195 skills found · Page 4 of 7
AnAppleCore / MOSEMulti-level Online Sequential Experts (MOSE) for online continual learning problem. (CVPR2024)
timeseriesAI / TimeseriesAI1Practical Deep Learning for Time Series / Sequential Data using fastai1/ Pytorch
nancheng58 / SSL4SR[CCIR 2023] Self-supervised learning for Sequential Recommender Systems
audiofhrozen / Motion DanceSequential Learning for Dance generation
recski / HunTaga sequential tagger for NLP using Maximum Entropy Learning and Hidden Markov Models
tam-ng / Human Activity RecognitionPerformed various Deep Learning techniques to detect Human Activity using Sequential Data detect human activities generated by sensor-based wearable devices
BAMresearch / WEBSLAMDSequential Learning App for Materials Discovery ("SLAMD") - Web Version
wangjlgz / MetaTLCode for "Sequential Recommendation for Cold-start Users with Meta Transitional Learning" (SIGIR2021)
Murkey8895 / FoldsformerCode for the paper "Foldsformer: Learning Sequential Multi-Step Cloth Manipulation With Space-Time Attention" (RA-L)
LFM-bot / IOCRecPytorch implementation for paper: Multi-Intention Oriented Contrastive Learning for Sequential Recommendation (WSDM23)
StanislavGrigoriev / EasyCNTKC# library for easy Deep Learning and Deep Reinforcement Learning. It is wrapper over C# CNTK API. Has implementation of layers (LSTM, Convolution etc.), optimizers, losses, shortcut-connections, sequential model, sequential multi-output model, agent teachers, policy gradients, actor-critic etc. Contains helpers for work with dataset (split, statistics, SMOTE etc). Allows train, evaluate and inference deep neural networks in style similar to Keras.
tudelft / Taming Event FlowTaming Contrast Maximization for Learning Sequential Event-based Optical Flow Estimation
JamZheng / CL4SRec PytorchA pytorch implementation of CL4SRec in ''Contrastive Learning for Sequential Recommendation", ICDE'22.
reddyprasade / Machine Learning Interview PreparationPrepare to Technical Skills Here are the essential skills that a Machine Learning Engineer needs, as mentioned Read me files. Within each group are topics that you should be familiar with. Study Tip: Copy and paste this list into a document and save to your computer for easy referral. Computer Science Fundamentals and Programming Topics Data structures: Lists, stacks, queues, strings, hash maps, vectors, matrices, classes & objects, trees, graphs, etc. Algorithms: Recursion, searching, sorting, optimization, dynamic programming, etc. Computability and complexity: P vs. NP, NP-complete problems, big-O notation, approximate algorithms, etc. Computer architecture: Memory, cache, bandwidth, threads & processes, deadlocks, etc. Probability and Statistics Topics Basic probability: Conditional probability, Bayes rule, likelihood, independence, etc. Probabilistic models: Bayes Nets, Markov Decision Processes, Hidden Markov Models, etc. Statistical measures: Mean, median, mode, variance, population parameters vs. sample statistics etc. Proximity and error metrics: Cosine similarity, mean-squared error, Manhattan and Euclidean distance, log-loss, etc. Distributions and random sampling: Uniform, normal, binomial, Poisson, etc. Analysis methods: ANOVA, hypothesis testing, factor analysis, etc. Data Modeling and Evaluation Topics Data preprocessing: Munging/wrangling, transforming, aggregating, etc. Pattern recognition: Correlations, clusters, trends, outliers & anomalies, etc. Dimensionality reduction: Eigenvectors, Principal Component Analysis, etc. Prediction: Classification, regression, sequence prediction, etc.; suitable error/accuracy metrics. Evaluation: Training-testing split, sequential vs. randomized cross-validation, etc. Applying Machine Learning Algorithms and Libraries Topics Models: Parametric vs. nonparametric, decision tree, nearest neighbor, neural net, support vector machine, ensemble of multiple models, etc. Learning procedure: Linear regression, gradient descent, genetic algorithms, bagging, boosting, and other model-specific methods; regularization, hyperparameter tuning, etc. Tradeoffs and gotchas: Relative advantages and disadvantages, bias and variance, overfitting and underfitting, vanishing/exploding gradients, missing data, data leakage, etc. Software Engineering and System Design Topics Software interface: Library calls, REST APIs, data collection endpoints, database queries, etc. User interface: Capturing user inputs & application events, displaying results & visualization, etc. Scalability: Map-reduce, distributed processing, etc. Deployment: Cloud hosting, containers & instances, microservices, etc. Move on to the final lesson of this course to find lots of sample practice questions for each topic!
Intelligent-Driving-Laboratory / Reinforcement Learning For Sequential Decision And Optimal ControlSource Code for "Reinforcement Learning for Sequential Decision and Optimal Control" by Shengbo Eben Li
sayZeeel / EE5611 ProjectMachine Learning for Wireless communications course project on Sequential Convolutional Recurrent Neural Networks for fast automatic modulation classification.
chiuph / SCALE PINNScale-PINN: Learning Efficient Physics-Informed Neural Networks Through Sequential Correction
RickZack / FedSeqOfficial PyTorch implementation of "Speeding up Heterogeneous Federated Learning with Sequentially Trained Superclients", accepted at ICPR 2022
chandraprvkvsh / Continual Learning For TransformersContinual Learning for Transformers that allows training on multiple tasks sequentially while preserving knowledge from earlier tasks using Elastic Weight Consolidation.
webvokess / Google Stock Price Prediction Using LSTMGoogle Stock Price Prediction using Long Short-Term Memory (LSTM) is a deep learning-based approach to forecasting stock prices using historical data. LSTM is a type of recurrent neural network (RNN) that is well-suited for sequential data like stock prices
