Tensorflow

TensorFlow is an open-source machine learning library for research and production. It was originally developed by Google and is now used by many organizations and individuals for a wide range of purposes, including natural language processing, computer vision, and predictive analytics. TensorFlow provides a flexible, high-performance platform for training and deploying machine learning models. It allows users to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays (tensors) with a variety of different architectures and hardware configurations. This makes it easy to implement and scale up complex machine learning algorithms and models. Overall, TensorFlow is a popular and powerful tool for machine learning and data science.

Image Classification with Convolutional Neural Networks – Classifying Cats and Dogs in Python

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This tutorial shows how to use Convolutional Neural Networks (CNNs) with Python for image classification. CNNs belong to the field of deep learning, a subarea of machine learning, and have become a cornerstone to many exciting innovations. There are endless applications, from self-driving cars over biometric security to automated tagging in social media. And the … Read more

Mastering Multivariate Stock Market Prediction with Python: A Guide to Effective Feature Engineering Techniques

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Are you interested in learning how multivariate forecasting models can enhance the accuracy of stock market predictions? Look no further! While traditional time series data provides valuable insights into historical trends, multivariate forecasting models utilize additional features to identify patterns and predict future price movements. This process, known as “feature engineering,” is a crucial step … Read more

Stock Market Prediction using Multivariate Time Series and Recurrent Neural Networks in Python

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Regression models based on recurrent neural networks (RNN) can recognize patterns in time series data, making them an exciting technology for stock market forecasting. What distinguishes these RNNs from traditional neural networks is their architecture. It consists of multiple layers of long-term, short-term memory (LSTM). These LSTM layers allow the model to learn patterns in … Read more

Rolling Time Series Forecasting: Creating a Multi-Step Prediction for a Rising Sine Curve using Neural Networks in Python

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Many time forecasting problems can be solved by predicting just one step into the future. However, some problems require a forecast for an extended period of time, which calls for a multi-step time series forecasting approach. This approach involves modeling the distribution of future values of a signal over a prediction horizon. In this article, … Read more

Stock Market Prediction – Adjusting Time Series Prediction Intervals in Python

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Get ready to level up your time-series forecasting game! In this tutorial, we’re going to take things up a notch by showing you how to adjust prediction intervals using Keras recurrent neural networks and Python. Now, you may remember our previous article on stock market forecasting where we made a forecast for the S&P500 stock … Read more

Stock Market Prediction using Univariate Recurrent Neural Networks (RNN) with Python

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Financial analysts have long been fascinated by the prospect of predicting the prices of financial assets. In recent years, there has been increasing interest in using machine learning and deep learning techniques to generate predictions, in addition to traditional methods such as technical and fundamental analysis. Python libraries like Keras and Scikit-Learn make it relatively … Read more