vlgiitr / Dl_topics
List of DL topics and resources essential for cracking interviews
Stars: ✭ 392
Projects that are alternatives of or similar to Dl topics
Spacy Streamlit
👑 spaCy building blocks and visualizers for Streamlit apps
Stars: ✭ 360 (-8.16%)
Mutual labels: natural-language-processing
Beginner nlp
A curated list of beginner resources in Natural Language Processing
Stars: ✭ 376 (-4.08%)
Mutual labels: natural-language-processing
Multiwoz
Source code for end-to-end dialogue model from the MultiWOZ paper (Budzianowski et al. 2018, EMNLP)
Stars: ✭ 384 (-2.04%)
Mutual labels: natural-language-processing
Matchzoo Py
Facilitating the design, comparison and sharing of deep text matching models.
Stars: ✭ 362 (-7.65%)
Mutual labels: natural-language-processing
Data Science
Collection of useful data science topics along with code and articles
Stars: ✭ 315 (-19.64%)
Mutual labels: natural-language-processing
Natural Language Processing
Programming Assignments and Lectures for Stanford's CS 224: Natural Language Processing with Deep Learning
Stars: ✭ 377 (-3.83%)
Mutual labels: natural-language-processing
Text mining resources
Resources for learning about Text Mining and Natural Language Processing
Stars: ✭ 358 (-8.67%)
Mutual labels: natural-language-processing
Armadillo Code
Armadillo: fast C++ library for linear algebra & scientific computing - http://arma.sourceforge.net
Stars: ✭ 388 (-1.02%)
Mutual labels: linear-algebra
Nlp Python Deep Learning
NLP in Python with Deep Learning
Stars: ✭ 374 (-4.59%)
Mutual labels: natural-language-processing
Nlpnet
A neural network architecture for NLP tasks, using cython for fast performance. Currently, it can perform POS tagging, SRL and dependency parsing.
Stars: ✭ 379 (-3.32%)
Mutual labels: natural-language-processing
Nlp
[UNMANTEINED] Extract values from strings and fill your structs with nlp.
Stars: ✭ 367 (-6.38%)
Mutual labels: natural-language-processing
Awesome Text Generation
A curated list of recent models of text generation and application
Stars: ✭ 370 (-5.61%)
Mutual labels: natural-language-processing
Start Machine Learning In 2020
A complete guide to start and improve in machine learning (ML), artificial intelligence (AI) in 2021 without ANY background in the field and stay up-to-date with the latest news and state-of-the-art techniques!
Stars: ✭ 357 (-8.93%)
Mutual labels: linear-algebra
Awesome Search
Awesome Search - this is all about the (e-commerce) search and its awesomeness
Stars: ✭ 361 (-7.91%)
Mutual labels: natural-language-processing
Transformers Tutorials
Github repo with tutorials to fine tune transformers for diff NLP tasks
Stars: ✭ 384 (-2.04%)
Mutual labels: natural-language-processing
Question generation
Neural question generation using transformers
Stars: ✭ 356 (-9.18%)
Mutual labels: natural-language-processing
Usc Ds Relationextraction
Distantly Supervised Relation Extraction
Stars: ✭ 378 (-3.57%)
Mutual labels: natural-language-processing
My Cs Degree
A CS degree with a focus on full-stack ML engineering, 2020
Stars: ✭ 391 (-0.26%)
Mutual labels: natural-language-processing
Tf Seq2seq
Sequence to sequence learning using TensorFlow.
Stars: ✭ 387 (-1.28%)
Mutual labels: natural-language-processing
Nlp Progress
Repository to track the progress in Natural Language Processing (NLP), including the datasets and the current state-of-the-art for the most common NLP tasks.
Stars: ✭ 19,518 (+4879.08%)
Mutual labels: natural-language-processing
Deep Learning Interview Topics
This repo contains a list of topics which we feel that one should be comfortable with before appearing for a DL interview. This list is by no means exhaustive (as the field is very wide and ever growing).
Mathematics
- Linear Algebra([notes][practice questions])
- Linear Dependence and Span
- Eigendecomposition
- Eigenvalues and Eigenvectors
- Singular Value Decomposition
- Probability and Statistics([notes][youtube series])
- Expectation, Variance and Co-variance
- Distributions
- Random Walks
- Bias and Variance
- Bias Variance Trade-off
- Estimators
- Biased and Unbiased
- Maximum Likelihood Estimation
- Maximum A Posteriori (MAP) Estimation
- Information Theory
- (Shannon) Entropy
- Cross Entropy
- KL Divergence
- Not a distance metric
- Derivation from likelihood ratio (Blog)
- Always greater than 0
- Proof by Jensen's Inequality
- Relation with Entropy (Explanation)
Basics
- Backpropogation
- Vanilla (blog)
- Backprop in CNNs
- Gradients in Convolution and Deconvolution Layers
- Backprop through time
- Loss Functions
- MSE Loss
- Derivation by MLE and MAP
- Cross Entropy Loss
- Binary Cross Entropy
- Categorical Cross Entropy
- MSE Loss
- Activation Functions (Sigmoid, Tanh, ReLU and variants) (blog)
- Optimizers
- Regularization
- Early Stopping
- Noise Injection
- Dataset Augmentation
- Ensembling
- Parameter Norm Penalties
- L1 (sparsity)
- L2 (smaller parameter values)
- BatchNorm (Paper)
- Internal Covariate Shift
- BatchNorm in CNNs (Link)
- Backprop through BatchNorm Layer (Explanation)
- Dropout (Paper) (Notes)
Computer Vision
- ILSVRC
- Object Recognition (Blog)
- Convolution
- Cross-correlation
- Pooling (Average, Max Pool)
- Strides and Padding
- Output volume dimension calculation
- Deconvolution (Transpose Conv.), Upsampling, Reverse Pooling (Visualization)
Natural Language Processing
- Recurrent Neural Networks
- Word Embeddings
- Word2Vec
- CBOW
- Glove
- FastText
- SkipGram, NGram
- ELMO
- OpenAI GPT
- BERT (Blog)
- Transformers (Paper) (Code) (Blog)
- BERT (Paper)
- Universal Sentence Encoder
Generative Models
- Generative Adversarial Networks (GANs)
- Variational Autoencoders (VAEs)
- Variational Inference (tutorial paper)
- ELBO and Loss Function derivation
- Normalizing Flows
Misc
- Triplet Loss
- BLEU Score
- Maxout Networks
- Support Vector Machines
- Maximal-Margin Classifier
- Kernel Trick
- PCA (Explanation)
- PCA using neural network
- Architecture
- Loss Function
- PCA using neural network
- Spatial Transformer Networks
- Gaussian Mixture Models (GMMs)
- Expectation Maximization
More Resources
- Stanford's CS231n Lecture Notes
- Deep Learning Book (Goodfellow et. al.)
Contributing
We welcome contributions to add resources such as notes, blogs, or papers for a topic. Feel free to open a pull request for the same!
Note that the project description data, including the texts, logos, images, and/or trademarks,
for each open source project belongs to its rightful owner.
If you wish to add or remove any projects, please contact us at [email protected].