you ready to take your HR career to the next level? Look no further than our SHRM CP/SCP Exam Prep guide, complete with 5 full-length practice tests to help you master key HR concepts and ace the exam.This comprehensive guide covers everything you need to know to pass the SHRM Certified Professional (CP) or Senior Certified Professional (SCP) exam, including strategic planning, employee relations, diversity and inclusion, and more. Each section is broken down into easy-to-understand explanations, so you can feel confident going into the exam.
But that’s not all – our guide also includes 5 full-length practice tests to help you assess your knowledge and pinpoint areas where you may need to focus your studying. With detailed answer explanations for each question, you’ll be able to track your progress and make sure you’re on the right track to success.
Whether you’re just starting out in HR or looking to advance your career, our SHRM CP/SCP Exam Prep guide is the perfect resource to help you achieve your goals. Don’t wait any longer – kickstart your career and become SHRM certified today!
#SHRM #CPSCP #Exam #Prep #Complete #Guide #FullLength #Tests #Master #Concepts #Kickstart #Career,2024Information Theory Step-by-Step: Concepts, Applications, and Problem-Solving in Data, Signals & Communication (Step By Step Subject Guides)
Price: $14.99
(as of Jan 18,2025 06:58:24 UTC – Details)
ASIN : B0D5BSK21K
Publisher : Independently published (May 26, 2024)
Language : English
Paperback : 154 pages
ISBN-13 : 979-8326691576
Item Weight : 10.2 ounces
Dimensions : 6 x 0.35 x 9 inches
Information Theory Step-by-Step: Concepts, Applications, and Problem-Solving in Data, Signals & Communication (Step By Step Subject Guides)Are you looking to dive into the world of information theory but not sure where to start? Look no further! In this comprehensive guide, we will break down the key concepts, applications, and problem-solving techniques in data, signals, and communication using a step-by-step approach.
First, we will start by understanding the fundamental principles of information theory, including entropy, channel capacity, and coding theory. We will then explore how these concepts are applied in various real-world scenarios, such as data compression, error detection, and correction, and digital communication systems.
Next, we will delve into the practical aspects of information theory, including the use of coding techniques like Huffman coding, Reed-Solomon codes, and convolutional codes. We will also discuss the importance of signal processing and modulation techniques in communication systems.
Finally, we will provide you with a series of problem-solving exercises to help reinforce your understanding of the material. By the end of this guide, you will have a solid foundation in information theory and be equipped to tackle more advanced topics in the field.
Whether you are a student, researcher, or industry professional, this guide is the perfect resource to help you master the concepts, applications, and problem-solving techniques in information theory. So, grab a copy of Information Theory Step-by-Step and embark on your journey to becoming an expert in data, signals, and communication.
#Information #Theory #StepbyStep #Concepts #Applications #ProblemSolving #Data #Signals #Communication #Step #Step #Subject #Guides,machine learning: an applied mathematics introductionIntroduction to Data Science: A Python Approach to Concepts, Techniques and Applications (Undergraduate Topics in Computer Science)
Price:$49.99– $42.65
(as of Jan 18,2025 04:11:33 UTC – Details)
Publisher : Springer; 2nd ed. 2024 edition (April 13, 2024)
Language : English
Paperback : 260 pages
ISBN-10 : 3031489551
ISBN-13 : 978-3031489556
Item Weight : 13.1 ounces
Dimensions : 5.75 x 0.5 x 9 inches
Introduction to Data Science: A Python Approach to Concepts, Techniques and Applications (Undergraduate Topics in Computer Science)Data science is a rapidly growing field that involves the extraction of knowledge and insights from data. In today’s digital age, the ability to analyze and interpret data is essential for making informed decisions in various industries.
In this post, we will explore the fundamentals of data science, with a focus on using Python as the primary programming language. Python is widely regarded as one of the best programming languages for data analysis and machine learning due to its simplicity, versatility, and extensive libraries.
This book, “Introduction to Data Science: A Python Approach to Concepts, Techniques and Applications,” is designed for undergraduate students in computer science who are interested in learning the fundamentals of data science. The book covers essential topics such as data visualization, statistical analysis, machine learning, and more, all using Python.
Throughout the book, readers will learn how to manipulate and analyze data using Python libraries such as Pandas, NumPy, and Matplotlib. They will also gain a solid understanding of statistical concepts and techniques, as well as how to apply machine learning algorithms to real-world datasets.
By the end of the book, readers will have a comprehensive understanding of the principles and practices of data science, as well as the practical skills needed to analyze data and make data-driven decisions. Whether you are a student looking to enter the field of data science or a professional seeking to enhance your skills, this book is an invaluable resource for mastering data science with Python.
#Introduction #Data #Science #Python #Approach #Concepts #Techniques #Applications #Undergraduate #Topics #Computer #Science,machine learning: an applied mathematics introduction
From Concepts to Code
Price:$190.00– $161.27
(as of Jan 04,2025 19:59:21 UTC – Details)
Publisher : Chapman and Hall/CRC; 1st edition (May 6, 2024)
Language : English
Hardcover : 386 pages
ISBN-10 : 1032517956
ISBN-13 : 978-1032517957
Item Weight : 1.58 pounds
Dimensions : 6.14 x 1.06 x 9.21 inches
Have you ever had a great idea for an app or website, but didn’t know how to turn it into reality?In our latest blog post, we discuss the process of going from concept to code. We cover everything from brainstorming and wireframing to development and testing.
If you’re interested in learning more about how to bring your ideas to life through coding, be sure to check out our post! #conceptstocode #coding #development #appdevelopment #webdevelopment
#Concepts #Code,machine learning: an applied mathematics introduction
A Mathematical Approach to Machine Learning: Concepts and Applications
Machine learning is a rapidly growing field that has revolutionized industries ranging from healthcare to finance. At its core, machine learning is the process of using algorithms and statistical models to enable computers to learn from and make predictions or decisions based on data. One of the key components of machine learning is mathematics, which provides the foundation for understanding how these algorithms work and how to apply them effectively.Mathematics plays a crucial role in machine learning by providing the tools and techniques needed to analyze data, build models, and make predictions. Concepts such as linear algebra, calculus, and probability theory are essential for understanding the underlying principles of machine learning algorithms. By utilizing these mathematical concepts, researchers and practitioners can develop more accurate and efficient models that can extract valuable insights from large datasets.
One of the fundamental mathematical concepts in machine learning is linear algebra, which deals with vectors, matrices, and linear transformations. These concepts are essential for representing and manipulating data in a machine learning context. For example, in the field of image recognition, images can be represented as matrices of pixel values, and linear algebra can be used to perform operations such as matrix multiplication to process these images and extract features.
Calculus is another important mathematical tool in machine learning, particularly in the optimization of machine learning models. Optimization algorithms such as gradient descent rely on calculus to find the optimal parameters for a model by minimizing a loss function. By using calculus, researchers can iteratively adjust the parameters of a model to improve its performance and accuracy.
Probability theory is also crucial in machine learning, as it provides the foundation for understanding uncertainty and making predictions based on data. Machine learning models often rely on probabilistic models to make predictions or estimate the likelihood of different outcomes. By using probability theory, researchers can quantify uncertainty and make informed decisions based on the data available.
Overall, a solid understanding of mathematics is essential for anyone looking to work in the field of machine learning. By mastering concepts such as linear algebra, calculus, and probability theory, researchers and practitioners can develop more accurate and efficient machine learning models that can extract valuable insights from data. By applying a mathematical approach to machine learning, researchers can unlock the potential of this powerful technology and drive innovation in a wide range of industries.
#Mathematical #Approach #Machine #Learning #Concepts #Applications,machine learning: an applied mathematics introduction
Machine Learning : Concepts, Methodologies, Tools and Applications 3 volume set
Machine Learning : Concepts, Methodologies, Tools and Applications 3 volume set
Price : 399.99
Ends on : N/A
View on eBay
Machine Learning: Concepts, Methodologies, Tools and Applications – 3 Volume SetMachine learning has become an integral part of modern technology, with applications ranging from self-driving cars to personalized recommendations on streaming platforms. In this comprehensive 3-volume set, we delve into the foundational concepts, methodologies, tools, and practical applications of machine learning.
Volume 1: Foundations of Machine Learning
– Introduction to machine learning and its significance in today’s world
– Overview of different types of machine learning algorithms, including supervised, unsupervised, and reinforcement learning
– Exploring key concepts such as feature engineering, model evaluation, and bias-variance tradeoff
– Case studies and real-world examples to illustrate the principles discussedVolume 2: Methodologies and Tools in Machine Learning
– In-depth exploration of popular machine learning frameworks and libraries, such as TensorFlow, scikit-learn, and PyTorch
– Hands-on tutorials on data preprocessing, model training, and evaluation using these tools
– Advanced topics like deep learning, neural networks, and natural language processing
– Best practices for model deployment and scaling in production environmentsVolume 3: Applications of Machine Learning
– Practical applications of machine learning in various industries, including healthcare, finance, and e-commerce
– Case studies on image recognition, fraud detection, and personalized recommendations
– Ethical considerations and challenges in deploying machine learning systems
– Future trends and emerging technologies in the field of machine learningWhether you’re a beginner looking to understand the basics of machine learning or an experienced practitioner seeking to explore advanced techniques and applications, this 3-volume set is your go-to resource for all things machine learning. Dive in and uncover the endless possibilities that machine learning has to offer.
#Machine #Learning #Concepts #Methodologies #Tools #Applications #volume #set,machine learning: an applied mathematics introduction
Applying Mathematical Concepts to Understand Machine Learning Techniques
Machine learning techniques have become essential tools in various industries, from finance to healthcare to marketing. These techniques allow computers to learn from data and make predictions or decisions without being explicitly programmed. While machine learning algorithms can seem complex, they are built on fundamental mathematical concepts that are crucial for understanding how they work.One of the key mathematical concepts that underpin machine learning is linear algebra. Linear algebra involves the study of vectors, matrices, and linear transformations. In machine learning, matrices are used to represent data sets, where each row corresponds to an observation and each column corresponds to a feature. By performing operations on these matrices, machine learning algorithms can identify patterns and relationships in the data that can be used to make predictions.
Another important mathematical concept in machine learning is calculus. Calculus is used to optimize machine learning algorithms by finding the minimum or maximum of a function. For example, in gradient descent, a popular optimization algorithm used in machine learning, calculus is used to find the direction in which the algorithm should move to minimize the loss function.
Probability theory is also essential for understanding machine learning techniques. Probability theory allows us to quantify uncertainty and make predictions based on probabilities. Machine learning algorithms such as Naive Bayes and Gaussian processes rely on probability theory to make predictions and classify data.
Statistics is another mathematical concept that is crucial for machine learning. Statistics allows us to make inferences about a population based on a sample of data. Machine learning algorithms use statistical techniques to estimate parameters and make predictions about unseen data.
By applying mathematical concepts such as linear algebra, calculus, probability theory, and statistics, we can gain a deeper understanding of how machine learning techniques work. This understanding is crucial for developing and improving machine learning algorithms, as well as for interpreting the results of machine learning models.
In conclusion, mathematical concepts play a crucial role in understanding machine learning techniques. By applying concepts from linear algebra, calculus, probability theory, and statistics, we can gain insight into how machine learning algorithms work and how they can be applied to solve real-world problems. Understanding these mathematical concepts is essential for anyone working in the field of machine learning.
#Applying #Mathematical #Concepts #Understand #Machine #Learning #Techniques,machine learning: an applied mathematics introduction
The Evolution of RNNs: From Basic Concepts to Advanced Applications
Recurrent Neural Networks (RNNs) have come a long way since their inception in the late 1980s. Originally designed as a way to model sequential data, RNNs have evolved to become a powerful tool for a wide range of applications, from natural language processing to time series analysis.The basic concept behind RNNs is simple: they are neural networks that have connections feeding back into themselves. This allows them to maintain a memory of previous inputs, making them well-suited for tasks that involve sequences of data. The ability to learn from past inputs and make predictions about future inputs is what sets RNNs apart from other types of neural networks.
Early RNNs were limited by the problem of vanishing gradients, which made it difficult for them to learn long-range dependencies in sequences. However, research in the early 2010s led to the development of more advanced RNN architectures, such as Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), which address this issue by allowing the network to selectively update its memory.
These advancements in RNN architecture have led to a surge in the use of RNNs for a wide range of applications. In natural language processing, RNNs have been used for tasks such as language modeling, machine translation, and sentiment analysis. In time series analysis, RNNs have been used for tasks such as forecasting stock prices and detecting anomalies in sensor data.
One of the key advantages of RNNs is their ability to handle variable-length sequences of data. This makes them well-suited for tasks that involve processing text, audio, or video data, where the length of the input can vary from one example to the next.
In recent years, researchers have continued to push the boundaries of what RNNs can achieve. For example, in the field of image captioning, researchers have combined RNNs with convolutional neural networks (CNNs) to create models that can generate descriptions of images. In the field of reinforcement learning, researchers have used RNNs to build models that can learn to play video games or control robotic systems.
Overall, the evolution of RNNs from basic concepts to advanced applications has been driven by a combination of theoretical advances and practical innovations. As researchers continue to explore the capabilities of RNNs, we can expect to see even more exciting applications in the future.
#Evolution #RNNs #Basic #Concepts #Advanced #Applications,rnn
Demystifying Deep Neural Networks: Key Concepts and Applications
Deep neural networks have become increasingly popular in recent years, thanks to their ability to solve complex problems in a variety of fields. However, for many people, the inner workings of these networks can seem like a mystery. In this article, we will demystify deep neural networks by breaking down key concepts and exploring their applications.At its core, a deep neural network is a type of machine learning model that is inspired by the way the human brain processes information. These networks are made up of layers of interconnected nodes, or neurons, that work together to learn patterns and make predictions. The term “deep” refers to the fact that these networks typically have multiple layers, allowing them to learn more complex relationships in the data.
One key concept in understanding deep neural networks is the idea of “training” the model. During the training process, the network is fed a large amount of data and adjusts the weights of the connections between neurons to minimize the error in its predictions. This process is repeated iteratively until the network can accurately predict the output for new, unseen data.
Another important concept is the use of activation functions. These functions introduce non-linearities into the network, allowing it to learn complex patterns and relationships in the data. Some common activation functions include sigmoid, tanh, and ReLU (Rectified Linear Unit).
Deep neural networks have found applications in a wide range of fields, including computer vision, natural language processing, and speech recognition. In computer vision, deep neural networks have been used to power facial recognition systems, autonomous vehicles, and medical image analysis. In natural language processing, these networks have been used to build chatbots, language translation systems, and sentiment analysis tools. In speech recognition, deep neural networks have been employed in virtual assistants like Siri and Alexa.
In conclusion, deep neural networks are powerful tools for solving complex problems in a variety of fields. By understanding key concepts such as training, activation functions, and applications, we can demystify these networks and appreciate their potential for driving innovation and advancement in technology.
#Demystifying #Deep #Neural #Networks #Key #Concepts #Applications,dnn
