1598933760
In the last Article we had seen all about neural network like History of neural network, Basic Building blocks of neural network, Real time use cases of neural network and also we had understood by basic example how neural network works. If you had not been to my last Article i highly recommend just have a look at the Article and after read this article because this is the link from the previous article. In my previous article i had covered all the basic terminologies which i will be using in this Article. This article will be completely programmatic using python and keras. I will be using very basic example and will show you how machine learns to predict. The link for my previous article is below, its been published over Analytics Vidhya
We will be using a simple example of conversion of Celsius to Fahrenheit in this Article but before that we will try to understand what exactly is Dense layer, because we will be using dense layer from keras to build our first network. To understand this we will assume a neural network with 1 input layer with 3 unit, **1 hidden layer **with 2 units and an output layer with 1 unit and the diagram that explains this assumed neural network is below. Units are nothing but the neurons. Each neuron is connected with all the neurons of previous layer, i mean all the neuron of input layer will be connected to the neurons of the hidden layer and each neuron of the hidden layers will be connected to the neurons of the output layer and we can have multiple hidden layers, but for simplicity in this example we have taken only 1 hidden layers. We can see in the below diagram all the neurons are fully connected with the neurons of the previous layers. This type of layers are fully connected or Dense layers.
Fully connected or dense layer
We had understood the concept of Fully connected or dense layer in neural network. So we need to understand if we are using the Dense layer in keras we simply stating the neurons are fully connected to the neurons of previous layer. To create the above discussed layer programmatically in Keras we will use below python code
Keras dense layer
The above code states that we have 1 hidden layer with 2 neurons. The no of neurons we used to specify as a unit and we used to pass as a parameter in the created layer in keras. Similarly for output layer we had specified unit as 1 that states that we have 1 neurons in the output layer. After creation of the hidden layer and output layer we generally build the model by passing the hidden layers and output layers as a parameter in Sequential API. If you want to know more about the Keras i will suggest just go through my earlier bogs i had explained it step by step.
We had understood the Dense layer. Now will try to understand what exactly this dense layer is doing in the network. To understand this will try to recall the the concepts that we had studied in the previous article, the concept of weights and biases. Consider we have x1, x2, x3 as the real input, a1 and a2 are the neurons at hidden layer and a3 are the neurons at output layers, in the previous neural network that we had discussed. As we discussed in fully connected layer each neuron is connected to the neurons of the previous layer so there will be weights associated with all the hidden and output layers so we will have 8 weights, 3 for 1 hidden layers a1 as we have 3 inputs and 3 for other hidden layers a2 and 2 for output layer as we have 2 hidden neurons. This can be understood by looking the below figure. During the execution of neural networks the weights and biases will be getting adjusted to best mach for the given input to the output. In the process of training the neural network only the weights and biases gets changes to fit the best value to fit the input and output that we used to feed the neural networks.
#keras #tensorflow #optimizer #python #deep-learning
1574339995
Description
Become a Python Programmer and learn one of employer’s most requested skills of 21st century!
This is the most comprehensive, yet straight-forward, course for the Python programming language on Simpliv! Whether you have never programmed before, already know basic syntax, or want to learn about the advanced features of Python, this course is for you! In this course we will teach you Python 3. (Note, we also provide older Python 2 notes in case you need them)
With over 40 lectures and more than 3 hours of video this comprehensive course leaves no stone unturned! This course includes tests, and homework assignments as well as 3 major projects to create a Python project portfolio!
This course will teach you Python in a practical manner, with every lecture comes a full coding screencast and a corresponding code notebook! Learn in whatever manner is best for you!
We will start by helping you get Python installed on your computer, regardless of your operating system, whether its Linux, MacOS, or Windows, we’ve got you covered!
We cover a wide variety of topics, including:
Command Line Basics
Installing Python
Running Python Code
Strings
Lists
Dictionaries
Tuples
Sets
Number Data Types
Print Formatting
Functions
Scope
Built-in Functions
Debugging and Error Handling
Modules
External Modules
Object Oriented Programming
Inheritance
Polymorphism
File I/O
Web scrapping
Database Connection
Email sending
and much more!
Project that we will complete:
Guess the number
Guess the word using speech recognition
Love Calculator
google search in python
Image download from a link
Click and save image using openCV
Ludo game dice simulator
open wikipedia on command prompt
Password generator
QR code reader and generator
You will get lifetime access to over 40 lectures.
So what are you waiting for? Learn Python in a way that will advance your career and increase your knowledge, all in a fun and practical way!
Basic knowledge
Basic programming concept in any language will help but not require to attend this tutorial
What will you learn
Learn to use Python professionally, learning both Python 2 and Python 3!
Create games with Python, like Tic Tac Toe and Blackjack!
Learn advanced Python features, like the collections module and how to work with timestamps!
Learn to use Object Oriented Programming with classes!
Understand complex topics, like decorators.
Understand how to use both the pycharm and create .py files
Get an understanding of how to create GUIs in the pycharm!
Build a complete understanding of Python from the ground up!
#Learn Python #Learn Python from Basic #Python from Basic to Advance #Python from Basic to Advance with Projects #Learn Python from Basic to Advance with Projects in a day
1667425440
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:
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 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"; }
Author: swannman
Source Code: https://github.com/swannman/pdf2gerb
License: GPL-3.0 license
1625843760
When installing Machine Learning Services in SQL Server by default few Python Packages are installed. In this article, we will have a look on how to get those installed python package information.
When we choose Python as Machine Learning Service during installation, the following packages are installed in SQL Server,
#machine learning #sql server #executing python in sql server #machine learning using python #machine learning with sql server #ml in sql server using python #python in sql server ml #python packages #python packages for machine learning services #sql server machine learning services
1598933760
In the last Article we had seen all about neural network like History of neural network, Basic Building blocks of neural network, Real time use cases of neural network and also we had understood by basic example how neural network works. If you had not been to my last Article i highly recommend just have a look at the Article and after read this article because this is the link from the previous article. In my previous article i had covered all the basic terminologies which i will be using in this Article. This article will be completely programmatic using python and keras. I will be using very basic example and will show you how machine learns to predict. The link for my previous article is below, its been published over Analytics Vidhya
We will be using a simple example of conversion of Celsius to Fahrenheit in this Article but before that we will try to understand what exactly is Dense layer, because we will be using dense layer from keras to build our first network. To understand this we will assume a neural network with 1 input layer with 3 unit, **1 hidden layer **with 2 units and an output layer with 1 unit and the diagram that explains this assumed neural network is below. Units are nothing but the neurons. Each neuron is connected with all the neurons of previous layer, i mean all the neuron of input layer will be connected to the neurons of the hidden layer and each neuron of the hidden layers will be connected to the neurons of the output layer and we can have multiple hidden layers, but for simplicity in this example we have taken only 1 hidden layers. We can see in the below diagram all the neurons are fully connected with the neurons of the previous layers. This type of layers are fully connected or Dense layers.
Fully connected or dense layer
We had understood the concept of Fully connected or dense layer in neural network. So we need to understand if we are using the Dense layer in keras we simply stating the neurons are fully connected to the neurons of previous layer. To create the above discussed layer programmatically in Keras we will use below python code
Keras dense layer
The above code states that we have 1 hidden layer with 2 neurons. The no of neurons we used to specify as a unit and we used to pass as a parameter in the created layer in keras. Similarly for output layer we had specified unit as 1 that states that we have 1 neurons in the output layer. After creation of the hidden layer and output layer we generally build the model by passing the hidden layers and output layers as a parameter in Sequential API. If you want to know more about the Keras i will suggest just go through my earlier bogs i had explained it step by step.
We had understood the Dense layer. Now will try to understand what exactly this dense layer is doing in the network. To understand this will try to recall the the concepts that we had studied in the previous article, the concept of weights and biases. Consider we have x1, x2, x3 as the real input, a1 and a2 are the neurons at hidden layer and a3 are the neurons at output layers, in the previous neural network that we had discussed. As we discussed in fully connected layer each neuron is connected to the neurons of the previous layer so there will be weights associated with all the hidden and output layers so we will have 8 weights, 3 for 1 hidden layers a1 as we have 3 inputs and 3 for other hidden layers a2 and 2 for output layer as we have 2 hidden neurons. This can be understood by looking the below figure. During the execution of neural networks the weights and biases will be getting adjusted to best mach for the given input to the output. In the process of training the neural network only the weights and biases gets changes to fit the best value to fit the input and output that we used to feed the neural networks.
#keras #tensorflow #optimizer #python #deep-learning
1618317562
View more: https://www.inexture.com/services/deep-learning-development/
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#deep learning development #deep learning framework #deep learning expert #deep learning ai #deep learning services