Anix Lynchgozeroshot.dev·Nov 5, 2024Part 2: 10 Advanced ML and Ensemble Methods with Math Notation Friendly Explained1. 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...K-mean
kasinadhsarmablogs.kasinadhsarma.in·Aug 17, 2024NeuroFlex: Pioneering the Future of AI and ConsciousnessIn 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...2d/3d convolution
Monojit Sarkarmonojit13.hashnode.dev·Jul 21, 2024What I learnt about LSTM.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...LSTM
Osen Muntuoseninsights.tech·Jun 12, 2024Understanding Long Short-Term Memory (LSTM) NetworksLong 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...1 like·33 readsLSTM
Hairulnizam Hashimhairulnizam.hashnode.dev·Jun 9, 2024Building an LSTM-Based Stock Price Prediction Model: An End-to-End GuideIn 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...5 likesAI
FMZ Quantfmzquant.hashnode.dev·May 31, 2024Neural Networks and Digital Currency Quantitative Trading Series (1) - LSTM Predicts Bitcoin Price1. 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, ...Bitcoin
kushagra raitechlearngrow.hashnode.dev·Apr 11, 2024Summarization of Customer Reviews PROJECTProblem 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...encoder decoder
Yashkumar Dubeyblogwithdubey.hashnode.dev·Jan 22, 2024Let's Dive into Seq2Seq LearningIn 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...encoders
Japkeerat Singhjapkeeratsingh.com·Jan 10, 2024How LSTMs architecture solves the problem created by RNNsRecurrent 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...LSTM
K Ahameddatailm.hashnode.dev·Jan 8, 2024Time Series Forecasting: Predicting the FutureTime 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: ...Machine LearningARIMA
