Downloads & Free Reading Options - Results

Earthquakes, underground military operations, human trafficking and Bill Gates' new wonderland. Don't miss this episode of the Humorous Mathematics podcast, where things never seem to add up.--- Send in a voice message: https://anchor.fm/humorousmathematics/messageSupport this podcast: https://anchor.fm/humorousmathematics/support

Your favorite Coincidence theorist discuss (five)G and the potential connection to the current global crisis and more. --- Send in a voice message: https://anchor.fm/humorousmathematics/messageSupport this podcast: https://anchor.fm/humorousmathematics/support

On the show this week we talk to Sanjoy Mahajan, Associate Professor of Applied Science and Engineering at Olin College of Engineering, Visiting Associate Professor of Electrical Engineering and Computer Science at MIT, and author of Street-Fighting Mathematics: The Art of Educated Guessing and Opportunistic Problem Solving.iTunes: itunes.apple.com/us/podcast/inquiring-minds/id711675943RSS: feeds.feedburner.com/inquiring-mindsStitcher: stitcher.com/podcast/inquiring-mindsTumblr: inquiringshow.tumblr.comSupport the show: https://www.patreon.com/inquiringmindsSee omnystudio.com/listener for privacy information.

On the show this week we talk to Sanjoy Mahajan, Associate Professor of Applied Science and Engineering at Olin College of Engineering, Visiting Associate Professor of Electrical Engineering and Computer Science at MIT, and author of Street-Fighting Mathematics: The Art of Educated Guessing and Opportunistic Problem Solving.iTunes: itunes.apple.com/us/podcast/inquiring-minds/id711675943RSS: feeds.feedburner.com/inquiring-mindsStitcher: stitcher.com/podcast/inquiring-mindsTumblr: inquiringshow.tumblr.comSupport the show: https://www.patreon.com/inquiringmindsSee omnystudio.com/listener for privacy information.

On the show this week we talk to Sanjoy Mahajan, Associate Professor of Applied Science and Engineering at Olin College of Engineering, Visiting Associate Professor of Electrical Engineering and Computer Science at MIT, and author of Street-Fighting Mathematics: The Art of Educated Guessing and Opportunistic Problem Solving.iTunes: itunes.apple.com/us/podcast/inquiring-minds/id711675943RSS: feeds.feedburner.com/inquiring-mindsStitcher: stitcher.com/podcast/inquiring-mindsTumblr: inquiringshow.tumblr.comSupport the show: https://www.patreon.com/inquiringmindsSee omnystudio.com/listener for privacy information.

MathDisk IV Side 2 (Arithmetic Teacher) National Council of Teachers of Mathematics

A short excerpt from Maharishi's Absolute Theory of Defence

Support fall-readiness for students as your student prepares to enter the next grade in mathematics

HTML documentation and inline help for Sage This item contains old versions of the Arch Linux package for sage-mathematics-doc . Website of the upstream project: http://www.sagemath.org License: GPL See the Arch Linux Archive documentation for details.

🧮 A collection of resources to learn mathematics for machine learning Mathematics for Machine Learning A collection of resources to learn mathematics for machine learning. Mathematics for Machine Learning by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong This is probably the place you want to start. Start slowly and work on some examples. Pay close attention to the notation and get comfortable with it. Book: https://mml-book.github.io The Elements of Statistical Learning by Jerome H. Friedman, Robert Tibshirani, and Trevor Hastie Machine learning deals with data and in turn uncertainty which is what statistics aims to teach. Get comfortable with topics like estimators, statistical significance, etc. Book: https://hastie.su.domains/ElemStatLearn/ If you are interested in an introduction to statistical learning, then you might want to check out "An Introduction to Statistical Learning" Probability Theory: The Logic of Science by E. T. Jaynes In machine learning, we are interested in building probabilistic models and thus you will come across concepts from probability theory like conditional probability and different probability distributions. Source: https://bayes.wustl.edu/etj/prob/book.pdf Probabilistic Machine Learning: An Introduction by Kevin Patrick Murphy This book contains a comprehensive overview of classical machine learning methods and the principles explaining them. Book: https://probml.github.io/pml-book/book1.html Multivariate Calculus by Imperial College London by Dr. Sam Cooper & Dr. David Dye Backpropagation is a key algorithm for training deep neural nets that rely on Calculus. Get familiar with concepts like chain rule, Jacobian, gradient descent,. Video Playlist: https://www.youtube.com/playlist?list=PLiiljHvN6z193BBzS0Ln8NnqQmzimTW23 Mathematics for Machine Learning - Linear Algebra by Dr. Sam Cooper & Dr. David Dye Agreat companion to the previous video lectures. Neural networks perform transformations on data and you need linear algebra to get better intuitions of how that is done. Video Playlist: https://www.youtube.com/playlist?list=PLiiljHvN6z1_o1ztXTKWPrShrMrBLo5P3 Mathematics for Deep Learning This reference contains some mathematical concepts to help build a better understanding of deep learning. Chapter: https://d2l.ai/chapter_appendix-mathematics-for-deep-learning/index.html The Matrix Calculus You Need For Deep Learning by Terence Parr & Jeremy Howard In deep learning, you need to understand a bunch of fundamental matrix operations. If you want to dive deep into the math of matrix calculus this is your guide. Paper: https://arxiv.org/abs/1802.01528 Information Theory, Inference and Learning Algorithms by David J. C. MacKay When you are applying machine learning you are dealing with information processing which in essence relies on ideas from information theory such as entropy and KL Divergence,... Book: https://www.inference.org.uk/itprnn/book.html This collection is far from exhaustive but it should provide a good foundation to start learning some of the mathematical concepts used in machine learning. Reach out on Twitter if you have any questions. To restore the repository download the bundle wget https://archive.org/download/github.com-dair-ai-Mathematics-for-ML_-_2022-05-22_06-36-12/dair-ai-Mathematics-for-ML_-_2022-05-22_06-36-12.bundle and run: git clone dair-ai-Mathematics-for-ML_-_2022-05-22_06-36-12.bundle Source: https://github.com/dair-ai/Mathematics-for-ML Uploader: dair-ai Upload date: 2022-05-22

By Kong Jie Wey, Class of 2017

You can open ZIM files with Kiwix software.

You can open ZIM files with Kiwix software.

CATCH 22 is a paradoxical situation where an individual cannot escape due to contradictory rules or conditions (from a novel by Joseph Heller). PressTV - Explainer: How Iran outsmarted Israel and downed F-35 fighter jets for the first time (Ivan Kesic) - https://www.presstv.ir/Detail/2025/06/14/749845/explainer-how-iran-outsmarted-israeli-regime-shot-down-F35-fighter-jets-first-time WW2 Daylight bombing horrors Article 1 - https://www.airandspaceforces.com/article/1008daylight/ Article 2 - https://worldwarwings.com/allied-bombers-losses-wwii/ REMARQUE88 SUBSTACK https://substack.com/@remarque88moneylessworld Remarque88 Channel - RUMBLE https://rumble.com/user/Remarque88 ODYSEE https://odysee.com/@dtcorr8888:b?view=content BITCHUTE https://www.bitchute.com/channel/GISyVy9bw05Q/ BITCHUTE (Crownuts) https://old.bitchute.com/channel/kN9FVa9ZwGRT/ REMARQUE88/CROWNUTS CHANNELS GiveSendGo Donation link - https://www.givesendgo.com/GBUJ1

CATCH 22 is a paradoxical situation where an individual cannot escape due to contradictory rules or conditions (from a novel by Joseph Heller). PressTV - Explainer: How Iran outsmarted Israel and downed F-35 fighter jets for the first time (Ivan Kesic) - https://www.presstv.ir/Detail/2025/06/14/749845/explainer-how-iran-outsmarted-israeli-regime-shot-down-F35-fighter-jets-first-time WW2 Daylight bombing horrors Article 1 - https://www.airandspaceforces.com/article/1008daylight/ Article 2 - https://worldwarwings.com/allied-bombers-losses-wwii/ REMARQUE88 SUBSTACK https://substack.com/@remarque88moneylessworld Remarque88 Channel - RUMBLE https://rumble.com/user/Remarque88 ODYSEE https://odysee.com/@dtcorr8888:b?view=content BITCHUTE https://www.bitchute.com/channel/GISyVy9bw05Q/ BITCHUTE (Crownuts) https://old.bitchute.com/channel/kN9FVa9ZwGRT/ REMARQUE88/CROWNUTS CHANNELS GiveSendGo Donation link - https://www.givesendgo.com/GBUJ1

CATCH 22 is a paradoxical situation where an individual cannot escape due to contradictory rules or conditions (from a novel by Joseph Heller). PressTV - Explainer: How Iran outsmarted Israel and downed F-35 fighter jets for the first time (Ivan Kesic) - https://www.presstv.ir/Detail/2025/06/14/749845/explainer-how-iran-outsmarted-israeli-regime-shot-down-F35-fighter-jets-first-time WW2 Daylight bombing horrors Article 1 - https://www.airandspaceforces.com/article/1008daylight/ Article 2 - https://worldwarwings.com/allied-bombers-losses-wwii/ REMARQUE88 SUBSTACK https://substack.com/@remarque88moneylessworld Remarque88 Channel - RUMBLE https://rumble.com/user/Remarque88 ODYSEE https://odysee.com/@dtcorr8888:b?view=content BITCHUTE https://www.bitchute.com/channel/GISyVy9bw05Q/ BITCHUTE (Crownuts) https://old.bitchute.com/channel/kN9FVa9ZwGRT/ REMARQUE88/CROWNUTS CHANNELS GiveSendGo Donation link - https://www.givesendgo.com/GBUJ1

CATCH 22 is a paradoxical situation where an individual cannot escape due to contradictory rules or conditions (from a novel by Joseph Heller). PressTV - Explainer: How Iran outsmarted Israel and downed F-35 fighter jets for the first time (Ivan Kesic) - https://www.presstv.ir/Detail/2025/06/14/749845/explainer-how-iran-outsmarted-israeli-regime-shot-down-F35-fighter-jets-first-time WW2 Daylight bombing horrors Article 1 - https://www.airandspaceforces.com/article/1008daylight/ Article 2 - https://worldwarwings.com/allied-bombers-losses-wwii/ REMARQUE88 SUBSTACK https://substack.com/@remarque88moneylessworld Remarque88 Channel - RUMBLE https://rumble.com/user/Remarque88 ODYSEE https://odysee.com/@dtcorr8888:b?view=content BITCHUTE https://www.bitchute.com/channel/GISyVy9bw05Q/ BITCHUTE (Crownuts) https://old.bitchute.com/channel/kN9FVa9ZwGRT/ REMARQUE88/CROWNUTS CHANNELS GiveSendGo Donation link - https://www.givesendgo.com/GBUJ1

CATCH 22 is a paradoxical situation where an individual cannot escape due to contradictory rules or conditions (from a novel by Joseph Heller). PressTV - Explainer: How Iran outsmarted Israel and downed F-35 fighter jets for the first time (Ivan Kesic) - https://www.presstv.ir/Detail/2025/06/14/749845/explainer-how-iran-outsmarted-israeli-regime-shot-down-F35-fighter-jets-first-time WW2 Daylight bombing horrors Article 1 - https://www.airandspaceforces.com/article/1008daylight/ Article 2 - https://worldwarwings.com/allied-bombers-losses-wwii/ REMARQUE88 SUBSTACK https://substack.com/@remarque88moneylessworld Remarque88 Channel - RUMBLE https://rumble.com/user/Remarque88 ODYSEE https://odysee.com/@dtcorr8888:b?view=content BITCHUTE https://www.bitchute.com/channel/GISyVy9bw05Q/ BITCHUTE (Crownuts) https://old.bitchute.com/channel/kN9FVa9ZwGRT/ REMARQUE88/CROWNUTS CHANNELS GiveSendGo Donation link - https://www.givesendgo.com/GBUJ1

Mathematics Series by Spectrum Software

Support fall-readiness for students as your student prepares to enter the next grade in mathematics

Support fall-readiness for students as your student prepares to enter the next grade in mathematics

Support fall-readiness for students as your student prepares to enter the next grade in mathematics

#MaryamMirzakhani #iranian genius woman, mathematician & professor of #mathematics at @Stanford has just passed away. Condolences #Science https://t.co/t4di67b1X8

The causal set approach to quantum gravity is based on the hypothesis that the underlying structure of spacetime is that of a random partial order. We survey some of the interesting mathematics that has arisen in connection with the causal set hypothesis, and describe how the mathematical theory can be translated to the application area. We highlight a number of open problems of interest to those working in causal set theory.

The causal set approach to quantum gravity is based on the hypothesis that the underlying structure of spacetime is that of a random partial order. We survey some of the interesting mathematics that has arisen in connection with the causal set hypothesis, and describe how the mathematical theory can be translated to the application area. We highlight a number of open problems of interest to those working in causal set theory.

The causal set approach to quantum gravity is based on the hypothesis that the underlying structure of spacetime is that of a random partial order. We survey some of the interesting mathematics that has arisen in connection with the causal set hypothesis, and describe how the mathematical theory can be translated to the application area. We highlight a number of open problems of interest to those working in causal set theory.

Mathematics Action Games - Pyramid Puzzler (1984)(Roklan)(US)

Mathematics Action Games - Pyramid Puzzler (1984)(Roklan)(US)

Secondary School Elementary Mathematics Materials Compilation as Updated on 2025, September 13 

Secondary School Elementary Mathematics Materials Compilation as Updated on 2025, September 13 

Perma.cc archive of https://www.getrevue.co/profile/themarkup/issues/the-mathematics-of-amazon-s-advantage-803575 created on 2022-09-09 23:05:39.489305+00:00.

Support fall-readiness for students as your student prepares to enter the next grade in mathematics

Support fall-readiness for students as your student prepares to enter the next grade in mathematics

Support fall-readiness for students as your student prepares to enter the next grade in mathematics

Support fall-readiness for students as your student prepares to enter the next grade in mathematics

Support fall-readiness for students as your student prepares to enter the next grade in mathematics

Support fall-readiness for students as your student prepares to enter the next grade in mathematics

Support fall-readiness for students as your student prepares to enter the next grade in mathematics

The book aims to bridge the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It covers the fundamental mathematical tools needed to understand machine learning, including linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability, and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students or professionals to efficiently learn the mathematics. The book uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models, and support vector machines . The book is suitable for students and others with a mathematical background. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding.

The book aims to bridge the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It covers the fundamental mathematical tools needed to understand machine learning, including linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability, and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students or professionals to efficiently learn the mathematics. The book uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models, and support vector machines . The book is suitable for students and others with a mathematical background. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding.

This article focuses on a secondary mathematics teacher education program where traditional field placements are prevalent in grades 9-12. Traditional, for this article, is defined as and comprised of: (a) straight-rowed desks in classrooms, (b) explicitly direct instruction (teacher-centered classrooms), (c) predominantly repetition of low-level cognitive tasks, and (d) consistent absence of technology use by students to explore mathematics concepts and problem solving. This article aims to inform readers of how one secondary mathematics teacher education program is addressing the challenges many programs currently experience in producing new teachers of best practice through advanced mathematical tasks and advanced technology use for teaching mathematics within education courses. Early evidence appears to be having effects on some pre-service teachers pre-conceived beliefs about teaching mathematics traditionally with greater anticipation for long-term sustainable change.

This study investigated the effects of general intelligence and seven specific cognitive abilities on college-age students' mathematics achievement. The present investigation went beyond previous research by employing structural equation modeling. It also represents the first study to examine the direct and indirect effects of general and specific cognitive abilities, simultaneously, on the mathematics achievement of college-age students. A model developed using the Cattell-Horn-Carroll theory of intelligence was the theoretical model used in all analyses. Data from 1,054 college-age students who participated in the standardization of the Woodcock-Johnson III (Woodcock, McGrew, & Mather, 2001) were divided into a calibration sample set and validation sample set. The calibration data set was used for model testing and modification and the independent validation sample data set was used for model validation. The specific areas of intelligence demonstrating direct effects on the mathematics achievement dependent variable were Crystallized Intelligence and Fluid Reasoning. The effects of general intelligence were found to be "indirect" in the college-age sample. Implications for instruction and intervention to improve college student's mathematics achievement are provided.

The aim of this research was to examine mathematics teachers' performances in defining special types of quadrilaterals, identifying their family and hierarchically classifying them. In this vein, 33 of 58 primary school mathematics teachers working in the province of Yozgat, Turkey were voluntarily recruited for this survey, and they were asked to complete a data collection form consisting of three open-ended questions. In the first question, participants were asked to define the special types of quadrilaterals. In the second question, teachers were asked to recognise kites, trapezoids, parallelograms, rectangles and rhombuses among 15 quadrilaterals. Finally, participants were prompted to make a hierarchical classification of the special types of quadrilaterals. The results of this study illustrated that while 20 mathematics teachers could define a kite hierarchically, only 1 of the sample could hierarchically define a trapezoid. In addition, participants encountered difficulties not only in determining the kite and trapezoid family, but also in demonstrating the relations of kite-rhombus, trapezoid-parallelogram and trapezoid-rectangle quadrilaterals. Hence, in-service training programs for primary school mathematics teachers should be organized to improve content knowledge of geometry in general and quadrilaterals in particular.

This study examined the knowledge levels of prospective mathematics teachers about the concepts of degree and radian, which are among the angle measuring units that constitute the basis of trigonometry, and the relationships between those concepts. The study group consisted of 93 prospective mathematics teachers attending a state university in Turkey. Qualitative and quantitative research methods were used for data collection and analysis. 4 questions about the concepts of degree and radian were asked to the prospective mathematics teachers. The responses of the prospective teachers were categorized as correct and incorrect. Then, incorrect answers were divided into sub-categories by means of coding method and presented in tables. According to research findings, 40% of the prospective mathematics teachers defined the concept of degree correctly while approximately 90% made an incorrect definition of radian.

In this article the development of two teachers as they make the transition from pre-service teachers to experienced teachers is examined. While these teachers participated in the same mathematics methods course and similar collaborative environments in their practicum experiences, their mathematics classroom instructional practices revealed stark differences by the time they were experienced teachers. In an effort to investigate these differences, the teachers' beliefs were explored in relation to those promoted in their pre-service course and in relation to their implementation of current mathematics education reform practices. Interview Protocols are appended. (Contains 1 table and 2 figures.)

Since its inception, the United States has struggled with its responsibility for educating African American students. Its history of denial and discrimination in the education of Black children has created a national crisis in which academic difficulty and school failure is disproportionately high. In an effort to improve the education of African American students, mounting evidence suggests that cultural aspects of students' learning styles can impact achievement levels in classrooms. One cultural aspect that was highlighted by Boykin in the 1980s and is gaining renewed attention is verve. By definition, verve is the propensity for energetic, intense, stylistic body language and expression (Boykin, 1983). According to Boykin (1977, 1983, 2001), verve is a definite component of learning style for African American children. The purpose of this study was to examine the relationship between verve and the academic achievement levels of selected African American middle-school students in reading and mathematics in an urban setting. This study was designed to answer the following research questions: (1) Were verve levels different between African American and European American students?; (2) Were verve levels different among males and females?; and (3) Was there a relationship between verve and the academic achievement of African American students in reading and mathematics? To focus on verve does not diminish the importance of other aspects of the schooling experience. Certainly, macro systems (i.e., resources, tracking programs, and curricula), as well as micro systems (i.e., instruction and teacher interactions) are pertinent. However, with the increasing emphasis on culture and culturally responsive teaching and pedagogy, there is great value in understanding more about verve and the academic achievement of African American students. (Contains 2 tables.)

This study investigated the opportunity to learn (OTL) that is available to Grade 12 mathematics learners. Learner workbooks were analysed in terms of time on task, curriculum coverage, curriculum coherence, and cognitive demand. Based on these elements, experienced mathematics teachers judged the opportunity that the learners have to achieve more than 60% for each topic. According to the workbooks, the average number of active learning days in this sample was 54.1 days per annum. This resulted in limited curriculum coverage in almost all sections in 16 of the 18 under-performing schools. In these schools, learners spent most of their time practising routine procedures. The high correlation of 0.95 (p < 0.001) between the experts' prediction about the opportunity to learn in the different schools (based on the learner workbooks) and learners' actual performance in the Grade 12 exam shows that the number, the coverage, the cognitive level, and the coherence of activities play a major role in understanding learner performance.

Khan Academy is a large and popular open educational resource (OER) with little empirical study into its impact on student achievement in mathematics when used in schools. In this study, we examined the use of Khan Academy as a mathematics intervention among seventh grade students over a 4-week period versus a control group. We also compared differences between students who had supplemental mathematics instruction and those who had not. In both cases, we found no statistically significant differences in student test scores. Khan Academy has several internal metrics used to track student performance and use. We found significant relationships between these metrics and student test scores in this study. Khan Academy and other OER provide access to information and knowledge to large numbers of the population. This research adds to the discourse methods by which Khan Academy and other OER may affect learners.

The quality of national examination items plays an enormous role in identifying students' competencies mastery and their difficulties. This study aims to identify the difficult items in the Junior High School Mathematics National Examination, to find the factors that cause students' difficulty and to reveal the strategies that the teachers and the students might implement in order to overcome them. The study is phenomenological research with the mixed methods. The data were collected using documentation of students' responses and focus group discussion (FGD) of teachers. The data analysis was conducted using Milles & Hubberman steps. The results of the study showed that there were 4 difficult items of the 40 test items for the students. The students' difficulties were the lack of concept understanding, difficulties in calculating, difficulties in selecting information, being deceived by the distractors, being unaccustomed to completing complex and non-integers test items, and completing contextual test items that have been presented in the form of figures or narrative texts.

Propelled by Maxine Greene's (1988) continuum of freedom from normative structures to critical consciousness and action, I illuminate the institutional and individual influences on teacher development and action in mathematics teaching. I focus on the question: What barriers and openings, both individually and institutionally, spur teachers to consider equitable mathematical teaching practices as important to pursue with commitment? Reflecting on the broad cultural features that uphold mathematical inequities, factors that inform mathematical contexts, and constructions of individual identity, I contemplate avenues to enter meaningful, asset-oriented growth for white teachers in particular toward equity practices in mathematics. I argue that linear frameworks, such as white privilege and racial identity development, do not provide the required reflection and action steps for transformation. Rather, the nuance of societal conditions and dynamics of individual identity must be examined in depth by teachers and mathematics classrooms to progress toward equity. Teachers' identity convergence, the moral imperative to understand the self and others, will prompt institutional change before interest convergence (Bell, 2005a) necessitates it, as the economic benefits of mathematical equity are not immediately apparent.