| Large Language Models are Zero-Shot Rankers for Recommender Systems |
#paper |
| Semantic Product Search for Matching Structured Product Catalogs in E-Commerce |
#paper |
| Crossed Feature |
#interview #deep-learning |
| Feature Hashing |
#interview #deep-learning |
| logarithm |
|
| Sequence-to-Sequence Model |
|
| Causal Language Modeling |
#interview #nlp |
| Decoder Only Transformer |
#interview #nlp |
| Encoder Only Transformer |
#interview #nlp |
| ML Case Study or ML Design |
#interview |
| Time Complexity of ML Algos |
#interview #machine-learning |
| Mean Reciprocal Rank (MRR) |
#interview #retrieval |
| F-Beta@K |
#interview #retrieval |
| Recall@K |
#interview #retrieval |
| Precision@K |
#interview #retrieval |
| Retrieval Metrics |
#interview #retrieval |
| Top-K in Retrieval System |
|
| PyTorch Refresher |
#interview #deep-learning |
| Reno Talk @UMBC on Scale-2024 |
|
| Molmo and PixMo |
#paper |
| What is More Likely to Happen Next |
#Computer-Science---Computation-and-Language #Computer-Science---Artificial-Intelligence #Computer-Science---Computer-Vision-and-Pattern-Recognition #paper |
| Fine Tuning Large Language Models |
#nlp |
| OpenPI-C |
#paper |
| MultiVENT |
#multimodal #paper #dataset |
| Home |
|
| 3 key question in data visualization |
#interview #visualization |
| Merge K-sorted List |
#data-structures #interview |
| Time Complexity of ML Models |
#machine-learning #interview |
| COIN |
#paper |
| Instructional Websites |
#dataset |
| matplotlib functions |
|
| matplotlib legend |
#visualization #interview |
| Essential Visualizations |
#visualization #interview |
| data visualization |
#visualization #interview |
| Self Attention vs. Cross Attention |
|
| MM-LLMs |
|
| Layer Normalization |
#deep-learning #interview |
| Odds Ratio |
#statistics #interview |
| Label Encoding |
#machine-learning #interview |
| Log (Odds Ratio) |
#statistics #interview |
| Amazon Leadership Principles |
#interview #behavioral |
| PyTorch Loss Functions |
#pytorch #machine-learning |
| Probability vs. Likelihood |
#probability #interview |
| spacy-syntactic-dependency |
#nlp #spacy |
| Histogram |
#interview #statistics |
| Exponential Distribution |
#interview #statistics |
| CNN |
#deep-learning #interview #vision |
| DBScan Clustering |
#machine-learning #interview |
| Gradient Descent |
#machine-learning #interview |
| Hierarchical Clustering |
#machine-learning #interview |
| K-means Clustering |
#machine-learning #interview |
| RNN |
#deep-learning #interview |
| Bias & Variance |
#machine-learning #interview #evaluation |
| ROC Curve |
#machine-learning #interview #evaluation |
| AUC Score |
#machine-learning #interview #evaluation |
| L2 or Ridge Regression |
#machine-learning #interview |
| L1 or Lasso Regression |
#machine-learning #interview |
| Multi Layer Perceptron |
#machine-learning #interview |
| Co-Variance |
#interview #statistics |
| Line Equation |
#math #interview |
| LSTM |
#deep-learning #interview |
| GRU |
#deep-learning #nlp #interview |
| BLEU Score |
#nlp #deep-learning #interview |
| Tanh |
#deep-learning #activation-function #interview |
| Domain vs. Codomain vs. Range |
#math |
| One Class Gaussian |
#machine-learning #interview |
| Multivariate Normal Distribution |
#statistics #interview |
| Support Vector |
#machine-learning #interview |
| Margin in SVM |
#machine-learning #interview |
| Random Forest |
#machine-learning #interview |
| Convex vs Nonconvex Function |
#math #interview |
| Saddle Points |
#machine-learning #interview |
| Local Minima |
#machine-learning |
| Global Minima |
#machine-learning |
| Softsign |
#activation-function #deep-learning #interview |
| Softplus |
#deep-learning #activation-function #interview |
| Log-cosh Loss |
#loss-in-ml #machine-learning #deep-learning #interview |
| bisect_left vs. bisect_right |
|
| Log (Odds) |
#statistics #interview |
| Adaboost |
#machine-learning #interview |
| Ensemble Learning |
#machine-learning #interview |
| Stacking or Meta Model in Ensemble Learning |
#machine-learning #interview |
| Bagging |
#machine-learning #interview |
| Boosting |
#machine-learning #interview |
| Machine Learning Algorithm Selection |
#machine-learning #deep-learning #interview |
| Neural Network |
#deep-learning #interview |
| Normal Distribution |
#interview #statistics |
| Activation Function |
#machine-learning #interview #activation-function |
| How To 100M Learning Text Video |
#paper #nlp #dataset |
| Logistic Regression |
#machine-learning #interview |
| Meteor Score |
#nlp #deep-learning #interview |
| AdaGrad |
#deep-learning #interview |
| Decision Tree (Classification) |
#machine-learning #interview |
| ROUGE-L Score |
#deep-learning #nlp #interview |
| ROUGE-LSUM Score |
#deep-learning #nlp #interview |
| ROUGE-N Score |
#nlp #deep-learning #interview |
| Sub-word Tokenizer |
#deep-learning #nlp #interview |
| Kernel in SVM |
#machine-learning #interview |
| Sub-sampling in Word2Vec |
#deep-learning #nlp #interview |
| Word Tokenizer |
#deep-learning #nlp #interview |
| Area Under Precision Recall Curve (AUPRC) |
#machine-learning #interview #evaluation |
| Backward Feature Elimination |
#machine-learning #interview |
| Continuous Bag of Words |
#deep-learning #nlp #interview |
| ELMo Embeddings |
#nlp #interview |
| Foundation Model |
#deep-learning #interview |
| Homonym or Polysemy |
#nlp #interview |
| Neural Network Normalization |
#deep-learning #interview |
| Stochastic Gradient Descent (SGD) |
#machine-learning #interview |
| Word Embeddings |
#deep-learning #nlp #interview |
| AdaDelta |
#machine-learning #interview |
| Adam |
#deep-learning #interview |
| Alternative Hypothesis |
#statistics #interview |
| BERT Embeddings |
#nlp #interview |
| Byte Level BPE |
#deep-learning #nlp #interview |
| Byte Pair Encoding (BPE) |
#deep-learning #interview #nlp |
| Challenges of NLP |
#nlp #interview |
| Co-occurrence based Word Embeddings |
#nlp #interview |
| Character Tokenizer |
#deep-learning #nlp #interview |
| Contextualized Word Embeddings |
#nlp #interview |
| Count based Word Embeddings |
#deep-learning #interview |
| Decoding Strategies |
#nlp #interview |
| Extrinsic Evaluation |
#evaluation #nlp #interview |
| FastText Embedding |
#nlp #interview |
| Forward Feature Selection |
#machine-learning #interview |
| GloVe Embedding |
#nlp #interview |
| Gradient Clipping |
#deep-learning #interview |
| Group Normalization |
#deep-learning #interview |
| Gumbel Softmax |
#deep-learning #interview |
| Intrinsic Evaluation |
#evaluation #nlp #interview |
| Jaccard Similarity |
#statistics #interview |
| Jaccard Distance |
#statistics #interview |
| Leaky ReLU |
#deep-learning #activation-function #interview |
| Min Max Normalization |
#deep-learning #interview |
| Multiset |
#math |
| Negative Sampling |
#deep-learning #nlp #interview |
| Nesterov Accelerated Gradient (NAG) |
#deep-learning #interview |
| One Hot Vector |
#deep-learning #nlp #interview |
| Polynomial Regression |
#machine-learning #interview |
| Precision Recall Curve (PRC) |
#machine-learning #interview #evaluation |
| RMSProp |
#deep-learning #interview |
| Reinforcement Learning from Human Feedback (RLHF) |
#nlp #deep-learning #interview |
| Self-Supervised Learning |
#deep-learning #interview |
| SentencePiece Tokenization |
#nlp #deep-learning #interview |
| Skip Gram Model |
#nlp #deep-learning #interview |
| Stochastic Gradient Descent with Momentum |
#deep-learning #interview |
| Stop Words |
#nlp #interview |
| Tokenizer |
#deep-learning #nlp #interview |
| True Negative Rate |
#evaluation #machine-learning #interview |
| Type 1 Error vs. Type 2 Error |
#machine-learning #interview #evaluation |
| WordPiece Tokenization |
#deep-learning #nlp #interview |
| Word2Vec Embedding |
#nlp #interview |
| Recent Notes |
|
| Sources of Uncertainty |
#probability |
| Second Order Derivative or Hessian Matrix |
#math |
| ReLU |
#machine-learning #interview #activation-function |
| Greedy Decoding |
#deep-learning #interview #nlp |
| Feature Engineering |
#machine-learning #interview |
| F-Beta Score |
#evaluation #machine-learning #interview |
| Gini Impurity |
#machine-learning #interview |
| Entropy and Information Gain |
#machine-learning #interview |
| Binary Cross Entropy |
#machine-learning #interview |
| Batch Normalization |
#deep-learning #interview |
| R-squared Value |
#statistics #interview #evaluation |
| Behavioral Interview |
#interview |
| Questions to ask in a Interview? |
#interview |
| DS & Algo Interview |
#interview #data-structures #algorithms |
| Mean Squared Logarithmic Error (MSLE) |
#machine-learning #interview #evaluation |
| Multivariate Linear Regression |
#machine-learning #interview |
| Multivariable Linear Regression |
#machine-learning #interview |
| p-value |
#statistics #interview |
| Null Hypothesis |
#interview #statistics |
| Elastic Net Regression |
#machine-learning #interview |
| Unsupervised Learning |
#machine-learning #interview |
| ML Interview |
#math #statistics #probability #visualization #machine-learning #deep-learning #loss-in-ml #evaluation #interview #nlp #vision |
| Normalization |
#machine-learning #deep-learning |
| Posterior Probability |
#probability #interview |
| Dropout |
#deep-learning #interview |
| Discriminative vs. Generative Models |
#deep-learning #interview |
| Exploding Gradient |
#deep-learning #interview |
| Negative Log Likelihood |
#interview #probability |
| TF-IDF |
#machine-learning #interview #nlp |
| Confusion Matrix |
#machine-learning #interview #evaluation |
| Mini Batch SGD |
#machine-learning #interview |
| Surprise |
#machine-learning #interview |
| Naive Bayes |
#machine-learning #interview |
| Perceptron |
#machine-learning #interview |
| K-means vs. Hierarchical |
#machine-learning #interview |
| K-nearest Neighbor (KNN) |
#machine-learning #interview |
| How To Write a Paper |
#write-paper #tutorial #paper |
| How to Read a Paper |
#paper |
| Deep Learning by Ian Goodfellow |
#deep-learning #book |
| Advanced NLP with Scipy |
#nlp #spacy #tutorial |
| How to prepare for Behavioral Interview |
#interview |
| Training a Deep Neural Network |
#deep-learning #interview |
| Bag of Words |
#nlp #interview |
| Softmax |
#machine-learning #interview #activation-function |
| Mutual Information |
#statistics #interview |
| Splitting tree in Decision Tree |
#machine-learning #interview |
| Standardization or Normalization |
#machine-learning #deep-learning #interview |
| Support Vector Machine (SVM) |
#machine-learning #interview |
| Maximal Margin Classifier |
#machine-learning #interview |
| Three Way Partioning |
#algorithms #interview |
| Feature Preprocessing |
#interview #deep-learning |
| How to Choose Kernel in SVM |
#machine-learning #interview |
| Cosine Similarity |
#machine-learning #statistics #interview |
| False Negative Error |
#machine-learning #interview |
| Dynamic Programming (DP) in python |
#interview #algorithms #python |
| Data Imputation |
#machine-learning #interview |
| Multi Label Cross Entropy |
#machine-learning #interview |
| Contrastive Learning |
#deep-learning #nlp #interview |
| Averaging in Ensemble Learning |
#machine-learning #interview |
| Recall |
#machine-learning #interview #evaluation |
| Precision |
#machine-learning #interview #evaluation |
| True Positive Rate |
#machine-learning #interview #evaluation |
| False Positive Rate |
#machine-learning #interview #evaluation |
| Gradient Boost (Regression) |
#machine-learning #interview |
| Gradient Boosting |
#machine-learning #interview |
| Decision Tree (Regression) |
#machine-learning #interview |
| Decision Tree |
#machine-learning #interview |
| Simple Linear Regression |
#machine-learning #interview |
| Independent Variable |
#machine-learning #interview |
| Dependent Variable |
#interview #machine-learning |
| Linear Regression |
#machine-learning #interview |
| Adjusted R-squared Value |
#interview #statistics #evaluation |
| Marginal Probability |
#interview #probability |
| Matrices |
#linear-algebra #math |
| Maximum Likelihood |
#probability #interview |
| Mean Absolute Error (MAE) |
#evaluation #machine-learning #deep-learning #interview #loss-in-ml |
| Mean Absolute Percentage Error (MAPE) |
#machine-learning #deep-learning #loss-in-ml #interview |
| Mean |
#interview #statistics |
| Median |
#interview #statistics |
| Merge Overlapping Intervals |
#algorithms #interview |
| Mean Squared Error (MSE) |
#machine-learning #loss-in-ml #interview #evaluation |
| Mode |
#statistics #interview |
| Multi Class Cross Entropy |
#interview #machine-learning #loss-in-ml |
| Model Based vs. Instance Based Learning |
#machine-learning #interview |
| N-gram Method |
#nlp #interview |
| One Class Classification |
#machine-learning #interview |
| Odds |
#statistics #interview |
| One vs Rest or One vs All Multi Class Classification |
#machine-learning #interview |
| One vs One Multi Class Classification |
#machine-learning #interview |
| Overcomplete Autoencoder |
#deep-learning #nlp #interview |
| Oversampling |
#machine-learning #interview |
| PCA vs. Autoencoder |
#deep-learning #machine-learning #interview |
| Padding in CNN |
#deep-learning #vision #interview |
| Parameter vs. Hyperparameter |
#machine-learning #deep-learning #interview |
| Perplexity |
#nlp #deep-learning #interview #evaluation |
| Pooling |
#deep-learning #interview |
| Population |
#statistics #interview |
| Prior Probability |
#probability #interview |
| Probability Density Function |
#probability #interview |
| Principal Component Analysis (PCA) |
#machine-learning #interview |
| Probability Distribution |
#probability #interview |
| Probability Mass Function |
#probability #interview |
| Problem Solving Algorithm Selection |
#algorithms #data-structures #interview |
| Quintile or Percentile |
#statistics #interview |
| Quotient Rule or Differentiation of Division |
#math #interview |
| Random Variable |
#probability #interview |
| Regularization |
#deep-learning #interview |
| Reinforcement Learning |
#deep-learning #machine-learning #interview |
| Root Mean Squared Error (RMSE) |
#evaluation #machine-learning #deep-learning #interview |
| Root Mean Squared Logarithmic Error (RMSLE) |
#machine-learning #deep-learning #evaluation #interview |
| SVC |
#machine-learning #interview |
| Sensitivity |
#machine-learning #interview #evaluation |
| Semi-supervised Learning |
#machine-learning #interview |
| Soft Margin in SVM |
#machine-learning #interview |
| Some Common Behavioral Questions |
#interview |
| Specificity |
#machine-learning #interview #evaluation |
| Standard deviation |
#interview #statistics |
| Standardization |
#machine-learning #deep-learning #interview |
| Statistical Significance |
#statistics #interview |
| Stride in CNN |
#deep-learning #vision #interview |
| Stump |
#machine-learning #interview |
| Supervised Learning |
#machine-learning #interview |
| Swallow vs. Deep Learning |
#machine-learning #deep-learning #interview |
| Triplet Loss |
#deep-learning #loss-in-ml #interview |
| Undercomplete Autoencoder |
#deep-learning #nlp #interview |
| Undersampling |
#machine-learning #interview |
| Variance |
#interview #statistics |
| diagonal-matrix |
#linear-algebra |
| determinant |
#linear-algebra |
| doing-literature-review |
|
| conditionally-independent-joint-distribution |
#probability |
| frobenius-norm |
#linear-algebra |
| eigenvalue-eigenvector |
#linear-algebra |
| fully-independent-join-distribution |
#probability |
| fully-joint-joint-distribution |
#probability |
| identity-matrix |
#linear-algebra |
| joint-distribuition |
#probability |
| jupyter-notebook-on-server |
#deep-learning |
| lp-norm |
#linear-algebra |
| max-norm |
#linear-algebra |
| orthonormal-vector |
#linear-algebra |
| orthogonal-matrix |
#linear-algebra |
| scalar |
#linear-algebra #math |
| spacy-doc-object |
#nlp #spacy |
| spacy-explanation-of-labels |
#nlp #spacy |
| norm |
#linear-algebra |
| spacy-matcher |
#spacy #nlp |
| spacy-doc-span-token |
#nlp #spacy |
| spacy-named-entities |
#nlp #spacy |
| spacy-operator-quantifier |
#spacy #nlp |
| spacy-pipeline |
#nlp #spacy |
| spacy-pos |
#nlp #spacy |
| spacy-semantic-similarity |
#nlp #spacy |
| spacy-pattern |
#spacy #nlp |
| trace-operator |
#linear-algebra |
| unit-vector |
#linear-algebra |
| vector |
#linear-algebra #math |
| Accuracy |
#machine-learning #interview #evaluation |
| Autoencoder for Denoising Images |
#deep-learning #vision #interview |
| Bayes Theorem |
#probability #interview |
| Beam Search |
#deep-learning #nlp #interview |
| Bidirectional RNN or LSTM |
#nlp #deep-learning #interview |
| Binomial Distribution |
#probability #statistics #interview |
| Binning or Bucketing |
#machine-learning #deep-learning #interview |
| Bayesian Optimization Hyperparameter Finding |
#deep-learning #machine-learning #interview |
| Causality vs. Correlation |
#statistics #interview |
| Chain Rule |
#math #interview |
| Collinearity |
#machine-learning #statistics #interview |
| Conditional Probability |
#interview #probability |
| Continuous Random Variable |
#probability #interview |
| Contrastive Loss |
#deep-learning #loss-in-ml #interview |
| Data Augmentation |
#machine-learning #interview |
| Curse of Dimensionality |
#machine-learning #interview |
| Cross Validation |
#machine-learning #interview |
| Cross Entropy |
#machine-learning #interview |
| Decision Boundary |
#machine-learning |
| Density Sparse Data |
#machine-learning #interview |
| Derivative |
#math #machine-learning #interview |
| Differentiation |
#math #machine-learning #interview |
| Differentiation of Product |
#math #interview |
| Discrete Random Variable |
#probability #interview |
| Dying ReLU |
#machine-learning #interview #deep-learning |
| Eigendecomposition |
#linear-algebra #math |
| Entropy |
#machine-learning #interview #loss-in-ml |
| Estimated Mean |
#interview #statistics |
| Estimated Variance |
#statistics #interview |
| Estimated Standard Deviation |
#interview |
| Euclidian Norm |
#math #interview |
| Expected Value |
#probability #interview |
| Expected Value for Discrete Events |
#probability #interview |
| F1 Score |
#machine-learning #interview #evaluation |
| Feature Extraction |
#machine-learning #interview |
| Finding Co-relation between two data or distribution |
#statistics #interview |
| GBM |
#machine-learning #interview |
| Gaussian Distribution |
#machine-learning #interview |
| Genetic Algorithm Hyperparameter Finding |
#machine-learning #deep-learning #interview |
| Gradient Boost (Classification) |
#machine-learning #interview |
| Gradient |
#math #machine-learning #interview |
| Grid Search Hyperparameter Finding |
#deep-learning #machine-learning #interview |
| Handling Imbalanced Dataset |
#machine-learning #interview |
| Heapq (nlargest or nsmalles) |
#data-structures #interview |
| How to combine in Ensemble Learning |
#machine-learning #interview |
| Huber Loss |
#loss-in-ml #machine-learning #deep-learning #interview |
| Hyperparameters |
#deep-learning #interview |
| Integration by Parts or Integration of Product |
#math |
| interview |
#interview #nlp #cv |
| K Fold Cross Validation |
#machine-learning #interview |
| Kernel Regression |
#machine-learning #interview |
| KL Divergence |
#machine-learning #interview |
| L1 vs. L2 Regression |
#machine-learning #deep-learning #interview |
| Likelihood |
#probability #interview |
| Log Scale |
#statistics #interview |
| Logistic Regression vs. Neural Network |
#machine-learning #deep-learning #interview |
| Loss vs. Cost |
#machine-learning #interview |
| Machine Learning vs. Deep Learning |
#machine-learning #deep-learning #interview |
| Majority vote in Ensemble Learning |
#machine-learning #interview |