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Training data generator for hierarchically modeling strong lenses with Bayesian neural networks
The baobab package can generate images of strongly-lensed systems, given some configurable prior distributions over the parameters of the lens and light profiles as well as configurable assumptions about the instrument and observation conditions. It supports prior distributions ranging from artificially simple to empirical.
A major use case for baobab is the generation of training and test sets for hierarchical inference using Bayesian neural networks (BNNs). The idea is that Baobab will generate the training and test sets using different priors. A BNN trained on the training dataset learns not only the parameters of individual lens systems but also, implicitly, the hyperparameters describing the training set population (the training prior). Such hierarchical inference is crucial in scenarios where the training and test priors are different so that techniques such as importance weighting can be employed to bridge the gap in the BNN response.
This is a course dedicated for training machine learning models for voice files with emotions (angry, disgust, fear, happy, neutral, or surprised) from video files downloaded from Youtube.
Active members of the team working on this repo include:
We plan to do slack updates every week 8 PM EST on Fridays. If we need to do a work session, we will arrange for that.
Here are some goals to try to beat with demo projects. Below are some example files that classify various emotions with their accuracies, standard deviations, model types, and feature emebddings. It will give you a good idea on what to brush up on as you think about new embeddings for audio and text features for models.
WIP Flag trainging using Core ML
Objective: investigate machine learning to present flag game challenges that are appropriate for the specific player. Present a flag challenge to any given player in gradual progression of difficulty, easy country flags first and more difficult flags as the user progresses.
ML Target (outputs) : ML Fatures (inputs) : Notes about ML Algorithms and Metrics:
Collection of Rapidminer Processes for Training Processes
Logistic Regression on Blog articles, Prediction of gender of author
Clustering of the IRIS Datasets with the follwing algorithms: Performance measured with:
Centroid distance and Davies Bouldin ITDS_Web_TextClustering
k-menoids Text Clustering of Wikipedia Documents
STS-Net is a training strategy which uses MSE and KLD to distill optical flow stream. The network can avoid the use of optical flow during testing while achieving high accuracy. We release the testing and train code. We have not put all the code, some code needs to be modified for better reading. We will add the test model as soon as possible.
For RGB stream: python test_single_stream.py --batch_size 1 --n_classes 51 --model resnext --model_depth 101 \
python STS_train.py --dataset HMDB51 --modality RGB_Flow \
python STS_train.py --dataset HMDB51 --modality RGB_Flow \
Computer Vision In this practical project will solve several computer vision problems using deep models. The goals of these projects are: Develop proficiency in using Keras for training and testing neural nets (NNs). Optimize the parameters and architectures of a dense feed-forward neural network (ffNN), and a convolutional neural net (CNN) for image classification. Build a traffic sign detection algorithm.
Credit Card Transaction Fraud Detection - Project Overview
Cyber frauds are increasing day by day in the world. More and more people are looted these days online due to increase in transactions happening via cards and online wallets. Hence it becomes important to increase the security and stop the online scamsters from looting the masses. Hence, keeping this issue in mind, I have developed a model wherein I have trained it to detect whether a transaction that has been carried out by a credit card fraudulent or not. The model has an efficiency of about 98% with Logistic Regression.
About the dataset
The dataset comprised of both fraudulent and non-fraudulent transactions with 99% transactions lying in non fraudulent transactions.
The transactions were evenly spread in all levels i.e from as low as $2 to as high as $400.
The dataset had details of about 2 lakh transactions.
Processes Involved in the whole Project
Importing the dataset as a csv and converting it into a dataframe so as to work with pandas on it.
Exploratory Data Analysis(EDA) to understand the data. Some important insights were gathered from this data exploration, which involved:
Number of transactions with NA values.
Number of fraudulent transactions and number of non fraudulent transactions.
Correlations between the various columns of the dataset.
Data Visualization to further understand the variations in dataset.
Data cleaning. This was done majorly to remove the outliers for a more accurate prediction.
Data sampling. This was done to increase the number of fraudulent transactions so that the model is trained to identify not just the non fraudulent once and not be biased. Various sampling algorithms were used here which included:
Random UP Sampler
Random DOWN Sampler
Near - Miss
Model Training. Data obtained from all the sampling techniques was fit into four different models to check which performs better.
Training a model
Four different models were used for this project. The models were:
XG - Boost
The data obtained from various sampling methods was fit into these models simultaneously and the area under the curve(AUC) was calculated for each one of them.
The best performing model was Logistic Regression with the highest area under the curve. The performances for the various models are as follows:
Logistic Regression: 0.98
Random Forests: 0.97
K-Nearest Neighbours: 0.93
XG-Boost: 0.97 Hence from the above results we can say that logistic regression performs the best and hence can be used for productionization.
A multi layer perceptron(MLP) neural network used to classify hand-written digits. By building a feedforward network with back propagation and training process using the Stochastic Gradient descent algorithm. Without using any machine learning libraries. Since it is a Multi-class classification problem- Implemented Softmax activation function for the output layer. Sigmoid activation function for the Hidden layers.
Android Application Development - Syllabus
Mobile Apps are becoming popular day by day. Today, Everyone owns a smartphone and they do a lot of things with the help of their smartphones such as making payments, ordering groceries, playing games, chatting with friends and colleagues etc .There is huge demand in the market to develop android apps. Its Googles CEO Sundar Pichai's initiative to train 2 Million people to become android developers as this platform has a huge need of developers. In view of this scenario and keeping industry needs in mind, APSSDC is offering Android Application Development - FDP so that the faculty across engineering colleges in the state of Andhra Pradesh gain App Development knowledge and share the same to their students.
i3 or above Processor is required 8 GB RAM is recommended Good Internet Connectivity Microphone and Speakers facility for Offline training program.
36 Hours (2 hours each day X 18 days)
Workshop Syllabus :
1. Introduction to Mobile App Development
2. History of Mobile evolution
3. Version History of Android
4. Android Architecture
5. Installing the Development Environment
a. Installation of Android Studio
b. Installation of Android emulator
c. Connecting the physical device with the IDE
6. Creating the first application
7. Hello World
8. Creating a User Interactable App
9. Hello Toast
10. Text and Scroll View
a. Explicit Intents
b. Implicit Intents
12. Activity LifeCycle
13. User Interface Components
14. Buttons and Clickable Images
15. Input Controls
16. Menus & Pickers
17. Using Material Design for UI
18. User Navigation
a. Navigation Drawer
b. Navigation Components
i. Navigation Graph
ii. Navigation Host
iii. Navigation Controller
c. Ancestral and Back Navigation
d. Lateral Navigation
i. Tabs for navigation
19. Recyclerview and DiffUtil
20. Working in the background
21. Fetching JSON Data from the internet using retrofit GET.
a. Discussion of various JSON Converters.
b. Writing data to the api using retrofit POST.
22. Broadcast Receivers
24. Saving user Data
d. Room Persistence Library.
Course Objectives :
Entry Requirements :
Mode Of Training :
WideResNet implementation on MNIST dataset. FGSM and PGD adversarial attacks on standard training, PGD adversarial training, and Feature Scattering adversarial training.
For standard training and PGD adversarial training use It automatically executes main.py with additional arguments like no. of iteration, epsilon value, max iterations for attack and step size in each attack. After training the model it executes FGSM and PGD attacks on it. For feature scattering based adversarial training use Does the same previous one but implements feature scattering based adversarial training.
Confluent Apache Kafka for Developers Course
This is the source code accompanying the Confluent Apache Kafka for Developers course. It is organized in two subfolders
labs. The former contains the complete sample solution for each exercise whilst the latter contains the scaffoliding for each exercise and is meant to be used and elaborated on by the students during the hands-on.
House Price Prediction System Documentation
This Project is based on Machine Learning in which We predict the Price of the House using a Dataset and training it by using several Machine Learning Algorithms Like EDA(Exploratory Data Analysis) and SEMMA(Sample, Explore, Modify, Model, Assess) model. The Model takes several factors like lot size, Number of Rooms, Floors, Bathrooms etc and hence Predicts the Price with 95% Accuracy in USD. You can use it here: 22.214.171.124
Mixer is a small and adaptive program for training and testing sentence classification models inspired by Cloud AutoML. It is completely offline and the speed of loading the dataset and training time directly depends on the performance of the computer
The mixer is a multilingual program, ie language independent. All font types that UTF-8 supports are also supported by the Mixer.
This is an iOS application that brings none or motivated people together that wants to be active. So, in other words its for people who want to do a specific sport or activity but does not have buddy to train with or is not commit enough to an organization. By using our application, the user should be able to find a training session based on their preference, they should be able to join a group or duo session and they should be able to add or find a training buddy.
Repository for Epam External Training Tasks
Task 1.1. The Magnificent Ten
Calculate the area of a rectangle with sides A and B. Display an image of a rectangular triangle with a height of N lines. Display an image of an isosceles triangle with a height of N lines. Display an image of a Christmas tree consisting of N isosceles triangles. Calculate the sum of all natural numbers from 1 to 1000, which are multiples of 3 or 5. Write a program to store text formatting options (bold, italic, underline and their combinations). Write a program that generates a random array, sorts this array and displays the maximum and minimum elements. Write a program that replaces all positive elements in a three-dimensional array with zeros. Write a program that determines the sum of non-negative elements in a one-dimensional array. Determine the sum of all elements of a two-dimensional array that are in even positions.
Task 1.2. String, Not Sting
Write a program that determines the average word length in the entered text string. Write a program that doubles in the first input string all the characters that belong to the second input string. Write a program that counts the number of words starting with a lowercase letter. Write a program that replaces the first letter of the first word in a sentence with a capital letter.
Task 2.1. OOP
Write your own class that describes the string as an array of characters. Describe the classes of geometric shapes. Implement your own editor that interacts with rings, circles, rectangles, squares, triangles and lines.
Task 2.2. Game Development
Create a class hierarchy and define methods for a computer game. Try making a playable version of your project.
Task 3.1. Weakest Text
There are N people in the circle, numbered from 1 to N. Every second person is crossed out in each round until there is one left. Create a program that simulates this process. For each word in a given text, indicate how many times it occurs.
Task 3.2. Dynamic Array
Based on the array, implement your own DynamicArray generic class, which is an array with a margin that stores objects of random types.
Task 3.3. Pizza Time
Extend the array of numbers with a method that performs actions with each specific element. The action must be passed to the method using a Delegate. Extend the String with a method that checks what language the word is written in the given string. Simulate the work of a pizzeria in your application. The user and the pizzeria interact through the pizza order. The user places an order and waits for a notification that the pizza is ready. The peculiarity of your pizzeria is that you do not store customer data.
Task 4.1. Files
There is a folder with files. For all text files located in this folder or subfolders, save the history of changes with the ability to roll back the state to any moment.
AllWize Training Environment
This course contains a training environment for workshops using two different approaches:
A vagrant definition file for a VirtualBox machine
A docker-compose file to run a docker stack The Vagrant machine will be reacheble at 192.168.42.10, the docker container will expose the ports directly to the host machine. None of the services have any security defined. This environment is not meant for production or machines exposed to the Internet!!!
Coding Dojo - code and programming training local
Coding Dojo is a safe environment for testing new ideas, promoting networking and sharing ideas among team members. It is very common for companies to promote open Dojos. In this way the company can meet professionals who can adapt to its environment and professionals also have the opportunity to know the environment of these companies.
Kata: In this format there is the figure of the presenter. He must demonstrate a ready-made solution, previously developed. The objective is that all participants are able to reproduce the solution achieving the same result, being allowed to make interruptions to resolve doubts at any time; Randori: In this format, everyone participates. A problem is proposed to be solved and the programming is carried out on only one machine, in pairs. For this format, the use of TDD and baby steps is essential. The person coding is the pilot, and his partner is the co-pilot. Every five minutes the pilot returns to the audience and the co-pilot assumes the status of pilot. A person from the audience takes on the position of co-pilot. Interruptions are only allowed when all tests are green. It is also important to note that it is the pair who decide what will be done to solve the problem. Everyone must understand the solution, which must be explained by the pilot and the co-pilot at the end of their implementation cycle; Kake: It is a format similar to Randori, however there are several pairs working simultaneously. Each shift the pairs are exchanged, promoting integration between all participants of the event. In this format, more advanced knowledge of the participants is necessary.
Two Python 3.x modules, that use Boto3, suitable for use in introductory / intermediate training
I developed two Python 3.x modules suitable for use in introductory to intermediate level training. Both use Boto3 to interact with the AWS S3 service.
The module s3_list.py is suitable for short, entry-level training. This module returns a list of the S3 buckets that belong to an AWS account. I have found this module ideal for entry-level training sessions lasting a day or less. The module S3_man.py is suitable for longer, intermediate-level training. It imports (i.e., includes) the s3_list.py module and supports a few basic S3 operations (e.g., list the objects in a bucket, create a bucket, delete a bucket). Both modules work across the Boto3 Resource and the Boto3 Client API sets. Additionally, both can be run using the default profile contained in the /.aws/credentials file (i.e., created during installation of the AWS CLI) or using an IAM user of your choice. To execute a module's functionality using an IAM user of your choice, you supply the AWS IAM access key id and the AWS IAM secret access key as parameters to a function call or a command line operation. If you do so, you also have the option of specifying which AWS regional endpoint will be used when communicating with the S3 service. Outside of Boto3, both modules only make use of modules from the Python 3.x Standard Library. And finally, both modules have the ability to be run as a stand-alone script or to be imported by another module.
An iteratively developed approach to the problem of fast SOM training. Will work towards the implementation of the HPSOM algorithm described by Liu et al.
To compile the code to a library, cd ~/SOMeSolution/src/C++ make The static library will be in bin/somesolution.a To compile the code to a commandline usable executable, cd ~/SOMeSolution/src/C++ make build
Through the command line you can add different flags and optional arguments. Arguments: Positional Arguments Description WIDTH HEIGHT Sets the width and heigth of the SOM Flag Description Example: The following will make a 10 x 10 SOM, generate it's own training data, have 100 features and 100 dimensions somesolution 10 10 -g 100 100 -o trained_data.txt
To visualize a SOM weights file produced by the commandline executable, simply run: python som.py -i weights.txt -d
Open source resources for SLP and vocal training The aim of this software is to help develop cross platform tools for analyzing speech with a focus on providing real time feedback for those involved in vocal therapy either as patients or practitioners. This is a work in progress, presently very limited in function and user friendlyness, as they are tools that I am personally using in my own vocal training. The software is written in python3, and has dependencies on PyQt4, pyqtgraph, numpy scipy, and pyaudio. It should be cross platform but it is being developed on Linux The record and playback functions have now been unified in the pitch_perfect.py application. Upon launch you will be able to choose a file for playback, or a file for recording. Once playback or recording has begun you may click stop to end the operation.
This code uses the Haar Cascade Classifier to detect face in a video feed (webcam used here) and extracts 100 training samples The training samples and raw images are stored in a folder named Training in C: drive The code has been tested on the following configuration:
Generalized regression neural network (GRNN) is a variation to radial basis neural networks. GRNN was suggested by D.F. Specht in 1991.GRNN can be used for regression, prediction, and classification. GRNN can also be a good solution for online dynamical systems. GRNN represents an improved technique in the neural networks based on the nonparametric regression. The idea is that every training sample will represent a mean to a radial basis neuron. GRNN is a feed forward ANN model consisting of four layers: input layer, pattern layer, summation layer and output layer. Unlike backpropagation ANNs, iterative training is not required. Each layer in the structure consists of different numbers of neurons and the layers are connected to the next layer in turn. 
In the first layer, the input layer, the number of neurons is equal to the number of properties of the data.
In the pattern layer, the number of neurons is equal to the number of data in the training set. In the neurons in this layer, the distances between the training data and the test data are calculated and the results are passed through the radial based function (activation function) with the value and the weight values are obtained.
The summation layer has two subparts one is Numerator part and another one is Denominator part. Numerator part contains summation of the multiplication of training output data and activation function output (weight values). Denominator is the summation of all weight values. This layer feeds both the Numerator & Denominator to the next output layer.
The output layer contains one neuron which calculate the output by dividing the numerator part of the Summation layer by the denominator part.
The general structure of GRNN 
Training procedure is to find out the optimum value of . Best practice is that find the position where the MSE (Mean Squared Error) is minimum. First divide the whole training sample into two parts. Training sample and test sample. Apply GRNN on the test data based on training data and find out the MSE for different . Now find the minimum MSE and corresponding value of . 
Retrieved from 
I wanted to learn how to use and train a [Transformer] (in a [pytorch] environment). This is my (not so serious) attempt at it. I collected a dataset of about 150k instances of movie titles (english, plus other languages as well), along side with their IMDB ratings. The objective was to generate a random movie title considering the input rating, so that the generated title is conditioned on the input rating (i.e. a lower rating should produce a movie title that if had existed it would have gotten a bad rating on IMDB). The resulting language model is modeling the following probabilities:
P(token1 | [rating])
P(token2 | [rating] token1)
P(token3 | [rating] token1 token2)
P(tokenN | [rating] token1 token2 ...) I'm not uploading the dataset here, but I've uploaded the model weights so you can try to generate titles on your machine.
Model architecture and training
The encoder/decoder architecture was completely dispensed with by just using a stack of 6 transformer encoder layers. Ratings and tokens uses different embeddings to keep the concepts separate within the neural network. The text is tokenized by using byte-pair encoding ([sentencepiece]). The BPE model was trained on the dataset.
The training happens in an unsupervised fashion, using cross entropy loss, teacher forcing, and Noam optimizer.
Practically, the model learns to predict the next token given the previous context (rating + tokens) (as you can see in the picture above). The uploaded pretrained model was trained with batch size = 512, d_model = 128, n_head = 4, dim_feedforward = 512 and 6 stacked transformers.
There isn't a proper reason behind the choice of these values, I just wanted to train it as fast as I could and also get "good" results.
I stopped the training at epoch 1120 with a average loss per batch of roughly 3.13.
Since the loss is still far from good, don't expect too much from this pretrained model.
multinomial with temperature sampling = 0.8 (no top_k or top_p sampling used/implemented) $ python3 eval.py --samples 10 4.5 Tall Wave
Wild Dr. Bay
The Witch We Getting Well Lords
The Secret of War
Un napriso tis amigos
The Lonesomes of Destrada
The Black Curse of Saghban
Ghosts of the Skateboard
$ python3 eval.py --samples 10 7.8
Una noche tu vida de Sabra
Terror of the End
To Best of Those West
You Are Ends
A personal training analyzer based on Python and Excel. As the PolarPersonalTrainer Webpage will be shut down by 31.12.2019, older Polar Fitness and GPS watches will be deprecated. This software enables to further use these older devices and creates relevant running training information in form of Excel WorkSheets. The software imports the training data in form HRM and GPX Files, creates training session worksheet for each training, and adds the training to an overview sheet.
This software aims to provide a similar (of course restricted) functionality as the website PolarPersonalTrainer by creating excel sheets with a similar look as the website. For each training session, an individual excel is created which contains the following information:
The software was tested with training data from a Polar RC3 GPS with HRM version 1.06.
Python 3.5.1 or higher
In this lesson, you'll learn how to use the WP-CLI, what they are, when you should use them and how it helps you in your WordPress development.
After completing this lesson, you will be able to:
Who is this lesson intended for? What interests/skills would they bring? Choose all that apply.
Star net is a multidimensional ICT and Security System Company whose existence sequel to the dire need to ensure that both small and big offices as well as homes automation are not frustrated by non-competent firms. We are mainly specialized on ICT Services, ICT Training, ICT Consultancy and Security Installations. OUR CORE SERVICES Bulk SMS we offer the best bulk sms platform with an API integration and fast delivery system for different telecommunication networks. S.E.O Search Engine Optimization is an important part of any successful local marketing strategy. Social Media Marketing We are available to help you publicize your product, company or event on all the social networks. Local Search Strategy Maximize your presence on search engine results pages on a local scale. Website Design Our team specializes in affordable web design using different tools on different platforms. Custom Email Design Custom email templates that speak to your customers and resonate with your brand. Graphics Design Our team specializes in affordable Graphics design such as Logos, Banners, Flyers etc. using different tools on different platforms.
Meta-Apo (Metagenomic Apochromat) calibrates the predicted gene profiles from 16S-amplicon sequences using an optimized machine-learning based algorithm and a small number of paired WGS-amplicon samples as model training, thus produces diversity patterns that are much more consistent between amplicon- and WGS-based strategies (Fig. 1). The Meta-Apo takes the functional gene profiles of small number (e.g. 15) of WGS-amplicon sample pairs as training, and outputs the calibrated functional profiles of large-scale (e.g. > 1,000) amplicon samples. Currently the Meta-Apo requires functional gene profiles to be annotated using KEGG Ontology. Fig.1. Calibration of predicted functional profiles of microbiome amplicon samples by a small number of amplicon-WGS sample pairs for training.
Meta-Apo only requires a standard computer with >1GB RAM to support the operations defined by a user.
Meta-Apo only requires a C++ compiler (e.g. g++) to build the source code.
In the field of Programming Kits learning from a live instructor-led and hand-on training courses would make a big difference as compared with watching a video learning materials. Participants must maintain focus and interact with the trainer for questions and concerns. In Qwikcourse, trainers and participants uses DaDesktop , a cloud desktop environment designed for instructors and students who wish to carry out interactive, hands-on training from distant physical locations.
For now, there are tremendous work opportunities for various IT fields. Most of the courses in Programming Kits is a great source of IT learning with hands-on training and experience which could be a great contribution to your portfolio.
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