` and `
` to explicitly mark paragraphs. (In the previous method, we had just used Carriage Returns and Line Feeds to mark them.) We may write: ```htmlThis is the first, which is important.
``` In the typesetting language used for writing this book, markup is introduced with the backslash escape character, followed by a descriptive name of the change being made, with the contents enclosed in curly brackets `{}`: ## Section Title This is the \textit{first} paragraph, which is \textbf{important}. Here, we have used `\section{}` for the section title, `\textit{}` for italic, and `\textbf{}` for bold. These differing mark-up systems are not just historical artifacts; they serve different purposes. The requirements may be wholly different for a document to be printed, to be put on the web, or to be viewed on an eBook reader. We promised to talk about representing the world's many languages and writing systems. Since 1989, there has been an international industrial effort, under the Unicode initiative, to encode more than one hundred thousand characters, giving each a number, and defining how they may be combined in valid ways. There are more than a million total slots available for future use. It is important to say that the Unicode system is concerned only with assigning characters to numbers. It does not specify the shapes those characters take: that is a matter for typographic choices. The principle is one of separation of concerns: that each part of a computer system should do one job well and allow interaction with the other, similarly well-designed components. This is particularly difficult for the Unicode system, which must navigate innumerable cultural differences and a wide variety of specific needs. The following five pages give some examples drawn from the huge Unicode standard. #################### File: A%20MACHINE%20MADE%20THIS%20BOOK%20ten%20sketches%20of%20computer%20science%20-%20JOHN%20WHITINGTON%20%28PDF%29.pdf Page: 102 Context: # Chapter 7. Doing Sums If \( y \) is greater than 0, on the other hand, we want to calculate \( x \times x^{y-1} \): ### power x y ``` if y = 0 then 1 else x power (y - 1) ``` So, we can now calculate \( 2^5 \), showing just the important steps: ### power 2 5 \[ \begin{align*} &= 2 \times 2^{4} \\ &= 2 \times (2 \times 2^{3}) \\ &= 2 \times (2 \times (2 \times 2^{2})) \\ &= 2 \times (2 \times (2 \times (2 \times 2^{1}))) \\ &= 2 \times (2 \times (2 \times (2 \times 1))) \\ &= 32 \end{align*} \] We have looked at numbers like 2 and 32, and the truth values **true** and **false**, but interesting programs often have to operate on more complicated structures. One such is a list, which we write with square brackets and commas, like this: `[1, 5, 4]`. A list is an ordered collection of other values. That is to say, the lists `[1, 5, 4]` and `[5, 4]` are different, even though they contain the same values. There is an empty list `[]` which contains no items. The first element of a list is called the **head**, and there is a built-in function to get it: ### head [1, 5, 4] ``` = 1 ``` The rest of the elements are collectively referred to as the **tail**, and again there is a built-in function to retrieve it: ### tail [1, 5, 4] ``` = [5, 4] ``` The empty list `[]` has neither a head nor a tail. We need just one more thing for our example programs, and that is the `•` operator which sticks two lists together: ``` [1, 5, 4] • [2, 3] ``` \[ = [1, 5, 4, 2, 3] \] #################### File: A%20MACHINE%20MADE%20THIS%20BOOK%20ten%20sketches%20of%20computer%20science%20-%20JOHN%20WHITINGTON%20%28PDF%29.pdf Page: 200 Context: ``` # Index - **resolution**, 3 - **river**, 141 - **Robert W. Floyd**, 118 - **rocket**, 104 - **rule-based hyphenation**, 138 - **Russian characters**, 33 - **Scholes, Christopher Latham**, 53 - **Scrabble**, 69 - **screen**, 3 - **search**, 41 - **engine**, 51 - **function**, 45 - **shape**, 15 - **built from lines**, 9 - **curved**, 15 - **filling**, 9 - **scaling**, 16 - **shift key**, 30 - **ship curves**, 17 - **skipping rule**, 49 - **small caps**, 125 - **sort**, 91 - **Stanford University**, 118 - **Steinberg**, - **Louis**, 118 - **Steinway Hall**, 108 - **stopping out**, 104 - **sub-pixel**, 8 - **subdivision**, 22 - **tablet**, 3 - **tag**, 34 - **tall**, 68 - **Talbot**, - **William Henry Fox**, 108 - **telegraph**, 30 - **text block**, 136 - **textual data**, 27 - **Thai alphabet**, 37 - **The Histories**, 27 - **threshold**, 98, 99 - **toner**, 4 - **torch**, - **for signalling**, 28 - **tree**, 82 - **root of**, 82 - **true**, 45, 84 - **typeface**, 5, 15, 33 - **typesetting**, 34 - **typewriter**, 53 - **unambiguous decoding**, 70 - **underfull line**, 137 - **Unicode**, 34 - **units**, 2 - **UNIVAC**, 60 - **universal compression**, 66 - **University of Cambridge**, 30 - **University of Washington**, 58 - **value**, 81 - **variable**, 83 - **video**, - **storage of**, 5 - **Western language**, 36 - **widow**, 139 - **William Henry Fox Talbot**, 108 - **woodblock**, 100 - **Zapf**, - **Hermann**, 123 - **Zapfino**, 127 - **Zhuyin**, 62 ``` #################### File: A%20MACHINE%20MADE%20THIS%20BOOK%20ten%20sketches%20of%20computer%20science%20-%20JOHN%20WHITINGTON%20%28PDF%29.pdf Page: 99 Context: # Chapter 7. Doing Sums (This is only somewhat related to the `if...then...else` construct of Chapter 4 – please put it out of your mind.) As a mathematical construct, this looks rather strange: we are used to seeing operators like `+` and `*`, which consist of one symbol and have an operand either side. This new operator has three parts (`if`, `then`, and `else`) and three operands (`x = 4`, `0`, and `x + 1`), and they are spread all over the place! But if we write it out as a tree, it looks much like the earlier trees: ``` if...then...else / \ 0 + / \ x 1 ``` An operator having more than two operands is not so strange after all. Suppose we evaluate it in the environment where `x = 6`: ``` if...then...else / \ 0 + / \ false 7 / \ 6 1 ``` Of course, we can write this out in linear form: * if `x = 4` then `0` else `6 + 1` * if `false` then `0` else `6 + 1` * `= 6 + 1` * `= 7` And, we can name the function: ``` test / = if x = 4 then 0 else x + 1 ``` We are getting a little closer to the sorts of calculations a real program does: making decisions about which part of an expression to evaluate. #################### File: A%20MACHINE%20MADE%20THIS%20BOOK%20ten%20sketches%20of%20computer%20science%20-%20JOHN%20WHITINGTON%20%28PDF%29.pdf Page: 118 Context: # Chapter 8. Grey Areas  The term "half-painted" involves a device called a rocker to roughen the plate all over. Ink gathers in the little indentations made by such a process, leading to an entirely black image. Burnishing tools can be used to flatten the copper in areas where ink is not wanted. Because this process is gradual (one may burnish more or less), the illusion of shades is easier to achieve. Figure D shows a mezzotint plate being wiped off ready for printing. Unfortunately, due to the softness of copper and the smallness of the indentations, these plates did not last long, and the quality of the printing declined with each pressing. Figure E shows a mezzotint print. Note the fineness of the grey tone reproduction. Another alternative to engraving is the process of etching, in which the whole plate is covered in an acid-resistant substance, which is then scratched off using tools in areas where the artist wants it to appear in the final print. The plate is then washed with acid, which roughens the metal in unprotected areas so that they will hold ink. The plate is then printed as with any other intaglio process. The great advantage is that this process is valuable to the general artist, who can draw in this medium without learning the difficult metalwork skills of the engraver. Improvements to the process include "stopping out," where the plate is briefly dipped in the acid, more acid-resistant substances added to certain areas, and then the plate is dipped again. This allows better control over grey tones. Figure F is an etching by Rembrandt, known as *The Hundred Guilder Print* after the sum reportedly once paid for a copy. Image Analysis: I'm unable to analyze the visual content as requested. If you have any specific questions or need information on a related topic, feel free to ask! #################### File: A%20First%20Encounter%20with%20Machine%20Learning%20-%20Max%20Welling%20%28PDF%29.pdf Page: 93 Context: # Bibliography 1. **Author, A. A.** (Year). *Title of the Book*. Publisher. 2. **Author, B. B.** (Year). *Title of the Article*. *Title of the Journal*, Volume(Issue), pages. DOI or URL 3. **Author, C. C.** (Year). *Title of the Thesis or Dissertation*. University. 4. **Author, D. D.** (Year). *Title of the Report*. Publisher. DOI or URL 5. **Author, E. E.** (Year). *Title of the Website*. Retrieved from URL 6. **Author, F. F.** (Year). *Title of the Conference Paper*. In *Proceedings of the Conference Name* (pp. pages). Publisher. DOI or URL ### Additional References - **Author, G. G.** (Year). *Title of the Book*. Publisher. - **Author, H. H.** (Year). *Title of the Article*. *Title of the Journal*, Volume(Issue), pages. DOI or URL ### Notes - Ensure all references are formatted consistently. - Verify the accuracy of all citations and links. #################### File: A%20MACHINE%20MADE%20THIS%20BOOK%20ten%20sketches%20of%20computer%20science%20-%20JOHN%20WHITINGTON%20%28PDF%29.pdf Page: 70 Context: # Chapter 5. Typing it In ## C. L. SHOLES ### Type-Writing Machine **No. 207,559** **Patented Aug. 27, 1878**  *Rather more recognisable as a typewriter to modern eyes, with four rows of keys in roughly the same arrangement as computer keyboards today and the paper clipped into a rotating drum, allowing for secure and reliable placement of each row. There is a foot pedal for advancing to the next line.* Image Analysis: Here's a detailed examination based on the provided visual content: ### 1. Localization and Attribution: - **Image 1**: The image is centered on the page, showcasing a typewriting machine patented by C. L. Sholes in 1878. ### 2. Object Detection and Classification: - **Objects Detected**: - **Typewriting Machine**: The main object, classified as a mechanical device. - **Keys**: Four rows identified depicted as levers for inputting characters. - **Paper Drum**: Mechanism for holding the paper. - **Foot Pedal**: Description includes a component for advancing paper. - **Key Features**: The typewriting machine includes a complex arrangement of keys similar to modern keyboards, along with a visible paper mechanism. ### 3. Scene and Activity Analysis: - **Scene Description**: The image depicts a mechanical device used for typing, with intricate details outlining its function. - **Main Actors and Actions**: The machine symbolizes the action of typing as facilitated by the keys and foot pedal, although no human activity is depicted. ### 4. Text Analysis: - **Text Detected**: - "C. L. SHOLES. Type-Writing Machine." - "No. 207,559. Patented Aug. 27, 1878." - **Content Significance**: The text identifies the inventor and patent information, emphasizing the historical significance of the invention in the context of writing technologies. ### 5. Diagram and Chart Analysis: - The image serves as a diagram of the typewriting machine rather than a chart or graph. ### 6. Product Analysis: - **Product Description**: The depicted typewriting machine is characterized by: - **Main Features**: Four rows of keys, a rotating drum for paper placement. - **Materials**: Likely constructed from metal and wood, typical of 19th-century machinery. - **Colors**: The illustration is monochrome, typical of patent drawings, focusing on mechanics rather than color. ### 7. Anomaly Detection: - No significant anomalies are present; the image is a clear technical illustration. ### 8. Color Analysis: - **Dominant Colors**: The image is in black and white, emphasizing structural details rather than color. ### 9. Perspective and Composition: - **Perspective**: The image is taken from a frontal view, providing a clear representation of the machine's design. - **Composition**: The arrangement of components like keys and the drum are central to understanding its function. ### 10. Contextual Significance: - The image contributes to discussions on the evolution of writing technologies, showcasing early mechanical design that laid the groundwork for modern typewriters and keyboards. ### 11. Metadata Analysis: - **Metadata**: No metadata is available for analysis, but context suggests it’s a patent illustration from the late 19th century. ### 12. Graph and Trend Analysis: - No graphs are present in the image. ### 13. Graph Numbers: - No graphed data to present. ### Additional Aspects: - **Ablaufprozesse (Process Flows)**: The typewriting machine suggests a process flow for typing text mechanically. - **Prozessbeschreibungen (Process Descriptions)**: The machine's operation involves pressing keys which activate mechanical arms to imprint characters onto paper. - **Typen Bezeichnung (Type Designations)**: The keys are categorized by function, resembling modern keyboard layouts. - **Trend and Interpretation**: The design reflects trends in mechanical innovation during the late 19th century, moving towards more user-friendly writing devices. - **Tables**: No tables are included in the image. This detailed analysis highlights various components and attributes of the typewriting machine illustrated in the provided visual content. #################### File: A%20First%20Encounter%20with%20Machine%20Learning%20-%20Max%20Welling%20%28PDF%29.pdf Page: 87 Context: # A.I. LAGRANGIANS AND ALL THAT Hence, the “sup” and “inf” can be interchanged if strong duality holds, hence the optimal solution is a saddle-point. It is important to realize that the order of maximization and minimization matters for arbitrary functions (but not for convex functions). Try to imagine a “V” shaped valley which runs diagonally across the coordinate system. If we first maximize over one direction, keeping the other direction fixed, and then minimize the result we end up with the lowest point on the rim. If we reverse the order we end up with the highest point in the valley. There are a number of important necessary conditions that hold for problems with zero duality gap. These Karush-Kuhn-Tucker conditions turn out to be sufficient for convex optimization problems. They are given by: ∇f(x*) + ∑_i λᵢ∇fᵢ(x*) + ∑_j νᵢ∇hᵢ(x*) = 0 (A.8) fᵢ(x*) ≤ 0 (A.9) hⱼ(x*) = 0 (A.10) λᵢ ≥ 0 (A.11) λᵢfᵢ(x*) = 0 (A.12) The first equation is easily derived because we already saw that p* = infₓ Lₗₚ(x, λ, ν) and hence all the derivatives must vanish. This condition has a nice interpretation as a “balancing of forces”. Imagine a ball rolling down a surface defined by f(x) (i.e. you are doing gradient descent to find the minimum). The ball gets blocked by a wall, which is the constraint. If the surface and constraint is convex then if the ball doesn’t move we have reached the optimal solution. At that point, the forces on the ball must balance. The first term represents the force of the ball against the wall due to gravity (the ball is still on a slope). The second term represents the reaction force of the wall in the opposite direction. The λ represents the magnitude of the reaction force, which needs to be higher if the surface slopes more. We say that this constraint is “active”. Other constraints which do not exert a force are “inactive” and have λ = 0. The latter statement can be read from the last KKT condition which we call “complementary slackness”. It says that either fᵢ(x) = 0 (the constraint is saturated and hence active) in which case λ is free to take on a non-zero value. However, if the constraint is inactive: fᵢ(x) ≤ 0, then λ must vanish. As we will see soon, the active constraints will correspond to the support vectors in SVMs! #################### File: A%20MACHINE%20MADE%20THIS%20BOOK%20ten%20sketches%20of%20computer%20science%20-%20JOHN%20WHITINGTON%20%28PDF%29.pdf Page: 83 Context: # Chapter 6. Saving Space We are down to 880 characters, a reduction of about 10%, compared with the original. The top 100 words in English are known to cover about half of the printed words, in general. We have not quite achieved that in this example. Let us try counting the number of each character in our text to see if we can take advantage of the fact that some letters are more common than others (our current method makes no use of the fact that, for example, spaces are very common): | Character | Frequency | |-----------|-----------| | space | 30 | | e | 24 | | t | 19 | | p | 10 | | a | 19 | | i | 19 | | s | 19 | | k | 5 | | j | 4 | | T | 4 | | h | 3 | | q | 3 | | x | 2 | | r | 15 | | W | 1 | | n | 13 | | l | 1 | | I | 1 | | 1 | 2 | | 0 | 1 | The space character is by far the most common (we say it has the highest frequency). The frequencies of the lower case letters are roughly what we might expect from recalling the value of Scrabble tiles; the punctuation characters are infrequent, and the capital letters very infrequent. We have talked about what a bit is, how 8 bits make a byte, and how one byte is sufficient to store a character (at least in English). Our original message is 975 bytes, or 975 x 8 = 7800 bits. We could encode each of the 33 characters we have found in our text using a different pattern of 6 bits, since 33 is less than 64, which is the number of 6-bit combinations 000000 through 111111 (the number of 5-bit combinations is 32, which is not quite enough). This would reduce our space from 975 × 8 = 7800 bits. However, we would have wasted much of the possible set of codes and taken no advantage of our knowledge of how frequently each character occurs. What we should like is a code which uses shorter bit patterns for more common characters, and longer bit patterns for less common ones. Let us write out the beginnings of such a code: | Character | Code | |-----------|------| | space | 0 | | e | 1 | #################### File: A%20MACHINE%20MADE%20THIS%20BOOK%20ten%20sketches%20of%20computer%20science%20-%20JOHN%20WHITINGTON%20%28PDF%29.pdf Page: 139 Context: # Chapter 9. Our Typeface ## Ligatures IJ Qu Th æ fi fl ff ffi fk fkk fj sp st tt tz ch ck ct Next are the **Small Caps**, which are capital letters set to the same height as lowercase letters. You can see examples of Small Caps in the front matter of this book (the parts before the first chapter). Notice that the small caps are not just scaled-down versions of the ordinary capitals – having the same general weight, they may be used alongside them. ## Small Caps Next, we have accented letters, of which only a tiny portion are shown here. Accents attach in different places on each letter, so many typefaces contain an accented version of each common letter-accent pair, together with separate accent marks which can be combined with other letters as required for more esoteric uses. Ä À Å Á Â Ç ä à â á ã ç Finally, here are some of the many other glyphs in Palatino, for currency symbols and so forth, and some of the punctuation: @ £ $ ¥ ⎕ ¶ † © #################### File: A%20MACHINE%20MADE%20THIS%20BOOK%20ten%20sketches%20of%20computer%20science%20-%20JOHN%20WHITINGTON%20%28PDF%29.pdf Page: 199 Context: # INDEX - Joint Photographic Experts Group, 75 - JPEG, 75 - justification, - full, 136, 137 - Kafka, - Franz, 135 - kerning, 127, 136 - keyboard, 27, 53 - keyword, 43 - laser printer, 4 - Latin alphabet, 61 - leading, 136 - ligature, 50, 124 - line, - anti-aliased, 8 - drawings, 5 - line feed, 31 - Linear A, 39 - lines per inch, 108 - lining numbers, 124 - Linotype, 123 - list, 88 - reversing, 90 - sorting, 91 - lossy compression, 74 - Louis Steinberg, 118 - lpi, 108 - mark-up, 33 - mezzotint, 102 - microtypography, 139 - Modern Greek, 61, 124 - monitor, 6 - negative, 106 - newspaper, 3 - newsprint, 3 - niello, 102 - non-zero rule, 24 - old style numbers, 124 - operandi, 85 - operator, 84 - optical font size, 128 - OR, 51 - ordered either, 114 - origin, 2 - orphan, 139 - output, 27 - Palatino, 15, 123 - paragraph, 135 - parameter, 43 - parentheses - in an expression, 82 - path, 18 - containing a hole, 23 - filling, 24 - self-crossing, 24 - pattern, 51 - Paul de Casteljau, 17 - PDF file, 3 - photograph, 97, 106 - phototypesetting, 144 - Pierre Bézier, 17 - Pierre de Fermat, 1 - Pinyin, 61 - pixel, 3, 15 - plate, 101 - point, 2 - Polybius, 27 - position, 1 - prefix, 70 - program, 43, 81 - pseudocode, 43 - pt, 2 - QWERTY keyboard, 58 - ragged-right, 137 - Rembrandt van Rijn, 104 - Remington & Sons, 53 - René Descartes, 1 #################### File: A%20MACHINE%20MADE%20THIS%20BOOK%20ten%20sketches%20of%20computer%20science%20-%20JOHN%20WHITINGTON%20%28PDF%29.pdf Page: 132 Context: # Chapter 8. Grey Areas  Our small-dot ordered dither patterns, suitable for on-screen use where pixels are clearly defined (unlike ink on paper), are not too bad. They do the job of creating the impression of grey shades where only black and white exist. However, the regular patterns of dots can be distracting; we see those patterns instead of the image, since our eyes are drawn to regular features. The technique of error diffusion leads to a better result than ordered dithering, with fewer distracting patterns. This method was invented in 1976 by Robert W. Floyd and his student Louis Steinberg at Stanford University. Say that we have an image made up of greys numbered between 0% (ink white) and 100% ink (black) like the one in this diagram—unavoidably, we shall have to use a somewhat small example: ``` 50 20 70 40 30 70 50 40 90 ``` We proceed pixel by pixel, starting at the top left, dealing with a row of pixels in order and then moving on to the next row, until we have looked at the whole image. For each pixel, we first decide whether to paint it black or white in the final image. If it is 50 percent or more black, we make that pixel black; if it is less than 50 percent, we make it white. We write this value to the final image. Now, we consider the error inherent in that choice—that is to say, how much too white or too black we were forced to make the pixel due to only having fully white and fully black available. For example, on the first pixel, we would choose to place a 100% black pixel, and the original value was 50%, so we were forced to make it 50% too black. We redistribute this error to some of the surrounding pixels. Image Analysis: I'm unable to analyze the attached visual content, but I can help with any other questions you might have or provide information on related topics. Let me know how you’d like to proceed! #################### File: A%20MACHINE%20MADE%20THIS%20BOOK%20ten%20sketches%20of%20computer%20science%20-%20JOHN%20WHITINGTON%20%28PDF%29.pdf Page: 57 Context: # Chapter 4. Looking and Finding If we reach a situation where the word overrun the end of the text, we stop immediately – no further match can now be found: | | | | |----|----|----| | 1 | T | 0123456789123456789 | | | houses and horses and hares | | | W | 012345 | horses | Let us try to write our algorithm out as a computer program. A program is a set of instructions written in a language which is understandable and unambiguous, both to the computer and to the human being writing it. First, we shall assume that the part of the program for comparing the word with the text at a given position already exists; we will write it later. For now, we shall concentrate on the part which decides where to start, where to stop, moves the word along the text position-by-position, and prints out any positions which match. For reasons of conciseness, we won’t see a real programming language but a so-called pseudocode—that is to say, a language which closely resembles any number of programming languages, but contains only the complexities needed for describing the solution to our particular problem. First, we can define a new algorithm called `search`: ## define search pt We used the keyword `define` to say that we are defining a new algorithm. Keywords are things which are built into the programming language. We write them in bold. Then we gave it the name `search`. (This is arbitrary – we could have called it `cuisine` if we had wanted.) We give the name of the thing this algorithm will work with, called a parameter—in our case, pt, which will be a number keeping track of how far along the searching process we are (pt for position in text). We shall arrange for the value of pt to begin at 0 – the first character. Our algorithm doesn’t do anything yet – if we asked the computer to run it, nothing would happen. Now, what we should like to do is to make sure that we are not overrunning the end of the text—if there can be no more matches. We are not overrunning if the position pt added to the length of the word w is less than or equal to the length of the text T, that is to say between these two positions: #################### File: A%20MACHINE%20MADE%20THIS%20BOOK%20ten%20sketches%20of%20computer%20science%20-%20JOHN%20WHITINGTON%20%28PDF%29.pdf Page: 16 Context: # Chapter 1. Putting Marks on Paper We can assign units if we like, such as centimetres or inches, to define what these "lengths" are. In publishing, we like to use a little unit called a point or pt, which is 1/72 of an inch. This is convenient because it allows us to talk mostly using whole numbers (it is easier to talk about 450pt than about 6.319 inches). We need such small units because the items on our page are quite small and must be carefully positioned (look at the writing on this page, and see how each tiny little shape representing a character is so carefully placed). Here is how an A4 page (which is about 595 pts wide and about 842 pts tall) might look: ``` y 800 ┤ │ 600 ┤ │ 400 ┤ │ 200 ┤ │ 0 ┼─────────── 0 200 400 600 x ``` You can see that the chapter heading "Chapter 1" begins at (80, 630). Notice that the coordinates of the bottom left of the page (called the origin) are, of course, (0, 0). The choice of the bottom left as our origin is somewhat arbitrary – one could make an argument that the top left point, with vertical positions measured downwards, is a more appropriate choice, at least in the West where we read top to bottom. Of course, one could also have the origin at the top right or bottom right, with horizontal positions measuring leftward. We shall be using such coordinates to describe the position and shape of each text, each word, each paragraph, as well as any drawings or photographs to be placed on the page. We will see how lines can be drawn between coordinates, and how to make the elegant curves which form the letters in a typeface. Once we have determined what shapes we wish to put on each page, we must consider the final form of our document. You may #################### File: A%20MACHINE%20MADE%20THIS%20BOOK%20ten%20sketches%20of%20computer%20science%20-%20JOHN%20WHITINGTON%20%28PDF%29.pdf Page: 146 Context: ``` # Chapter 9. Our Typeface | Character | Description | |-----------|-----------------------------| | ≤ | Less than or equal to | | ≥ | Greater than or equal to | | ∧ | Logical AND | | ∨ | Logical OR | | ∑ | Summation | | Ω | Omega symbol | | ƒ | Function notation | | ¢ | Cent symbol | | £ | Pound symbol | | ¥ | Yen symbol | | § | Section sign | | ¶ | Paragraph sign | | © | Copyright symbol | | ® | Registered trademark symbol | | ™ | Trademark symbol | | ¨ | Diaeresis | ## Lowercase Letters | Letter | Character | |---------|-----------| | a | a | | b | b | | c | c | | ... | ... | ## Uppercase Letters | Letter | Character | |---------|-----------| | A | A | | B | B | | C | C | | ... | ... | ## Notes Palatino, glyphs 501–1000. (The blank ones are spaces of various widths and types.) ``` #################### File: A%20MACHINE%20MADE%20THIS%20BOOK%20ten%20sketches%20of%20computer%20science%20-%20JOHN%20WHITINGTON%20%28PDF%29.pdf Page: 148 Context: # Chapter 9. Our Typeface ## Problems **Solutions on page 166.** The following words have been badly spaced. Photocopy or print out this page, cut out the letters, and then paste them onto another page along a straight line, finding an arrangement which is neither too tight nor too loose. 1. P a l a t i n o 2. A V E R S I O N 3. C o n j e c t u r e #################### File: A%20MACHINE%20MADE%20THIS%20BOOK%20ten%20sketches%20of%20computer%20science%20-%20JOHN%20WHITINGTON%20%28PDF%29.pdf Page: 105 Context: # Chapter 7. Doing Sums check that it works (again, in our shortened form of diagram): 1. `reverse [1, 2, 3]` - `=⇒ reverse [2, 3] [+1]` 2. `=⇒ reverse [3] [2] + [1]` 3. `=⇒ (([3] * reverse []) + [1])` 4. `=⇒ ([3] + [1]) + [2]` 5. `=⇒ [3, 2, 1]` Let us approach a more complicated problem. How might we sort a list into numerical order, whatever order it is in to start with? For example, we want to sort `[53, 9, 2, 6, 19]` to produce `[2, 6, 9, 19, 53]`. The problem is a little unapproachable—it seems rather complex. One way to begin is to see if we can solve the simplest part of the problem. Well, just like reverse, sorting a list of length zero is easy—there is nothing to do: ``` sort l = if l = [] then [] else . . . ``` If the list has length greater than zero, it has a head and a tail. Assume for a moment that the tail is already sorted—then we just need to insert the head into the tail at the correct position and the whole list will be sorted. Here is a definition for sort, assuming we have an insert function (we shall concoct insert in a moment): ``` sort l = if l = [] then [] else insert (head l) (sort (tail l)) ``` If the list is empty, we do nothing; otherwise, we insert the head of the list into its sorted tail. Assuming sorted exists, here is the whole evaluation of our sorting procedure on the list `[53, 9, 2, 6, 19]`, showing only uses of sort and insert for brevity: - `sort [53, 9, 2, 6, 19]` - `=⇒ insert 53 (sort [9, 2, 6, 19])` - `=⇒ insert 53 (insert 9 (sort [2, 6, 19]))` - `=⇒ insert 53 (insert 9 (insert 2 (sort [6, 19])))` - `=⇒ insert 53 (insert 9 (insert 2 (insert 6 (sort [19]))))` - `=⇒ insert 53 (insert 9 (insert 2 (insert 6 (insert 19 []))))` - `=⇒ insert 53 (insert 9 (insert 2 (insert 6 [19])))` - `=⇒ insert 53 (insert 9 (insert 2 [6, 19]))` - `=⇒ insert 53 (insert 9 [2, 6, 19])` - `=⇒ insert 53 [2, 6, 9, 19]` - `=⇒ [2, 6, 9, 19, 53]` #################### File: A%20MACHINE%20MADE%20THIS%20BOOK%20ten%20sketches%20of%20computer%20science%20-%20JOHN%20WHITINGTON%20%28PDF%29.pdf Page: 183 Context: # Further Reading There follows a list of interesting books for each chapter. Some are closely related to the chapter contents, some tangentially. The level of expertise required to understand each of them varies quite a bit, but do not be afraid to read books you do not understand all of, especially if you can obtain or borrow them at little cost. ## Chapter 1 1. **Computer Graphics: Principles and Practice** James D. Foley, Andries van Dam, Steven K. Feiner, and John F. Hughes. Published by Addison Wesley (second edition, 1995). ISBN 0201848406. 2. **Contemporary Newspaper Design: Shaping the News in the Digital Age – Typography & Image on Modern Newsprint** John D. Berry and Roger Black. Published by Mark Batty (2007). ISBN 0972420432. ## Chapter 2 1. **A Book of Curves** E. H. Lockwood. Published by Cambridge University Press (1961). ISBN 0521044448. 2. **Fifty Typefaces That Changed the World: Design Museum Fifty** John L. Waters. Published by Conran (2013). ISBN 184901629X. 3. **Thinking with Type: A Critical Guide for Designers, Writers, Editors, and Students** Ellen Lupton. Published by Princeton Architectural Press (second edition, 2010). ISBN 1568989695. #################### File: A%20First%20Encounter%20with%20Machine%20Learning%20-%20Max%20Welling%20%28PDF%29.pdf Page: 27 Context: # 3.1 In a Nutshell Learning is all about generalizing regularities in the training data to new, yet unobserved data. It is not about remembering the training data. Good generalization means that you need to balance prior knowledge with information from data. Depending on the dataset size, you can entertain more or less complex models. The correct size of the model can be determined by playing a compression game. Learning = generalization = abstraction = compression. #################### File: A%20MACHINE%20MADE%20THIS%20BOOK%20ten%20sketches%20of%20computer%20science%20-%20JOHN%20WHITINGTON%20%28PDF%29.pdf Page: 79 Context: # Chapter 6 ## Saving Space As computers get ever faster, we ask ever more of them: a higher-resolution film streamed in real time, a faster download, or the same experience on a mobile device over a slow connection as we have at home or in the office over a fast one. When we talk of efficiency, we are concerned with the time taken to do a task, the space required to store data, and knock-on effects such as how often we have to charge our device’s battery. And so we cannot simply say "things are getting faster all the time; we need not worry about efficiency." An important tool for reducing the space information takes up (and so, increasing the speed with which it can be moved around) is **compression**. The idea is to process the information in such a way that it becomes smaller, but also so that it may be *decompressed* — that is to say, the process must be reversible. Imagine we want to send a coffee order. Instead of writing “Four espressos, two double espressos, a cappuccino, and two lattes,” we might write “42EDC2L.” This relies, of course, on the person to whom we are sending the order knowing how to decompress it. The instructions for decomposing might be longer than the message itself, but if we are sending similar messages each day, we need only share the instructions once. We have reduced the message from 67 characters to 7, making it almost ten times smaller. This sort of compression happens routinely, and it is really just a matter of choosing a better representation for storing a particular kind of information. It tends to be more successful the more uniform the data is. Can we come up with a compression method which works for any data? If not, what about one which works well? #################### File: A%20First%20Encounter%20with%20Machine%20Learning%20-%20Max%20Welling%20%28PDF%29.pdf Page: 6 Context: # PREFACE About 60% correct on 100 categories, the fact that we pull it off seemingly effortlessly serves as a "proof of concept" that it can be done. But there is no doubt in my mind that building truly intelligent machines will involve learning from data. The first reason for the recent successes of machine learning and the growth of the field as a whole is rooted in its multidisciplinary character. Machine learning emerged from AI but quickly incorporated ideas from fields as diverse as statistics, probability, computer science, information theory, convex optimization, control theory, cognitive science, theoretical neuroscience, physics, and more. To give an example, the main conference in this field is called: **advances in neural information processing systems**, referring to information theory and theoretical neuroscience and cognitive science. The second, perhaps more important reason for the growth of machine learning is the exponential growth of both available data and computer power. While the field is built on theory and tools developed statistics, machine learning recognizes that the most exciting progress can be made to leverage the enormous flood of data that is generated each year by satellites, sky observatories, particle accelerators, the human genome project, banks, the stock market, the army, seismic measurements, the internet, video, scanned text, and so on. It is difficult to appreciate the exponential growth of data that our society is generating. To give an example, a modern satellite generates roughly the same amount of data as previous satellites produced together. This insight has shifted the attention from highly sophisticated modeling techniques on small datasets to more basic analysis on much larger datasets (the latter sometimes called **data-mining**). Hence the emphasis shifted to algorithmic efficiency, and as a result, many machine learning faculty (like myself) can typically be found in computer science departments. To give some examples of recent successes of this approach, one would only have to turn on one computer and perform an internet search. Modern search engines do not run terribly sophisticated algorithms, but they manage to store and sift through almost the entire content of the internet to return sensible search results. There has also been much success in the field of machine translation, not because a new model was invented but because many more translated documents became available. The field of machine learning is multifaceted and expanding fast. To sample a few sub-disciplines: statistical learning, kernel methods, graphical models, artificial neural networks, fuzzy logic, Bayesian methods, and so on. The field also covers many types of learning problems, such as supervised learning, unsupervised learning, semi-supervised learning, active learning, reinforcement learning, etc. I will only cover the most basic approaches in this book from a highly-per. #################### File: A%20MACHINE%20MADE%20THIS%20BOOK%20ten%20sketches%20of%20computer%20science%20-%20JOHN%20WHITINGTON%20%28PDF%29.pdf Page: 178 Context: # Solutions ## 2 | | | | | | |-----|-----|-----|-----|-----| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |  ### Grayscale Palette | Color | |-------| |  | |  | |  | |  | |  | |  | |  | |  | Image Analysis: I can't analyze or describe the visual content in the image you provided. If you have other questions or need assistance, feel free to ask! #################### File: A%20First%20Encounter%20with%20Machine%20Learning%20-%20Max%20Welling%20%28PDF%29.pdf Page: 38 Context: # CHAPTER 6. THE NATIVE BAYESIAN CLASSIFIER An example of the traffic that it generates: the University of California Irvine receives on the order of 2 million spam emails a day. Fortunately, the bulk of these emails (approximately 97%) is filtered out or dumped into your spam box and will reach your attention. How is this done? Well, it turns out to be a classic example of a classification problem: spam or ham, that’s the question. Let’s say that spam will receive a label 1 and ham a label 0. Our task is to label each new email with either 0 or 1. What are the attributes? Rephrasing this question, what would you measure in an email to see if it is spam? Certainly, if I read "viagra" in the subject I would stop right there and dump it in the spam box. What else? Here are a few: "enlargement," "cheap," "buy," "pharmacy," "money," "loan," "mortgage," "credit," and so on. We can build a dictionary of words that we can detect in each email. This dictionary could also include word phrases such as "buy now," "penis enlargement," one can make phrases as sophisticated as necessary. One could measure whether the words or phrases appear at least once or one could count the actual number of times they appear. Spammers know about the way these spam filters work and counteract by slight misspellings of certain key words. Hence we might also want to detect words like "viagra" and so on. In fact, a small arms race has ensued where spam filters and spam generators find tricks to counteract the tricks of the "opponent." Putting all these subtleties aside for a moment we’ll simply assume that we measure a number of these attributes for every email in a dataset. We’ll also assume that we have spam/ham labels for these emails, which were acquired by someone removing spam emails by hand from his/her inbox. Our task is then to train a predictor for spam/ham labels for future emails where we have access to attributes but not to labels. The NB model is what we call a "generative" model. This means that we imagine how the data was generated in an abstract sense. For emails, this works as follows: in an imaginary entity first decides how many spam and ham emails it will generate on a daily basis. Say, it decides to generate 40% spam and 60% ham. We will assume this doesn’t change with time (of course it does, but we will make this simplifying assumption for now). It will then decide what the chance is that a certain word appears `x` times in a spam email. For example, the word "viagra" has a chance of 96% to not appear at all, 1% to appear once, 0.9% to appear twice, etc. These probabilities are clearly different for spam and ham. "Viagra" should have a much smaller probability to appear in a ham email (but I could of course; consider I send this text to my publisher by email). Given these probabilities, we can then go on and try to generate emails that actually look like real emails, i.e., with proper sentences, but we won’t need that in the following. Instead we make the simplifying assumption that email consists of "a bag of words," in random order. #################### File: A%20MACHINE%20MADE%20THIS%20BOOK%20ten%20sketches%20of%20computer%20science%20-%20JOHN%20WHITINGTON%20%28PDF%29.pdf Page: 165 Context: # Solutions So we have the new Bézier curves AEHJ and JIFD as before:  ## 3 **With the even-odd rule:** - Square 1 - Square 2 - Square 3 - Square 4 **With the non-zero rule:** - Square 5 - Square 6 --- # Chapter 3 **1** 32-11-42-54-23-11-14-11-31-24-44-44-31-15-31-11-32-12. There are 18 characters in the message, and so 36 numbers to transmit (though in Polybius's system of torches, these would be done two at a time, so just 18 distinct actions). We might use Z for the space character, since it doesn't appear often in normal text. We could use ZZZ for the end of message. ########## """QUERY: Please summarize the whole context. It is important that you include a summary for each file. All files should be included, so please make sure to go through the entire context""" Consider the chat history for relevant information. If query is already asked in the history double check the correctness of your answer and maybe correct your previous mistake. If you find information separated by a | in the context, it is a table formatted in Markdown - the whole context is formatted as md structure. If the client ask for a formatting structure follow his advise. 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Page 105, A%20MACHINE%20MADE%20THIS%20BOOK%20ten%20sketches%20of%20computer%20science%20-%20JOHN%20WHITINGTON%20%28PDF%29.pdf - Page 183, A%20First%20Encounter%20with%20Machine%20Learning%20-%20Max%20Welling%20%28PDF%29.pdf - Page 27, A%20MACHINE%20MADE%20THIS%20BOOK%20ten%20sketches%20of%20computer%20science%20-%20JOHN%20WHITINGTON%20%28PDF%29.pdf - Page 79, A%20First%20Encounter%20with%20Machine%20Learning%20-%20Max%20Welling%20%28PDF%29.pdf - Page 6, A%20MACHINE%20MADE%20THIS%20BOOK%20ten%20sketches%20of%20computer%20science%20-%20JOHN%20WHITINGTON%20%28PDF%29.pdf - Page 178, A%20First%20Encounter%20with%20Machine%20Learning%20-%20Max%20Welling%20%28PDF%29.pdf - Page 38, A%20MACHINE%20MADE%20THIS%20BOOK%20ten%20sketches%20of%20computer%20science%20-%20JOHN%20WHITINGTON%20%28PDF%29.pdf - Page 165 ================================================== **Elapsed Time: 11.18 seconds** ================================================== FINAL ANSWER Answer: