1. k-Means Clustering k-Means Clustering is an unsupervised learning algorithm used for grouping data points into \( k \) clusters based on their similarity. The algorithm works by assigning each data point to the nearest cluster center, then adjusti...
In the rapidly evolving landscape of artificial intelligence, a groundbreaking project has emerged that promises to revolutionize our understanding of machine learning, quantum computing, and even consciousness itself. Enter NeuroFlex, an ambitious a...
LSTM’s can learn from its input sequence when to use short term dependency and when to use long term dependency. In short term dependency it decides to clear the cell state. In long term dependency it decides not to clear the cell state. LSTM’s cont...
Long Short-Term Memory (LSTM) networks are a type of recurrent neural network (RNN) specifically designed to model and predict sequential data. Unlike traditional RNNs, LSTMs are capable of learning long-term dependencies, making them particularly ef...
In this blog post, we’ll walk you through creating an end-to-end machine learning application to predict stock market prices. We'll use Python, TensorFlow, Keras, and Flask for this project. Let's dive in step-by-step, from data collection to deploym...
1. Brief introduction The deep neural network has become more and more popular in recent years. It has solved the problems that could not be solved in the past in many fields and has demonstrated its strong ability. In the prediction of time series, ...
Problem Statement Here we will see how the entire context of a particular text can be automatically generated in a precise less number of words. I have used the kaggle data set (https://www.kaggle.com/nicapotato/womens-ecommerce-clothing-reviews/home...
In the era of ever-growing landscape of Machine Learning Field, one paradigm stands out for its remarkable ability to adapt and applications that is Sequence-to-Sequence (Seq2Seq) Learning. It is all about training models to convert sequences from on...
Recurrent Neural Networks had a problem - vanishing gradient problem. Why the problem exists is better discussed in the previous article. In a brief, the vanishing gradient problem implies that the context of the sentence is forgotten about too quick...
Time series data captures how a variable changes over time, like stock prices, website traffic, or sensor readings. Time series forecasting involves using this data to predict future values or trends in the series. Here are some popular techniques: ...
