Tag Archives: Foundations

Cell Biology Step by Step: The Blueprint of Life: Exploring Cellular Components & the Foundations of Cellular Structure (Step By Step Subject Guides)

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Price: $24.99
(as of Dec 24,2024 20:24:35 UTC – Details)




ASIN ‏ : ‎ B0DNN5WP82
Publisher ‏ : ‎ Independently published (November 20, 2024)
Language ‏ : ‎ English
Hardcover ‏ : ‎ 168 pages
ISBN-13 ‏ : ‎ 979-8300577094
Item Weight ‏ : ‎ 11 ounces
Dimensions ‏ : ‎ 6 x 0.57 x 9 inches


Cell Biology Step by Step: The Blueprint of Life

In this post, we will delve into the fascinating world of cell biology, exploring the intricate components that make up the building blocks of life. From the nucleus to the mitochondria, we will uncover the foundations of cellular structure and the essential roles that each component plays in the functioning of a cell.

Step 1: Introduction to Cell Biology
We will start by providing an overview of cell biology, discussing the history of its discovery and the advancements that have been made in understanding the complex processes that occur within a cell.

Step 2: The Cell Membrane
One of the most important components of a cell is the cell membrane, which acts as a barrier between the cell’s internal environment and the external world. We will explore the structure and function of the cell membrane, including how it regulates the movement of molecules in and out of the cell.

Step 3: The Nucleus
At the heart of every cell lies the nucleus, which houses the cell’s genetic material and controls the cell’s activities. We will examine the structure of the nucleus and the role it plays in storing and transmitting genetic information.

Step 4: Mitochondria and Energy Production
Mitochondria are often referred to as the powerhouse of the cell, as they are responsible for producing energy through a process called cellular respiration. We will discuss the structure of mitochondria and how they generate ATP, the cell’s primary source of energy.

Step 5: Endoplasmic Reticulum and Protein Synthesis
The endoplasmic reticulum is a network of membranes within the cell that is involved in protein synthesis and lipid metabolism. We will explore the different types of endoplasmic reticulum and how they contribute to the production of proteins within the cell.

Step 6: Golgi Apparatus and Protein Packaging
The Golgi apparatus is responsible for processing and packaging proteins for transport within and outside the cell. We will examine the structure of the Golgi apparatus and how it modifies proteins before they are sent to their final destination.

By following this step-by-step guide, you will gain a deeper understanding of the cellular components that make up the blueprint of life. Stay tuned for more in-depth explorations of cell biology and the fascinating world of cellular structure.
#Cell #Biology #Step #Step #Blueprint #Life #Exploring #Cellular #Components #Foundations #Cellular #Structure #Step #Step #Subject #Guides,basicnology

Mastering NLP from Foundations to LLMs: Apply advanced rule-based techniques to LLMs and solve real-world business problems using Python

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Price: $52.99 - $40.77
(as of Dec 24,2024 14:25:06 UTC – Details)


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Packt is a leading publisher of technical learning content with the ability to publish books on emerging tech faster than any other.

Our mission is to increase the shared value of deep tech knowledge by helping tech pros put software to work.

We help the most interesting minds and ground-breaking creators on the planet distill and share the working knowledge of their peers.

Publisher ‏ : ‎ Packt Publishing (April 26, 2024)
Language ‏ : ‎ English
Paperback ‏ : ‎ 340 pages
ISBN-10 ‏ : ‎ 1804619183
ISBN-13 ‏ : ‎ 978-1804619186
Item Weight ‏ : ‎ 1.3 pounds
Dimensions ‏ : ‎ 0.58 x 7.5 x 9.25 inches

Customers say

Customers find the book provides detailed Python examples and system design insights that make NLP concepts easy to grasp and apply effectively. They describe it as a great primer for NLP and a vital resource for standing out as a skilled practitioner. However, opinions differ on the formatting – some find it easy to read and understand, while others mention issues with math equations formatting.

AI-generated from the text of customer reviews


Are you ready to take your NLP skills to the next level? In our upcoming workshop, “Mastering NLP from Foundations to LLMs,” we will dive deep into advanced rule-based techniques and show you how to apply them to Large Language Models (LLMs) using Python.

Whether you’re a seasoned NLP practitioner or just starting out, this workshop will equip you with the tools and knowledge needed to tackle real-world business problems with confidence. From sentiment analysis to text generation, we will cover a wide range of applications and provide hands-on experience working with cutting-edge NLP models.

Join us as we explore the latest advancements in NLP and learn how to leverage LLMs to unlock new possibilities in your projects. Don’t miss this opportunity to enhance your skills and take your NLP expertise to the next level. Sign up now and start mastering NLP like never before!
#Mastering #NLP #Foundations #LLMs #Apply #advanced #rulebased #techniques #LLMs #solve #realworld #business #problems #Python

AI Foundations Of Quantum Machine Learning: Easy To Read Guide Introducing the Foundations of Quantum Machine Learning

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Price: $3.99
(as of Dec 24,2024 14:12:15 UTC – Details)




ASIN ‏ : ‎ B0CW2ZWKS5
Publisher ‏ : ‎ Green Mountain Computing; 1st edition (February 20, 2024)
Publication date ‏ : ‎ February 20, 2024
Language ‏ : ‎ English
File size ‏ : ‎ 320 KB
Simultaneous device usage ‏ : ‎ Unlimited
Text-to-Speech ‏ : ‎ Enabled
Screen Reader ‏ : ‎ Supported
Enhanced typesetting ‏ : ‎ Enabled
X-Ray ‏ : ‎ Not Enabled
Word Wise ‏ : ‎ Not Enabled
Print length ‏ : ‎ 174 pages


Are you interested in diving into the exciting world of Quantum Machine Learning? Look no further! In this easy-to-read guide, we will introduce you to the foundations of Quantum Machine Learning, with a focus on the role of Artificial Intelligence (AI) in this cutting-edge field.

Quantum Machine Learning combines the principles of quantum physics with the power of machine learning to solve complex problems that are beyond the capabilities of classical computers. By harnessing the unique properties of quantum systems, such as superposition and entanglement, quantum computers have the potential to revolutionize the field of AI.

In this guide, we will cover key concepts such as qubits, quantum gates, quantum circuits, and quantum algorithms. We will also explore how AI techniques, such as neural networks and reinforcement learning, can be integrated with quantum computing to accelerate the development of advanced machine learning models.

Whether you are a beginner looking to learn more about the intersection of AI and quantum computing, or a seasoned professional seeking to stay ahead of the curve, this guide will provide you with a solid foundation in Quantum Machine Learning. Stay tuned for more in-depth articles and tutorials on this exciting topic!
#Foundations #Quantum #Machine #Learning #Easy #Read #Guide #Introducing #Foundations #Quantum #Machine #Learning

Data Mining 101 [Systematic Review]: A Holistic View of Data Mining: Covering Its Theoretical Foundations and Practical Real-World Applications

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Price: $2.99
(as of Dec 24,2024 11:45:04 UTC – Details)




ASIN ‏ : ‎ B0DFQGL3N1
Publication date ‏ : ‎ August 29, 2024
Language ‏ : ‎ English
File size ‏ : ‎ 645 KB
Simultaneous device usage ‏ : ‎ Unlimited
Text-to-Speech ‏ : ‎ Enabled
Screen Reader ‏ : ‎ Supported
Enhanced typesetting ‏ : ‎ Enabled
X-Ray ‏ : ‎ Not Enabled
Word Wise ‏ : ‎ Not Enabled
Print length ‏ : ‎ 22 pages


Data Mining 101 [Systematic Review]: A Holistic View of Data Mining: Covering Its Theoretical Foundations and Practical Real-World Applications

Data mining is a powerful tool used to extract meaningful insights from large datasets. It involves the process of discovering patterns, trends, and relationships within data to help businesses make informed decisions. In this systematic review, we will provide a comprehensive overview of data mining, covering its theoretical foundations and practical real-world applications.

Theoretical Foundations of Data Mining

Data mining is rooted in the fields of statistics, machine learning, and database systems. It utilizes a variety of algorithms and techniques to analyze data and uncover valuable information. Some of the key theoretical foundations of data mining include:

1. Statistics: Data mining often relies on statistical methods to analyze data and make inferences. Techniques such as regression analysis, hypothesis testing, and clustering are commonly used in data mining.

2. Machine Learning: Machine learning algorithms play a crucial role in data mining, as they enable computers to learn from data and make predictions or decisions without being explicitly programmed. Supervised learning, unsupervised learning, and reinforcement learning are common machine learning approaches used in data mining.

3. Database Systems: Data mining requires access to large datasets stored in databases. Knowledge of database systems and query languages is essential for extracting and manipulating data for analysis.

Practical Real-World Applications of Data Mining

Data mining has a wide range of practical applications across various industries. Some of the common real-world applications of data mining include:

1. Customer Segmentation: Data mining is used to segment customers based on their behavior, preferences, and demographics. This helps businesses tailor their marketing strategies and product offerings to different customer segments.

2. Fraud Detection: Financial institutions use data mining to detect fraudulent transactions and activities. By analyzing patterns and anomalies in transaction data, data mining algorithms can identify potential fraud cases.

3. Predictive Maintenance: In manufacturing industries, data mining is used for predictive maintenance of equipment and machinery. By analyzing sensor data and historical maintenance records, data mining algorithms can predict when equipment is likely to fail and schedule maintenance proactively.

4. Healthcare Analytics: Data mining is used in healthcare to analyze patient data and improve diagnosis, treatment, and outcomes. By analyzing electronic health records and medical imaging data, data mining can help healthcare providers make more accurate and timely decisions.

In conclusion, data mining is a valuable tool for extracting insights from data and making informed decisions. By understanding its theoretical foundations and practical applications, businesses can leverage data mining to gain a competitive advantage and drive innovation.
#Data #Mining #Systematic #Review #Holistic #View #Data #Mining #Covering #Theoretical #Foundations #Practical #RealWorld #Applications

Foundations of Reinforcement Learning with Applications in Finance (Chapman & Hall/CRC Mathematics and Artificial Intelligence Series)

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Price: $61.07
(as of Dec 24,2024 11:37:13 UTC – Details)




ASIN ‏ : ‎ B0BS1C266D
Publisher ‏ : ‎ Chapman and Hall/CRC; 1st edition (December 16, 2022)
Publication date ‏ : ‎ December 16, 2022
Language ‏ : ‎ English
File size ‏ : ‎ 22977 KB
Simultaneous device usage ‏ : ‎ Up to 4 simultaneous devices, per publisher limits
Text-to-Speech ‏ : ‎ Not enabled
Enhanced typesetting ‏ : ‎ Not Enabled
X-Ray ‏ : ‎ Not Enabled
Word Wise ‏ : ‎ Not Enabled
Print length ‏ : ‎ 522 pages
Format ‏ : ‎ Print Replica


Foundations of Reinforcement Learning with Applications in Finance (Chapman & Hall/CRC Mathematics and Artificial Intelligence Series)

Reinforcement learning is a powerful machine learning technique that has gained popularity in recent years due to its ability to learn complex behaviors and make decisions in an uncertain environment. In the field of finance, reinforcement learning has been successfully applied to a variety of problems, including portfolio optimization, algorithmic trading, and risk management.

In this book, the authors provide a comprehensive introduction to the foundations of reinforcement learning, with a focus on its applications in finance. The book covers key concepts such as Markov decision processes, dynamic programming, and temporal difference learning, as well as more advanced topics such as deep reinforcement learning and policy gradient methods.

Throughout the book, the authors provide practical examples and case studies to illustrate how reinforcement learning can be used to solve real-world financial problems. Whether you are a student, researcher, or practitioner in the field of finance, this book is a valuable resource for understanding the principles of reinforcement learning and applying them to financial decision-making.

With its clear explanations, rigorous mathematical treatment, and practical examples, Foundations of Reinforcement Learning with Applications in Finance is a must-read for anyone interested in harnessing the power of reinforcement learning in the financial industry.
#Foundations #Reinforcement #Learning #Applications #Finance #Chapman #HallCRC #Mathematics #Artificial #Intelligence #Series

Foundations of Statistical Natural Language Processing

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Price: $125.00 - $69.89
(as of Dec 24,2024 11:29:41 UTC – Details)




Publisher ‏ : ‎ The MIT Press; 1st edition (June 18, 1999)
Language ‏ : ‎ English
Hardcover ‏ : ‎ 620 pages
ISBN-10 ‏ : ‎ 0262133601
ISBN-13 ‏ : ‎ 978-0262133609
Reading age ‏ : ‎ 18 years and up
Grade level ‏ : ‎ 12 and up
Item Weight ‏ : ‎ 2.89 pounds
Dimensions ‏ : ‎ 9.46 x 8.04 x 1.25 inches


Foundations of Statistical Natural Language Processing

Statistical Natural Language Processing (NLP) is a subfield of artificial intelligence and linguistics that focuses on the development of algorithms and models to process and understand human language. It combines techniques from computer science, machine learning, and linguistics to enable computers to analyze and generate human language text.

The foundations of Statistical NLP lie in the following key concepts:

1. Probability theory: Statistical NLP relies heavily on the principles of probability theory to model uncertainties and make predictions about language data. Techniques such as n-gram models, hidden Markov models, and probabilistic context-free grammars are commonly used to estimate the likelihood of different sequences of words in a given language.

2. Machine learning: Machine learning algorithms play a crucial role in Statistical NLP by enabling computers to learn patterns and relationships in language data. Supervised learning techniques, such as support vector machines and neural networks, are commonly used for tasks such as text classification, named entity recognition, and sentiment analysis.

3. Linguistic theory: Linguistic theories provide a theoretical framework for understanding the structure and meaning of human language. Concepts such as syntax, semantics, and pragmatics are essential for developing NLP models that can accurately analyze and generate natural language text.

4. Computational linguistics: Computational linguistics is the interdisciplinary field that combines linguistics and computer science to study the computational aspects of human language. Techniques such as part-of-speech tagging, parsing, and machine translation are essential for building NLP systems that can effectively process and understand language data.

Overall, the foundations of Statistical NLP encompass a diverse range of techniques and theories from mathematics, computer science, and linguistics. By combining these disciplines, researchers and practitioners can develop advanced NLP systems that can perform a wide range of tasks, from language translation to sentiment analysis, with a high degree of accuracy and efficiency.
#Foundations #Statistical #Natural #Language #Processing

Beyond Fairness in Computer Vision: A Holistic Approach to Mitigating Harms and Fostering Community-Rooted Computer Vision Research (Foundations and Trends(r) in Computer Graphics and Vision)

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Price: $80.00
(as of Dec 24,2024 10:00:49 UTC – Details)




Publisher ‏ : ‎ Now Publishers (September 30, 2024)
Language ‏ : ‎ English
Paperback ‏ : ‎ 118 pages
ISBN-10 ‏ : ‎ 1638283540
ISBN-13 ‏ : ‎ 978-1638283546
Item Weight ‏ : ‎ 6.2 ounces
Dimensions ‏ : ‎ 6.14 x 0.25 x 9.21 inches


In recent years, computer vision has made significant advancements in various applications, from healthcare to autonomous vehicles. However, there is growing concern about the fairness and ethical implications of computer vision systems, particularly in areas such as bias, privacy, and discrimination.

In our latest research paper, “Beyond Fairness in Computer Vision: A Holistic Approach to Mitigating Harms and Fostering Community-Rooted Computer Vision Research,” published in Foundations and Trends(r) in Computer Graphics and Vision, we propose a comprehensive framework for addressing these challenges.

Our approach goes beyond traditional fairness metrics and focuses on creating community-rooted computer vision research that is inclusive, transparent, and accountable. We emphasize the importance of involving diverse stakeholders, including marginalized communities, in the design and evaluation of computer vision systems to ensure that they are fair and equitable.

Furthermore, we highlight the need for interdisciplinary collaboration between computer vision researchers, ethicists, policymakers, and community advocates to address the complex ethical issues surrounding computer vision technology. By taking a holistic approach, we can mitigate harms and foster a more responsible and socially conscious use of computer vision systems.

We believe that our framework provides a roadmap for advancing the field of computer vision in a way that benefits society as a whole. We invite researchers, practitioners, and policymakers to join us in this important conversation and work together to build a more ethical and inclusive future for computer vision technology.
#Fairness #Computer #Vision #Holistic #Approach #Mitigating #Harms #Fostering #CommunityRooted #Computer #Vision #Research #Foundations #Trendsr #Computer #Graphics #Vision

Mathematical Foundations of Reinforcement Learning

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Price: $89.99
(as of Dec 24,2024 08:36:26 UTC – Details)




Publisher ‏ : ‎ Springer; 2024th edition (February 22, 2025)
Language ‏ : ‎ English
ISBN-10 ‏ : ‎ 9819739438
ISBN-13 ‏ : ‎ 978-9819739431
Item Weight ‏ : ‎ 1.74 pounds


Reinforcement learning is a powerful machine learning technique that enables an agent to learn how to make decisions by interacting with an environment and receiving rewards or punishments based on its actions. At the core of reinforcement learning lies a strong foundation in mathematics, which helps to formalize the learning process and optimize the agent’s decision-making.

One key mathematical concept in reinforcement learning is the Markov decision process (MDP), which models the environment as a set of states, actions, transition probabilities, and rewards. MDPs provide a framework for formalizing the sequential decision-making process and finding an optimal policy for the agent to follow.

Another important mathematical tool in reinforcement learning is the concept of value functions, which quantify the expected future rewards that an agent can achieve from a given state or action. By optimizing these value functions, the agent can learn to make decisions that maximize its long-term rewards.

Additionally, reinforcement learning often involves the use of optimization techniques such as dynamic programming, Monte Carlo methods, and temporal difference learning. These methods help to update the agent’s policy based on its experiences and improve its decision-making over time.

Overall, a strong understanding of mathematical concepts such as MDPs, value functions, and optimization techniques is essential for developing and implementing effective reinforcement learning algorithms. By leveraging these mathematical foundations, researchers and practitioners can push the boundaries of what is possible in artificial intelligence and autonomous decision-making.
#Mathematical #Foundations #Reinforcement #Learning

Responsible Artificial Intelligence (Artificial Intelligence: Foundations, Theory, and Algorithms)

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Price: $64.99
(as of Dec 24,2024 06:39:30 UTC – Details)




Publisher ‏ : ‎ Springer; 1st ed. 2019 edition (November 13, 2019)
Language ‏ : ‎ English
Hardcover ‏ : ‎ 136 pages
ISBN-10 ‏ : ‎ 3030303705
ISBN-13 ‏ : ‎ 978-3030303709
Item Weight ‏ : ‎ 11.2 ounces
Dimensions ‏ : ‎ 6.14 x 0.51 x 9.21 inches


In today’s rapidly advancing technological landscape, the development and implementation of artificial intelligence (AI) systems have become increasingly prevalent. While AI has the potential to revolutionize industries and improve efficiency, it also raises important ethical considerations regarding the responsible use of these powerful tools.

In the book “Artificial Intelligence: Foundations, Theory, and Algorithms”, the authors delve into the key principles and concepts that underpin AI technology, emphasizing the importance of ethical considerations in the design and deployment of AI systems. By exploring the foundations, theory, and algorithms behind AI, readers gain a deeper understanding of how these systems work and the potential implications of their use.

One of the main themes of the book is the need for responsible AI, which involves ensuring that AI systems are developed and used in a way that aligns with ethical standards and respects human values. This includes considerations such as fairness, transparency, accountability, and privacy in AI decision-making processes.

By promoting responsible AI practices, we can help mitigate potential risks and ensure that AI technology is used for the greater good of society. As AI continues to evolve and shape our world, it is crucial that we prioritize ethical considerations and strive to create AI systems that benefit all individuals and communities.

Overall, “Artificial Intelligence: Foundations, Theory, and Algorithms” serves as a valuable resource for understanding the complexities of AI technology and the importance of responsible AI practices in today’s digital age. By engaging with these concepts, we can work towards a future where AI is used ethically and responsibly for the betterment of society as a whole.
#Responsible #Artificial #Intelligence #Artificial #Intelligence #Foundations #Theory #Algorithms

Graph Neural Networks: Foundations, Frontiers, and Applications

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Price: $119.99 - $55.78
(as of Dec 24,2024 06:06:53 UTC – Details)




Publisher ‏ : ‎ Springer; 1st ed. 2022 edition (January 4, 2022)
Language ‏ : ‎ English
Hardcover ‏ : ‎ 725 pages
ISBN-10 ‏ : ‎ 9811660530
ISBN-13 ‏ : ‎ 978-9811660535
Item Weight ‏ : ‎ 2.62 pounds
Dimensions ‏ : ‎ 6.25 x 1.75 x 9.5 inches


Graph Neural Networks (GNNs) have emerged as a powerful tool for analyzing and modeling complex relationships in data. In this post, we will explore the foundations of GNNs, discuss current frontiers in the field, and highlight some of the exciting applications where GNNs have been successfully applied.

Foundations of Graph Neural Networks:
GNNs are a type of neural network that operate on graph-structured data, such as social networks, citation networks, and molecular structures. Unlike traditional neural networks, which operate on grid-structured data like images or text, GNNs are able to capture the relational structure of data and leverage this information to make predictions.

At the core of GNNs are message passing algorithms, which allow nodes in a graph to exchange information with their neighbors. By iteratively passing messages between nodes, GNNs are able to aggregate information from the entire graph and make predictions based on this global context.

Frontiers in Graph Neural Networks:
One of the key challenges in GNN research is developing models that are able to effectively capture long-range dependencies in graphs. Current research is focused on designing more powerful message passing algorithms, incorporating attention mechanisms, and learning hierarchical representations of graph data.

Another frontier in GNN research is developing models that are able to handle dynamic graphs, where the structure of the graph changes over time. This requires designing GNNs that are able to adapt to changes in the graph topology and learn from sequential data.

Applications of Graph Neural Networks:
GNNs have been successfully applied to a wide range of applications, including social network analysis, recommendation systems, drug discovery, and computer vision. In social network analysis, GNNs can be used to predict user behavior, detect communities, and identify influential nodes. In drug discovery, GNNs have been used to predict the properties of molecules and design new drugs. In computer vision, GNNs have been applied to tasks like image segmentation, object detection, and image generation.

Overall, Graph Neural Networks have become a powerful tool for analyzing and modeling complex relationships in data. With ongoing research pushing the boundaries of what is possible with GNNs, we can expect to see even more exciting applications of this technology in the future.
#Graph #Neural #Networks #Foundations #Frontiers #Applications