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dbatools

🚀 SQL Server automation and instance migrations have never been safer, faster or freer

dc-ign

The Deep Convolutional Inverse Graphics Network

dcgan_tensorflow_anim_face

Animation face generator using DCGAN

deadwood

Unity3D project for Media Art Solo Show at UCLA DMA 2017.

deconvfaces

Generating faces with deconvolution networks

deep-fonts

Generate fonts using deep learning

deep-goggle

Source code for "Understanding Deep Image Representations by Inverting Them", CVPR 2015

deep-head-pose

:fire::fire: Deep Learning Head Pose Estimation using PyTorch.

deep-visualization-toolbox

DeepVis Toolbox

deepdream

deepdream.torch

Torch version for https://github.com/google/deepdream

deepdreamanim

DeepDream Animation Helper

deepdreammusicvideo

Deepdream parameter used for Calista & The Crashroots Music Video.

deeplearningexamples

Deep Learning Examples

deepmdma

Hallucinations of a neural network set to music

deeptextures

Code to synthesise textures using convolutional neural networks as described in Gatys et al. 2015 (http://arxiv.org/abs/1505.07376)

deferredcoarsepixelshading

Deferred Coarse Pixel Shading Source Code (For the article in GPU Pro 7)

deform

A fully-featured deformer system for Unity.

dejavu

Dejavu: Motion Prediction in Static Images using structured RF.

demo.raspberrypi

Raspberry Pi demo project

densepose

A real-time approach for mapping all human pixels of 2D RGB images to a 3D surface-based model of the body

design-patterns-for-humans

An ultra-simplified explanation to design patterns

design-patterns-in-c

Practical design patterns in C

detectorgraph

Strongly-typed, dependency based application framework for code/data separation with dependency injection and data passing.

devdog

developer-colorspace-testing

A high dynamic range set of imagery for testing OCIO based colour transforms

development-of-autonomous-downscaled-model-car-using-neural-networks-and-machine-learning

Machine learning using convolution neural network Required: raspberry pi pi cam compatibile rc car motor driver l293d Please create the respective files: forward idle left right reverse optimized_thetas This project aims to build an autonomous rc car using supervised learning of a neural network with a single hidden layer. We have not used any Machine Learning libraries since we wanted to implement the neural network from scratch to understand the concepts better. We will be referring the DC motor controlling the left/right direction as the front motor and the motor controlling the forward/reverse direction as the back motor. Connect the BACK_MOTOR_DATA_ONE and BACK_MOTOR_DATA_TWO GPIO pins(GPIO17 and GPIO27) of the Raspberry Pi to the Input pins for Motor 1(Input 1, Input 2) and the BACK_MOTOR_ENABLE_PIN GPIO pin(GPIO22) to the Enable pin for Motor 1(Enable 1,2) in the L293D Motor Driver IC. Connect the Output pins for Motor 1(Output 1, Output 2) of the IC to the back motor. Connect the FRONT_MOTOR_DATA_ONE and FRONT_MOTOR_DATA_TWO GPIO pins(GPIO19 and GPIO26) of the Raspberry Pi to the Input pins for Motor 2(Input 3, Input 4) in the IC. Connect the Output pins for Motor 2(Output 3, Output 4) of the IC to the front motor. The PWM_FREQUENCY and INITIAL_PWM_DUTY_CYCLE represent the initial frequency and duty cycle of the PWM output. We have created five class labels namely forward, reverse, left, right and idle and assigned their expected values. All class labels would require a folder of the same name to be present in the current directory. The input images resize to the dimension of the IMAGE_DIMENSION tuple value during training. The LAMBDA and HIDDEN_LAYER_SIZE values represent the default lambda value and the number of nodes in the hidden layer while training the neural network. All these values are configurable in configuration.py. The images for training are captured using interactive_control_train.py, the car is controlled using the direction arrows and all the images are recorded in the same folder along with the corresponding key press. After segregating the images into their corresponding class folders, the neural network is trained using train.py which takes two optional arguments - lambda and hidden layer size; default values would be those specified in the configuration file. At the command prompt, run the following command Once we have the trained model, the RC car is run autonomously using autonomous.py which takes an optional argument for the trained model; default will use the latest model in the optimized_thetas folder. Please feel free to post your doubts on code through my linkedin link: edin.com/in/shreyas-ramachandran-srinivasan-565638117/ CONTROLLING THE CAR The controlling process consists of 4 parts:  The sensor interface layer includes various programming modules worried about getting and time stamping all sensor information.  The discernment layer maps sensor information into inward models. The essential module in this layer is the PI camera, which decides the vehicle's introduction and area. Two distinct modules enable auto to explore in view of ultrasonic sensor and the camera. A street discovering module utilizes the PI camera determined pictures to discover the limit of a street, so the vehicle can focus itself along the side. At last, a surface evaluation module separates parameters of the present street to determine safe vehicle speeds.  The control layer is in charge of managing the controlling, throttle, and brake reaction of the vehicle. A key module is the way organizer, which sets the direction of the vehicle in controlling and speed space.  The vehicle interface layer fills in as the interface to the robot's drive-by-wire framework. It contains all interfaces to the vehicle's brakes, throttle, and controlling wheel. It likewise includes the interface to the vehicle's server, a circuit that manages the physical capacity to a significant number of the framework segments. In the proposed system, the raspberry Pi is used to control the L293D board, which allows motors to be controlled through the raspberry pi through the pulses provided by it. Based on the images obtained, raspberry pi provides PWM pulses tocontrol the L293D controller. L293D is a 16 Pin Motor Driver IC as shown in Figure 9. This is designed to provide bidirectional drive currents at voltages from 5 V to 36 V. Fig 9 L293D Breakout Board It also allows the speed of the motor to be controlled using PWM. It’s a series of high and low. The Duration of high and low determine the voltage supplied to the motor and hence the speed of the motor. PWM Signals: The DC motor speed all in all is specifically relative to the supply voltage, so if lessen the voltage from 9 volts to 4.5 volts, then our speed turn out to be half of what it initially had. Yet, for changing the speed of a dc motor we can't continue changing the supply voltage constantly. The speed controller PWM for a DC motor works by changing the normal voltage provided to the motor.The input signals we have given to PWM controller may be a simple or computerized motion as per the outline of the PWM controller. The PWM controller acknowledges the control flag and modifies the obligation cycle of the PWM motion as indicated by the prerequisites. In these waves frequency is same but the ON and OFF times are different. Recharge power bank of any capacity, here, 2800 mAH is used (operating voltage of 5V DC), can be used to provide supply to central microcontroller. The microcontroller used will separate and supply the required amount of power to each hardware component. This battery power pack is rechargeable and can get charged and used again and again.

devops-for-windows-apps

Sample Windows app to show devops best practices

devops-pipelines

Warehouse of notebooks that contains azure data explorer queries to help in root causing pipeline delays in azuredevops.

dgp

Procedural Construction of Buildings