Anyone who ever wanted to perform Optical Character Recognition (OCR) must have heard about Tesseract.

Why? Well, cause, one, it was developed by Google; two, it is state of the art and what’s more? It’s free!

So why are we even writing this post? Why are you guys even reading it? Well, that’s because even Tesseract has some major drawbacks.

Tesseract works at its best when the images are of a document or are organized as a document.

In our latest entry under the Stock Price Prediction Series, let’s learn how to predict Stock Prices with the help of XGBoost Model.

In case you want to dig into the other approaches of Stock Price Prediction, have a look at our other blogs in this series:

• Stock Prediction: Facebook Prophet
• Statistical Approach to Stock Price Prediction: Naive Forecast, Moving Average
• Stock Price Prediction: Neural Network with Tensorflow
• Stock Price Prediction: Using Deep Neural Networks on Keras

The following article would introduce you to steps and training procedures you need to follow while carrying out time series forecasting with the help of XGBoost. …

Trying to deploy a custom object detector that detects, say, price tags? Or a similar object for which finding an extensive dataset is a pain. Dive into our blog to learn hacks of how to create such an extensive dataset and exploit TensorFlow Custom Object Detection API to the fullest in such extreme scenarios.

Dataset

Usual object detection tasks include detecting cars,dogs,cat,etc.But due to the unique nature of our problem we had no predefined database. Well, we had to do the painful labor of manually creating a dataset.

Just Kidding!! We will web-scrap to make our lives a bit easier.

Our complete scrapping code can be found on the GitHub, just make a few changes and you are good to go. …

Let’s learn how to predict stock prices using a single layer neural network with the help of TensorFlow Backend. You’ll be in awe when you see how marvelous such a simple architecture performs on a dataset of stock prices.
The content of this blog is inspired by the Coursera Series: Sequences, Time Series and Prediction.

This is our third entry in our series of blogs on TimeSeries Prediction. Link to previous two:

• Stock Prediction: Facebook Prophet
• Statistical Approach to Stock Price Prediction: Naive Forecast, Moving Average

Loading the Dataset

We work with New Germany Fund Data. It is loaded as a Pandas DataFrame. We predict the OPEN Prices. Hence, the column OPEN was created into an instance of NumPy array. With a total of 996 values, we decided to choose a train-validation split of 850:146. …

Let’s employ some basic statistical methods to predict stick prices. We will first learn what these methods mean followed by quick code implementations. You’ll be surprised to see that such simple approaches have great accuracies!

This is our second blog under Stock Price Prediction. Our first blog in this series provides an easy-to-understand guide to Facebook Prophet, a Pretrained Model to Forecast Time Series.

Naive Forecast

Naive Forecast is the most basic method of forecasting stock prices. This approach preaches that the forecast is nothing but the value of the variable at a previous timestamp.
For instance, in a dataset where the timestamp is a day, the predicted opening stock price for tomorrow is simply today’s opening value. Though simple, it yields awesome results! …

Predicting stock prices is a difficult task. Several factors can affect the price of the stock which is not always easy to accommodate in a model. There is no model in the world currently which can accurately predict the stock prices and there might never be one owing to the reasons mentioned above. Facebook has given a “state of the art model” and “easy to use” and a wide range of hyperparameter tuning options to give somewhat accurate predictions.

Pre-processing

As mentioned above, we have a dataset that has stock prices for New Germany Fund from the year 2013 to 2018. Now as we import the data and see it for the first time, we see that it is not sorted in the ascending order of the dates, This is a major issue as forecasted values are more likely to depend on the immediate past entries rather than entries before. …

CIFAR-10 is one of the benchmark datasets for the task of image classification. It is a subset of the 80 million tiny images dataset and consists of 60,000 colored images (32x32) composed of 10 object classes.

This dataset allowed us to explore different approaches to Image Classification.

In this article, we try to bring forth the issues faced and results while training the dataset and carrying out predictions on Google Colab Notebooks.

We tried the following approaches on CIFAR-10 dataset:

• CNN Architecture: Default CIFAR-10 Image Size
• ResNet101 Model: Default CIFAR-10 Image Size
• ResNet101 Model: Default ResNet101 Input Image Size

For all three variations, the preprocessing was almost the same. …

Image Classification is one of the most trivial yet challenging problems under Computer Vision. To get a better understanding, we have tried to perform image classification on a very famous benchmark dataset: Fashion MNIST. This dataset was meant to replace MNIST because:

• MNIST is too easy.
• MNIST is overused.
• MNIST can not represent modern CV tasks.

Preprocessing the Dataset

The Fashion MNIST Dataset available on Kaggle consists of two files: train.csv and test.csv. Both have 785 columns, with the first one being the label and the following 784 being the pixel values of 28x28 images.
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