Based Neural Network for Querying Tables Using Natural Language

Querying relational data structures using natural languages has long been a dream of technologists in the space. With the recent advancements in deep learning and natural language understanding(NLU), we have seen attempts by mainstream software packages such as Tableau or Salesforce.com to incorporate natural language to interact with their datasets. However, those options remain extremely limited, constrained specific data structures and hardly resemble a natural language interaction. At the same time, we continue hitting milestones in question-answering models such as Google’s BERT or Microsoft’s Turing-NG. Could we leverage those advancements to interact with tabular data? Recently, Google Research unveiled TAPAS( Table parser), a model based on the BERT architecture that process questions and answers against tabular datasets.

Interacting with tabular data is natural language is one of those scenarios that looks conceptually trivial and results in a nightmare in the real world. Most attempts to solve this issue have been based on semantic parsing methods that process a natural language sentence and generate the corresponding SQL. That approach works in very constrained scenarios but is hardly scalable to real natural language interactions. Let’s take the following example the following datasets of American wrestling champions. The table to the right represents some possible questions that can be executed against that dataset.

Source: https://ai.googleblog.com/2020/04/using-neural-networks-to-find-answers.html

Some questions such as #1: “Which wrestler had the most number of reigns?” directly maps to a SQL sentence “SELECT TOP Name ORDER BY No. Of Reigns DESC”. Those queries are easy to process by a semantic parser. However, queries such as #5: “_Which of the following wrestlers were ranked in the bottom 3? Out of these, who had more than one reign?” _are conversational natural and more difficult to process. Additionally, if you factor in common conversational elements such as ambiguity, long-form sentences or synonymous, just to list a few, you start getting a picture of the complexity of using natural language to interact with tabular data.

The TAPAS Approach

Instead of creating a model that is constrained to a specific table structure, Google decided to follow a more holistic approach building a neural network that can be adapted to any form of a tabular dataset. To accomplish that, Google decided to based TAPAS in its famous BERT encoder architecture that set new records for natural language models a couple of years ago. TAPAS extends the BERT model in four fundamental areas:

1. Additional Embeddings
2. Cell Selection
3. Aggregation-Operation-Prediction
4. Inference

Additional Embeddings

The most notable addition to the base BERT model is the use of extra embeddings for encoding the textual input. Tapas leverages learned embeddings for the row and column indexes as well as for one special rank index that represents the order of elements in numerical columns. More specifically, TAPAS adds the following types of positional embeddings:

• **Position ID: **Just like BERT, this embedding represents the index of the token in the flattened sequence.
• Segment ID: Encodes a table header as 0 and a table cell as 1.
• **Column/Row ID: **The index of the column or row containing the token.
• Rank ID: This embedding is designed to process superlative questions from numeric values. If the cell values are numbers, this embedding sorts them and assign them a value based on their numeric rank.

• Previous Answer: This embedding is designed for scenarios such as question #5 that combines multiple questions. Specifically, this embedding indicates whether a cell token was the answer to a previous question.

#neural networks

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Pdf2gerb: Perl Script Converts PDF Files to Gerber format

pdf2gerb

Perl script converts PDF files to Gerber format

Pdf2Gerb generates Gerber 274X photoplotting and Excellon drill files from PDFs of a PCB. Up to three PDFs are used: the top copper layer, the bottom copper layer (for 2-sided PCBs), and an optional silk screen layer. The PDFs can be created directly from any PDF drawing software, or a PDF print driver can be used to capture the Print output if the drawing software does not directly support output to PDF.

The general workflow is as follows:

1. Design the PCB using your favorite CAD or drawing software.
2. Print the top and bottom copper and top silk screen layers to a PDF file.
3. Run Pdf2Gerb on the PDFs to create Gerber and Excellon files.
4. Use a Gerber viewer to double-check the output against the original PCB design.
5. Make adjustments as needed.
6. Submit the files to a PCB manufacturer.

Please note that Pdf2Gerb does NOT perform DRC (Design Rule Checks), as these will vary according to individual PCB manufacturer conventions and capabilities. Also note that Pdf2Gerb is not perfect, so the output files must always be checked before submitting them. As of version 1.6, Pdf2Gerb supports most PCB elements, such as round and square pads, round holes, traces, SMD pads, ground planes, no-fill areas, and panelization. However, because it interprets the graphical output of a Print function, there are limitations in what it can recognize (or there may be bugs).

See docs/Pdf2Gerb.pdf for install/setup, config, usage, and other info.

---

pdf2gerb_cfg.pm

#Pdf2Gerb config settings:
#Put this file in same folder/directory as pdf2gerb.pl itself (global settings),
#or copy to another folder/directory with PDFs if you want PCB-specific settings.
#There is only one user of this file, so we don't need a custom package or namespace.
#NOTE: all constants defined in here will be added to main namespace.
#package pdf2gerb_cfg;

use strict; #trap undef vars (easier debug)
use warnings; #other useful info (easier debug)


##############################################################################################
#configurable settings:
#change values here instead of in main pfg2gerb.pl file

use constant WANT_COLORS => ($^O !~ m/Win/); #ANSI colors no worky on Windows? this must be set < first DebugPrint() call

#just a little warning; set realistic expectations:
#DebugPrint("${\(CYAN)}Pdf2Gerb.pl ${\(VERSION)}, $^O O/S\n${\(YELLOW)}${\(BOLD)}${\(ITALIC)}This is EXPERIMENTAL software.  \nGerber files MAY CONTAIN ERRORS.  Please CHECK them before fabrication!${\(RESET)}", 0); #if WANT_DEBUG

use constant METRIC => FALSE; #set to TRUE for metric units (only affect final numbers in output files, not internal arithmetic)
use constant APERTURE_LIMIT => 0; #34; #max #apertures to use; generate warnings if too many apertures are used (0 to not check)
use constant DRILL_FMT => '2.4'; #'2.3'; #'2.4' is the default for PCB fab; change to '2.3' for CNC

use constant WANT_DEBUG => 0; #10; #level of debug wanted; higher == more, lower == less, 0 == none
use constant GERBER_DEBUG => 0; #level of debug to include in Gerber file; DON'T USE FOR FABRICATION
use constant WANT_STREAMS => FALSE; #TRUE; #save decompressed streams to files (for debug)
use constant WANT_ALLINPUT => FALSE; #TRUE; #save entire input stream (for debug ONLY)

#DebugPrint(sprintf("${\(CYAN)}DEBUG: stdout %d, gerber %d, want streams? %d, all input? %d, O/S: $^O, Perl: $]${\(RESET)}\n", WANT_DEBUG, GERBER_DEBUG, WANT_STREAMS, WANT_ALLINPUT), 1);
#DebugPrint(sprintf("max int = %d, min int = %d\n", MAXINT, MININT), 1); 

#define standard trace and pad sizes to reduce scaling or PDF rendering errors:
#This avoids weird aperture settings and replaces them with more standardized values.
#(I'm not sure how photoplotters handle strange sizes).
#Fewer choices here gives more accurate mapping in the final Gerber files.
#units are in inches
use constant TOOL_SIZES => #add more as desired
(
#round or square pads (> 0) and drills (< 0):
    .010, -.001,  #tiny pads for SMD; dummy drill size (too small for practical use, but needed so StandardTool will use this entry)
    .031, -.014,  #used for vias
    .041, -.020,  #smallest non-filled plated hole
    .051, -.025,
    .056, -.029,  #useful for IC pins
    .070, -.033,
    .075, -.040,  #heavier leads
#    .090, -.043,  #NOTE: 600 dpi is not high enough resolution to reliably distinguish between .043" and .046", so choose 1 of the 2 here
    .100, -.046,
    .115, -.052,
    .130, -.061,
    .140, -.067,
    .150, -.079,
    .175, -.088,
    .190, -.093,
    .200, -.100,
    .220, -.110,
    .160, -.125,  #useful for mounting holes
#some additional pad sizes without holes (repeat a previous hole size if you just want the pad size):
    .090, -.040,  #want a .090 pad option, but use dummy hole size
    .065, -.040, #.065 x .065 rect pad
    .035, -.040, #.035 x .065 rect pad
#traces:
    .001,  #too thin for real traces; use only for board outlines
    .006,  #minimum real trace width; mainly used for text
    .008,  #mainly used for mid-sized text, not traces
    .010,  #minimum recommended trace width for low-current signals
    .012,
    .015,  #moderate low-voltage current
    .020,  #heavier trace for power, ground (even if a lighter one is adequate)
    .025,
    .030,  #heavy-current traces; be careful with these ones!
    .040,
    .050,
    .060,
    .080,
    .100,
    .120,
);
#Areas larger than the values below will be filled with parallel lines:
#This cuts down on the number of aperture sizes used.
#Set to 0 to always use an aperture or drill, regardless of size.
use constant { MAX_APERTURE => max((TOOL_SIZES)) + .004, MAX_DRILL => -min((TOOL_SIZES)) + .004 }; #max aperture and drill sizes (plus a little tolerance)
#DebugPrint(sprintf("using %d standard tool sizes: %s, max aper %.3f, max drill %.3f\n", scalar((TOOL_SIZES)), join(", ", (TOOL_SIZES)), MAX_APERTURE, MAX_DRILL), 1);

#NOTE: Compare the PDF to the original CAD file to check the accuracy of the PDF rendering and parsing!
#for example, the CAD software I used generated the following circles for holes:
#CAD hole size:   parsed PDF diameter:      error:
#  .014                .016                +.002
#  .020                .02267              +.00267
#  .025                .026                +.001
#  .029                .03167              +.00267
#  .033                .036                +.003
#  .040                .04267              +.00267
#This was usually ~ .002" - .003" too big compared to the hole as displayed in the CAD software.
#To compensate for PDF rendering errors (either during CAD Print function or PDF parsing logic), adjust the values below as needed.
#units are pixels; for example, a value of 2.4 at 600 dpi = .0004 inch, 2 at 600 dpi = .0033"
use constant
{
    HOLE_ADJUST => -0.004 * 600, #-2.6, #holes seemed to be slightly oversized (by .002" - .004"), so shrink them a little
    RNDPAD_ADJUST => -0.003 * 600, #-2, #-2.4, #round pads seemed to be slightly oversized, so shrink them a little
    SQRPAD_ADJUST => +0.001 * 600, #+.5, #square pads are sometimes too small by .00067, so bump them up a little
    RECTPAD_ADJUST => 0, #(pixels) rectangular pads seem to be okay? (not tested much)
    TRACE_ADJUST => 0, #(pixels) traces seemed to be okay?
    REDUCE_TOLERANCE => .001, #(inches) allow this much variation when reducing circles and rects
};

#Also, my CAD's Print function or the PDF print driver I used was a little off for circles, so define some additional adjustment values here:
#Values are added to X/Y coordinates; units are pixels; for example, a value of 1 at 600 dpi would be ~= .002 inch
use constant
{
    CIRCLE_ADJUST_MINX => 0,
    CIRCLE_ADJUST_MINY => -0.001 * 600, #-1, #circles were a little too high, so nudge them a little lower
    CIRCLE_ADJUST_MAXX => +0.001 * 600, #+1, #circles were a little too far to the left, so nudge them a little to the right
    CIRCLE_ADJUST_MAXY => 0,
    SUBST_CIRCLE_CLIPRECT => FALSE, #generate circle and substitute for clip rects (to compensate for the way some CAD software draws circles)
    WANT_CLIPRECT => TRUE, #FALSE, #AI doesn't need clip rect at all? should be on normally?
    RECT_COMPLETION => FALSE, #TRUE, #fill in 4th side of rect when 3 sides found
};

#allow .012 clearance around pads for solder mask:
#This value effectively adjusts pad sizes in the TOOL_SIZES list above (only for solder mask layers).
use constant SOLDER_MARGIN => +.012; #units are inches

#line join/cap styles:
use constant
{
    CAP_NONE => 0, #butt (none); line is exact length
    CAP_ROUND => 1, #round cap/join; line overhangs by a semi-circle at either end
    CAP_SQUARE => 2, #square cap/join; line overhangs by a half square on either end
    CAP_OVERRIDE => FALSE, #cap style overrides drawing logic
};
    
#number of elements in each shape type:
use constant
{
    RECT_SHAPELEN => 6, #x0, y0, x1, y1, count, "rect" (start, end corners)
    LINE_SHAPELEN => 6, #x0, y0, x1, y1, count, "line" (line seg)
    CURVE_SHAPELEN => 10, #xstart, ystart, x0, y0, x1, y1, xend, yend, count, "curve" (bezier 2 points)
    CIRCLE_SHAPELEN => 5, #x, y, 5, count, "circle" (center + radius)
};
#const my %SHAPELEN =
#Readonly my %SHAPELEN =>
our %SHAPELEN =
(
    rect => RECT_SHAPELEN,
    line => LINE_SHAPELEN,
    curve => CURVE_SHAPELEN,
    circle => CIRCLE_SHAPELEN,
);

#panelization:
#This will repeat the entire body the number of times indicated along the X or Y axes (files grow accordingly).
#Display elements that overhang PCB boundary can be squashed or left as-is (typically text or other silk screen markings).
#Set "overhangs" TRUE to allow overhangs, FALSE to truncate them.
#xpad and ypad allow margins to be added around outer edge of panelized PCB.
use constant PANELIZE => {'x' => 1, 'y' => 1, 'xpad' => 0, 'ypad' => 0, 'overhangs' => TRUE}; #number of times to repeat in X and Y directions

# Set this to 1 if you need TurboCAD support.
#$turboCAD = FALSE; #is this still needed as an option?

#CIRCAD pad generation uses an appropriate aperture, then moves it (stroke) "a little" - we use this to find pads and distinguish them from PCB holes. 
use constant PAD_STROKE => 0.3; #0.0005 * 600; #units are pixels
#convert very short traces to pads or holes:
use constant TRACE_MINLEN => .001; #units are inches
#use constant ALWAYS_XY => TRUE; #FALSE; #force XY even if X or Y doesn't change; NOTE: needs to be TRUE for all pads to show in FlatCAM and ViewPlot
use constant REMOVE_POLARITY => FALSE; #TRUE; #set to remove subtractive (negative) polarity; NOTE: must be FALSE for ground planes

#PDF uses "points", each point = 1/72 inch
#combined with a PDF scale factor of .12, this gives 600 dpi resolution (1/72 * .12 = 600 dpi)
use constant INCHES_PER_POINT => 1/72; #0.0138888889; #multiply point-size by this to get inches

# The precision used when computing a bezier curve. Higher numbers are more precise but slower (and generate larger files).
#$bezierPrecision = 100;
use constant BEZIER_PRECISION => 36; #100; #use const; reduced for faster rendering (mainly used for silk screen and thermal pads)

# Ground planes and silk screen or larger copper rectangles or circles are filled line-by-line using this resolution.
use constant FILL_WIDTH => .01; #fill at most 0.01 inch at a time

# The max number of characters to read into memory
use constant MAX_BYTES => 10 * M; #bumped up to 10 MB, use const

use constant DUP_DRILL1 => TRUE; #FALSE; #kludge: ViewPlot doesn't load drill files that are too small so duplicate first tool

my $runtime = time(); #Time::HiRes::gettimeofday(); #measure my execution time

print STDERR "Loaded config settings from '${\(__FILE__)}'.\n";
1; #last value must be truthful to indicate successful load


#############################################################################################
#junk/experiment:

#use Package::Constants;
#use Exporter qw(import); #https://perldoc.perl.org/Exporter.html

#my $caller = "pdf2gerb::";

#sub cfg
#{
#    my $proto = shift;
#    my $class = ref($proto) || $proto;
#    my $settings =
#    {
#        $WANT_DEBUG => 990, #10; #level of debug wanted; higher == more, lower == less, 0 == none
#    };
#    bless($settings, $class);
#    return $settings;
#}

#use constant HELLO => "hi there2"; #"main::HELLO" => "hi there";
#use constant GOODBYE => 14; #"main::GOODBYE" => 12;

#print STDERR "read cfg file\n";

#our @EXPORT_OK = Package::Constants->list(__PACKAGE__); #https://www.perlmonks.org/?node_id=1072691; NOTE: "_OK" skips short/common names

#print STDERR scalar(@EXPORT_OK) . " consts exported:\n";
#foreach(@EXPORT_OK) { print STDERR "$_\n"; }
#my $val = main::thing("xyz");
#print STDERR "caller gave me $val\n";
#foreach my $arg (@ARGV) { print STDERR "arg $arg\n"; }

---

Download Details:

Author: swannman
Source Code: https://github.com/swannman/pdf2gerb

License: GPL-3.0 license

#perl 

Introduction to Structured Query Language SQL pdf

SQL stands for Structured Query Language. SQL is a scripting language expected to store, control, and inquiry information put away in social databases. The main manifestation of SQL showed up in 1974, when a gathering in IBM built up the principal model of a social database. The primary business social database was discharged by Relational Software later turning out to be Oracle.

Models for SQL exist. In any case, the SQL that can be utilized on every last one of the major RDBMS today is in various flavors. This is because of two reasons:

1. The SQL order standard is genuinely intricate, and it isn’t handy to actualize the whole standard.

2. Every database seller needs an approach to separate its item from others.

Right now, contrasts are noted where fitting.

#programming books #beginning sql pdf #commands sql #download free sql full book pdf #introduction to sql pdf #introduction to sql ppt #introduction to sql #practical sql pdf #sql commands pdf with examples free download #sql commands #sql free bool download #sql guide #sql language #sql pdf #sql ppt #sql programming language #sql tutorial for beginners #sql tutorial pdf #sql #structured query language pdf #structured query language ppt #structured query language

Based Neural Network for Querying Tables Using Natural Language

Querying relational data structures using natural languages has long been a dream of technologists in the space. With the recent advancements in deep learning and natural language understanding(NLU), we have seen attempts by mainstream software packages such as Tableau or Salesforce.com to incorporate natural language to interact with their datasets. However, those options remain extremely limited, constrained specific data structures and hardly resemble a natural language interaction. At the same time, we continue hitting milestones in question-answering models such as Google’s BERT or Microsoft’s Turing-NG. Could we leverage those advancements to interact with tabular data? Recently, Google Research unveiled TAPAS( Table parser), a model based on the BERT architecture that process questions and answers against tabular datasets.

Interacting with tabular data is natural language is one of those scenarios that looks conceptually trivial and results in a nightmare in the real world. Most attempts to solve this issue have been based on semantic parsing methods that process a natural language sentence and generate the corresponding SQL. That approach works in very constrained scenarios but is hardly scalable to real natural language interactions. Let’s take the following example the following datasets of American wrestling champions. The table to the right represents some possible questions that can be executed against that dataset.

Source: https://ai.googleblog.com/2020/04/using-neural-networks-to-find-answers.html

Some questions such as #1: “Which wrestler had the most number of reigns?” directly maps to a SQL sentence “SELECT TOP Name ORDER BY No. Of Reigns DESC”. Those queries are easy to process by a semantic parser. However, queries such as #5: “_Which of the following wrestlers were ranked in the bottom 3? Out of these, who had more than one reign?” _are conversational natural and more difficult to process. Additionally, if you factor in common conversational elements such as ambiguity, long-form sentences or synonymous, just to list a few, you start getting a picture of the complexity of using natural language to interact with tabular data.

The TAPAS Approach

Instead of creating a model that is constrained to a specific table structure, Google decided to follow a more holistic approach building a neural network that can be adapted to any form of a tabular dataset. To accomplish that, Google decided to based TAPAS in its famous BERT encoder architecture that set new records for natural language models a couple of years ago. TAPAS extends the BERT model in four fundamental areas:

1. Additional Embeddings
2. Cell Selection
3. Aggregation-Operation-Prediction
4. Inference

Additional Embeddings

The most notable addition to the base BERT model is the use of extra embeddings for encoding the textual input. Tapas leverages learned embeddings for the row and column indexes as well as for one special rank index that represents the order of elements in numerical columns. More specifically, TAPAS adds the following types of positional embeddings:

• **Position ID: **Just like BERT, this embedding represents the index of the token in the flattened sequence.
• Segment ID: Encodes a table header as 0 and a table cell as 1.
• **Column/Row ID: **The index of the column or row containing the token.
• Rank ID: This embedding is designed to process superlative questions from numeric values. If the cell values are numbers, this embedding sorts them and assign them a value based on their numeric rank.

• Previous Answer: This embedding is designed for scenarios such as question #5 that combines multiple questions. Specifically, this embedding indicates whether a cell token was the answer to a previous question.

#neural networks

No Code introduction to Neural Networks

The simple architecture explained

Neural networks have been around for a long time, being developed in the 1960s as a way to simulate neural activity for the development of artificial intelligence systems. However, since then they have developed into a useful analytical tool often used in replace of, or in conjunction with, standard statistical models such as regression or classification as they can be used to predict or more a specific output. The main difference, and advantage, in this regard is that neural networks make no initial assumptions as to the form of the relationship or distribution that underlies the data, meaning they can be more flexible and capture non-standard and non-linear relationships between input and output variables, making them incredibly valuable in todays data rich environment.

In this sense, their use has took over the past decade or so, with the fall in costs and increase in ability of general computing power, the rise of large datasets allowing these models to be trained, and the development of frameworks such as TensforFlow and Keras that have allowed people with sufficient hardware (in some cases this is no longer even an requirement through cloud computing), the correct data and an understanding of a given coding language to implement them. This article therefore seeks to be provide a no code introduction to their architecture and how they work so that their implementation and benefits can be better understood.

Firstly, the way these models work is that there is an input layer, one or more hidden layers and an output layer, each of which are connected by layers of synaptic weights¹. The input layer (X) is used to take in scaled values of the input, usually within a standardised range of 0–1. The hidden layers (Z) are then used to define the relationship between the input and output using weights and activation functions. The output layer (Y) then transforms the results from the hidden layers into the predicted values, often also scaled to be within 0–1. The synaptic weights (W) connecting these layers are used in model training to determine the weights assigned to each input and prediction in order to get the best model fit. Visually, this is represented as:

#machine-learning #python #neural-networks #tensorflow #neural-network-algorithm #no code introduction to neural networks

Natural Language Processing 

I came across a well-prepared dataset provided by Google, with 58 000 ‘carefully curated’ Reddit comments, labelled with one or more of 27 emotions, e.g. anger, confusion, love. Google had used this to train a BERT model, in which they had varying success in emotion detection depending on the type of comment. I thought it would be a great example to learn how to tweak Convolutional Neural Networks (CNNs) and Embedding to a Natural Language Processing (NLP) problem, and obtained decent accuracy for some emotions, but not as good of course as Google’s BERT.

This is not a tutorial — there are plenty of great tutorials on CNNs and Embedding out there. Instead, this is rather a complete code example, tackling multi-class multi-label classification, which was rather hard to find complete & free examples for.

Spoiler: My code doesn’t do as well as Google, who also provide their code in the above link. Their well-oiled BERT solution obtains around 46% F1 score, while I only obtain 42%. However, I didn’t spend too much time optimising my model, so there might easily be another 2 or 3 percent available through some optimisations.

More on the GoEmotions dataset

Firstly, kudos to the Google research team (actual paper here) for such a well-prepared and cleaned dataset, which you can obtain from the link.

The dataset, along with some other metadata, provides a reddit comment (“text”), along with a corresponding set of emotion labels:

Starting intuition

This is a complicated domain — different users display sentiment in different ways, and many of these comments are very short and contain two meanings, e.g. “That’s adorable asf” Secondly, some comments, such as “This video doesn’t even show the shoes he was wearing”, are completely neutral and labelled as such.

However, there are definitely clearly words and phrases which are giveaways to certain emotions. Some, such as love or excitement, are probably quite easy to detect. However, subtle emotions such as pride or curiosity, less so. So I expect to get some success, but certainly don’t expect to do better than Google. After all, these comments aren’t responses to “How do you feel?” — they are seemingly arbitrarily picked from reddit.

The code, explained

The python code obtaining 42% F1 score on the dataset is here. Once you obtain the dataset from Google, you can run it out of the box just by changing the path to the datasets, assuming you have all dependencies installed. However, training the Neural Network will take about 30 minutes to train on 15 epochs depending on your computing power.

Below, I’ll describe each section, excluding the imports.

Fetching the data

This is standard. We are fetching the data CSVs (which are separate by Google’s design), concatenating them, and choosing our input vector, X, and binary output, y. We are then splitting the data into training and test data. The dataset is very large — almost 20,000 comments. Therefore, many other train_test_split splits would have worked, too, but we can go with the standard practice.

After this, we now have roughly 20,000 mappings of comments (“I really love bitterballen!”) with their associated labelled emotions (admiration, amusement, anger, annoyance, approval, caring, confusion, curiosity, desire, disappointment, disapproval, disgust, embarrassment, excitement, fear, gratitude, grief, joy, love, nervousness, optimism, pride, realization, relief, remorse, sadness, surprise).

#neural-networks #machine-learning #convolutional-network #data-science #neural networks