Kaggle competition: Digit Recognizer This notebook implements a hand written digit recognizer trained on MNIST dataset. It was implemented for the kaggle knowledge competition named Digit Recognizer. Import the necessary modules import matplotlib.pyplot as plt import numpy as np import random as ran import tensorflow as tf import os import pandas as pd %matplotlib inline […]

## Deep Fashion: In Shop Clothes Retrieval Playground

Deep Fashion: In Shop Clothes Retrieval Just a playground jupyter notebook to check the Deep Fashion dataset and the detection accuracy. Then I also tried the saliency of the given images. To know more details about the dataset, visit the Deep Fashion project page. The dataset can be downloaded from here. Import necessary modules import […]

## Processing and Showing Images in Jupyter Notebook

This jupyter notebook shows how to process and showing images. First, load all the necessary modules for image processing. import numpy as np import os import six.moves.urllib as urllib from PIL import Image import cv2 import matplotlib.pyplot as plt %matplotlib inline Here, we define the directories from where the images are loaded. ROOT_DIR = ‘/Users/sparrow/Learning/machine-learning/the-eye’ […]

## Object Detection Using Already Trained Models

In this jupyter notebook, we will try two different model: SSD Mobilenet trained on COCO dataset and Faster RCNN model trained on Resnet dataset. Object Detection using ssd_mobilenet_v2_coco_2018_03_29 and faster_rcnn_inception_resnet_v2_atrous_lowproposals_oid_2018_01_28 We need several utilities from tensorflow/model repo. The repo can be found here. Download the repo and add the path of research and research/object_detection to […]

## Restoring A Trained TensorFlow Model And Predict Using That

So you have trained your first machine learning model using Tensorflow. Now you want to use your model to do prediction of independent data but you don’t know how to do it? The tutorial you were following online ends with lots of evaluation and validation scores and graphs but does not give any hint about […]

## Find movie overview similarity using gensim

I am working on to build a movie recommendation system which will understand my taste for movies and will recommend accordingly. The typical movie recommendation system available now does not work very good for me. I guess it is because they mostly matches the genre of movie I watched and recommend movies from that same […]

## Use timeseries data for predicting weather

Weather forecast in Germany This notebook was adopted from Hvass-Labs’s timeseries tutorial. In this notebook, we will try to predict weather for near future. For testing, I am going to use weather data collected for the city Wiesbaden, as I live here now. The data is collected from Climate Data Center (CDC) of Deutscher Wetterdienst. […]

## Use timeseries data for predicting weather using Tensorflow

Weather forecast in Germany This notebook implements a deep learning network to work on time series data. This is an adoptation of my another notebook where I implemented a RNN to predict weather forecast in Wiesbaden, Germany. Here I used the same data, just the network is a deep learning network instead of RNN. The […]

## Stock market prediction using Keras and LSTM

Dhaka Stock Exchange (DSE) Price prediction Recently I have been working on time-series data a lot. I was thinking to get to know the very basics of LSTM. So, what can be better than trying to predict a stock price using LSTM. I have collected some data from Dhaka Stock Exchange (DSE). The data were […]

## Training a object detection model on Fashion-MNIST

For more details about the dataset: https://github.com/zalandoresearch/fashion-mnist This notebook was adopted from: https://github.com/wagonhelm/NaNmnist/blob/master/NaNmnist.ipynb IMPORTANT: First download the dataset from here https://github.com/zalandoresearch/fashion-mnist#get-the-data and put them in the data/fashion directory. Otherwise, tensorflow will download the traditional handwritten digit mnist dataset. from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets(‘data/fashion’, one_hot=True) import matplotlib.pyplot as plt import numpy as np import […]