Pinned Repositories
100-exos-Numpy
500-Person-Gender-Height-Weight-Body-Mass-Index
Gender : Male / Female Height : Number (cm) Weight : Number (Kg) Index : 0 - Extremely Weak 1 - Weak 2 - Normal 3 - Overweight 4 - Obesity 5 - Extreme Obesity
Access-Phone-camera-for-OpenCV
If you are having problem is using your laptop webcam or you dont have a good webcam for working with OpenCV or related Computer vision projects then, Nothing to worry about because using this project source code and following the readme instructions you can use your phone came to work with OpenCV library in Python.
Action_Recognition
ActionReco contains all steps of training action recognition model with optical flow and single person tracking
ActionViewer
ICVL Action Dataset Viewer
AESencryption
AES256bit Encryption and Decryption
Analyzing-R-users-Worldwide-2019
Stackoverflow recently released the anonymized results of their 2019 annual developer survey. The goal of this project is to analyse various trends for R users .
Android-Calculator
android-vision
Sample code for the Android Mobile Vision API.
Artificial-Intelligence-Deep-Learning-Machine-Learning-Tutorials
A comprehensive list of Deep Learning / Artificial Intelligence and Machine Learning tutorials - rapidly expanding into areas of AI/Deep Learning / Machine Vision / NLP and industry specific areas such as Automotives, Retail, Pharma, Medicine, Healthcare by Tarry Singh until at-least 2020 until he finishes his Ph.D. (which might end up being inter-stellar cosmic networks! Who knows! 😀)
Bennio's Repositories
Bennio/Keras-Mask-R-CNN
In this project we use Keras and Mask R-CNN to perform instance segmentation
Bennio/industry-machine-learning
A curated list of applied machine learning and data science notebooks and libraries across different industries.
Bennio/Data-Science--Cheat-Sheet
Cheat Sheets
Bennio/facial_landmarks
Facial landmarks detection with dlib, OpenCV, and Python
Bennio/raccoon_dataset
The dataset is used to train my own raccoon detector and I blogged about it on Medium
Bennio/Sign-Language
A very simple CNN project.
Bennio/android-vision
Sample code for the Android Mobile Vision API.
Bennio/scikit-learn
scikit-learn: machine learning in Python
Bennio/microservices-demo
Sample cloud-native application with 10 microservices showcasing Kubernetes, Istio, gRPC and OpenCensus. Provided for illustration and demo purposes.
Bennio/Access-Phone-camera-for-OpenCV
If you are having problem is using your laptop webcam or you dont have a good webcam for working with OpenCV or related Computer vision projects then, Nothing to worry about because using this project source code and following the readme instructions you can use your phone came to work with OpenCV library in Python.
Bennio/Python-for-Data-Science-and-Machine-Learning-Bootcamp
program with Python, how to create amazing data visualizations, and how to use Machine Learning with Python! Here a just a few of the topics we will be learning: Programming with Python NumPy with Python Using pandas Data Frames to solve complex tasks Use pandas to handle Excel Files Web scraping with python Connect Python to SQL Use matplotlib and seaborn for data visualizations Use plotly for interactive visualizations Machine Learning with SciKit Learn, including: Linear Regression K Nearest Neighbors K Means Clustering Decision Trees Random Forests Natural Language Processing Neural Nets and Deep Learning Support Vector Machines and much, much more!
Bennio/loginpass
Social connections powered by Authlib.
Bennio/Credit-Card-Fraud-Detection
Context It is important that credit card companies are able to recognize fraudulent credit card transactions so that customers are not charged for items that they did not purchase. Content The datasets contains transactions made by credit cards in September 2013 by european cardholders. This dataset presents transactions that occurred in two days, where we have 492 frauds out of 284,807 transactions. The dataset is highly unbalanced, the positive class (frauds) account for 0.172% of all transactions. It contains only numerical input variables which are the result of a PCA transformation. Unfortunately, due to confidentiality issues, we cannot provide the original features and more background information about the data. Features V1, V2, ... V28 are the principal components obtained with PCA, the only features which have not been transformed with PCA are 'Time' and 'Amount'. Feature 'Time' contains the seconds elapsed between each transaction and the first transaction in the dataset. The feature 'Amount' is the transaction Amount, this feature can be used for example-dependant cost-senstive learning. Feature 'Class' is the response variable and it takes value 1 in case of fraud and 0 otherwise.
Bennio/Deep-Learning-basics-with-Python-TensorFlow-and-Keras
Deep Learning basics with Python, TensorFlow and Keras(SENTDEX TUTORIALS)
Bennio/image-classification
Binary image classification tensorflow demo
Bennio/Deep-Learning-with-OpenCV
Deep Learning is a fast growing domain of Machine Learning and if you’re working in the field of computer vision/image processing already (or getting up to speed), it’s a crucial area to explore. With OpenCV 3.3, we can utilize pre-trained networks with popular deep learning frameworks. The fact that they are pre-trained implies that we don’t need to spend many hours training the network — rather we can complete a forward pass and utilize the output to make a decision within our application. OpenCV does not (and does not intend to be) to be a tool for training networks — there are already great frameworks available for that purpose. Since a network (such as a CNN) can be used as a classifier, it makes logical sense that OpenCV has a Deep Learning module that we can leverage easily within the OpenCV ecosystem. Popular network architectures compatible with OpenCV 3.3 include: GoogleLeNet (used in this blog post) AlexNet SqueezeNet VGGNet (and associated flavors) ResNet
Bennio/cloud-scanner
Cloud Scanner is a cloud agnostic tool that extracts cloud based resources from cloud providers like Azure and ingests them into a configured data source for further processing.
Bennio/ignite
High-level library to help with training neural networks in PyTorch
Bennio/real-time-face-recognition
A face-recognition project with webcam, web-sockets and python
Bennio/mit-deep-learning-book-pdf
MIT Deep Learning Book in PDF format (complete and parts) by Ian Goodfellow, Yoshua Bengio and Aaron Courville
Bennio/keras
Deep Learning for humans
Bennio/Cam-Scanner-in-python
The script takes an image as input and then scans the document from the image by applying few image processing techniques and gives the output image with scanned effect
Bennio/Face-Recognition-with-OpenCV-in-Python
In this project we have discussed on how to perform face recoginition with openCV in python .
Bennio/Handwritten-Digits-Recognition-in-python
In this tutorial we will learn how to recognize handwritten digits in python using machine learning library called scikit learn.
Bennio/CNN-from-Scratch
A scratch implementation of Convolutional Neural Network in Python using only numpy and validated over CIFAR-10 & MNIST Dataset
Bennio/recommendationEngine
A 3-hour workshop for understanding deep learning recommendation systems.
Bennio/Sign-Language-Interpreter-using-Deep-Learning
A sign language interpreter using live video feed from the camera.
Bennio/Content-based-recommendation-system
Recommendation systems are a collection of algorithms used to recommend items to users based on information taken from the user. These systems have become ubiquitous, and can be commonly seen in online stores, movies databases and job finders. In this notebook, we will explore Content-based recommendation systems and implement a simple version of one using Python and the Pandas library.
Bennio/Predict-Fuel-Efficiency-Regression-
In a regression problem, we aim to predict the output of a continuous value, like a price or a probability. Contrast this with a classification problem, where we aim to select a class from a list of classes (for example, where a picture contains an apple or an orange, recognizing which fruit is in the picture). This notebook uses the classic Auto MPG Dataset and builds a model to predict the fuel efficiency of late-1970s and early 1980s automobiles. To do this, we'll provide the model with a description of many automobiles from that time period. This description includes attributes like: cylinders, displacement, horsepower, and weight.
Bennio/deep-learning-coursera
Deep Learning Specialization by Andrew Ng on Coursera.