Bookmarks to the selected Deep Learning / Machine Learning Resources on the Web
Author: Murat Karakaya
Date created: 19 May 2020
Last modified: 15 Dec 2021
Description: In this post, I share my bookmarks classified according to specific topics in Deep Learning / Machine Learning. Thus, you can save your time searching for similar information on the web. If you have any comments or updates please feel free to share with me!
If you are interested in Deep Learning / Machine learning, you can find hundreds of video tutorials with Python code samples in Jupyter notebooks at the following links:
- Murat Karakaya Akademi YouTube Channel in English or Turkish
- muratkarakaya.net
- Kaggle
- Github pages
- Github Repo
Adversarial attacks
- Adversarial machine learning: The underrated threat of data poisoning — TechTalks
- benedekrozemberczki/awesome-graph-classification: A collection of important graph embedding, classification and representation learning papers with implementations.
- Number plate recognition with Tensorflow — Matt’s ramblings
- OpenALPR — Automatic License Plate Recognition
- Number plate detection with Supervisely and Tensorflow (Part 1)
- Number plate detection on Indian car vehicles using YOLOv2
- Car Plate Recognition System with Raspberry Pi and Node-RED | Random Nerd Tutorials
- Machine Learning Automatic License Plate Recognition ~ Dror Gluska
- DeepSystems/supervisely-tutorials: 🌈 Tutorials for Supervise.ly
- Sequence of digits recognition and localization — Petr Marek
- Digit Sequence Recognition using Deep Learning · Sajal Sharma
- Convolutional neural networks-based intelligent recognition of Chinese license plates | SpringerLink
- Robust license plate recognition using neural networks trained on synthetic images — ScienceDirect
- Toward End-to-End Car License Plate Detection and Recognition With Deep Neural Networks — IEEE Journals & Magazine
- Vehicle license plate detection using region-based convolutional neural networks | SpringerLink
- Automatic License Plate Recognition via sliding-window darknet-YOLO deep learning — ScienceDirect
- AVLab — AOLP DATASET
- Number Plate Datasets | Plate Recognizer ALPR
- Computational Vision: [Data Sets]
- PKU VehicleID — Multimedia Learning Group
Attention Layer
- (3) Attention in Neural Networks — YouTube
- A Brief Overview of Attention Mechanism — SyncedReview — Medium
- Intuitive Understanding of Attention Mechanism in Deep Learning
- Image Captioning with Attention | TensorFlow Core
- [1706.03762] Attention Is All You Need
- Attention in Deep Networks with Keras — Towards Data Science
- Attention Mechanism
- Attention mechanism — Heuritech — Medium
- A Comprehensive Guide to Attention Mechanism in Deep Learning
- The Annotated GPT-2 | Making commits each day, towards a better future
- Visualizing A Neural Machine Translation Model (Mechanics of Seq2seq Models With Attention) — Jay Alammar — Visualizing machine learning one concept at a time.
- Getting started with Attention for Classification — matthewmcateer.me
- nmt_with_attention.ipynb — Colaboratory
- Attention Mechanisms
- Attention in NLP: One Key to Making Pre-Training Successful
- Attention in Recommendation Systems — AI³ | Theory, Practice, Business — Medium
- How to Develop an Encoder-Decoder Model with Attention in Keras
- Neural machine translation with attention | TensorFlow Core
- Tensorflow Keras Attention source code line-by-line explained — mc.ai
- Deep Learning(CS7015): Lec 15.3 Attention Mechanism — YouTube
- Attention, CNN and what not for Text Classification | by Rahul Agarwal | Towards Data Science
- Text Classification with Hierarchical Attention Network
- Neural Machine Translation using Bahdanau Attention Mechanism | by Yash Marathe | Analytics Vidhya | Medium
- Attention? Attention!
- Building Seq2Seq LSTM with Luong Attention in Keras for Time Series Forecasting | by Huangwei | Level Up Coding
- Attention: Sequence 2 Sequence model with Attention Mechanism | by Renu Khandelwal | Towards Data Science
- Rasa Algorithm Whiteboard — Attention 1: Self Attention — YouTube
- Rasa Algorithm Whiteboard — Attention 2: Keys, Values, Queries — YouTube
- Rasa Algorithm Whiteboard — Attention 3: Multi Head Attention — YouTube
- Implementing Neural Machine Translation with Attention mechanism using Tensorflow | by Renu Khandelwal | Towards Data Science
- Illustrated: Self-Attention. Step-by-step guide to self-attention… | by Raimi Karim | Towards Data Science
- How to get meaning from text with language model BERT | AI Explained — YouTube
- Lecture 12.1 Self-attention — YouTube
- (Part 2) Multi-Head Attention — A Detailed Intuitive Guide to Transformers Neural Networks — YouTube
- (Part 0) The Rise of Transformers — A Detailed Intuitive Guide to Transformers Neural Networks — YouTube
- jbcordonnier.com/posts/attention-cnn/
Auto ML tools
Autoencoder
- Autoencoders made simple — Towards Data Science
- Auto-Encoder: What Is It? And What Is It Used For? (Part 1)
- What The Heck Are VAE-GANs? — Towards Data Science
- Building Autoencoders in Keras
- Adversarial Autoencoders on MNIST dataset Python Keras Implementation
- Variational Autoencoders Explained
- Intuitively Understanding Variational Autoencoders — Towards Data Science
- Intuitively Understanding Variational Autoencoders — Towards Data Science
- Tutorial — What is a variational autoencoder? — Jaan Altosaar
- Tutorial — What is a variational autoencoder? — Jaan Altosaar
- A Tutorial on Variational Autoencoders with a Concise Keras Implementation | Louis Tiao
- A Tutorial on Variational Autoencoders with a Concise Keras Implementation | Louis Tiao
- Visualizing MNIST using a Variational Autoencoder | Kaggle
- Visualizing MNIST using a Variational Autoencoder | Kaggle
- Build a simple Image Retrieval System with an Autoencoder
- Applied Deep Learning — Part 3: Autoencoders — Towards Data Science
- Implementation Notes: Take the 60-day K-line feature of the VGG16 AutoEncoder — Hallblazzar: Developer Blog — Medium
- How can we reverse the order of the layers in a pre-trained network like VGG in Keras (or other frameworks) while substituting the Maxpooling layers with Upsampling layers? I would like to make an autoencoder from ImageNet pretrained CNNs. — Quora
- Visualizing Autoencoders with Tensorflow.js
Backends
- Deeplearning4j: Open-source, Distributed Deep Learning for the JVM
- Welcome — Theano 1.0.0 documentation
- SKIL CE 1.0.0
- Torch | Scientific computing for LuaJIT.
- Caffe | Deep Learning Framework
- Amazon Deep Learning AMIs
- Kaggle: Your Home for Data Science
- Hello, Colaboratory — Colaboratory
CNN
- A Gentle Introduction to Pooling Layers for Convolutional Neural Networks
- An Introduction to different Types of Convolutions in Deep Learning
- Keras Conv2D and Convolutional Layers — PyImageSearch
Colab
- Tutorial on Using Google Colab for Kaggle Competition
- Importing data to Google Colaboratory — Steemit
- Load Google Drive CSV into Pandas DataFrame for Google Colaboratory — Nick Ali
- python — Google Colab is very slow compared to my PC — Stack Overflow
- Dev steps to Google Colab — Manish Verma — Medium
Courses
- Convolutional Neural Networks for Image Processing | DataCamp
- Free Online Course: Deep Learning in Computer Vision from Coursera | Class Central
- Machine Learning Crash Course | Google Developers
- Yapay Zeka ve Makine Öğrenmesi Teknolojilerine Giriş Eğitimi | Wissen Akademie
- Yann LeCun’s Deep Learning Course at CDS — NYU Center for Data Science
- Introduction to Machine Learning — Global AI Hub
- CS50’s Introduction to Artificial Intelligence with Python
Data Augmentation
- Keras ImageDataGenerator and Data Augmentation — PyImageSearch
- aleju/imgaug: Image augmentation for machine learning experiments.
- SMOTE for Imbalanced Classification with Python
Data Sets
- NYC Taxi & Limousine Commission — Trip Record Data
- Tencent Traffic Sign Dataset 100K
- Datasets
- Dataset Search
- Dataset Search
- ai.stanford.edu/~jkrause/cars/car_dataset.html
- Toronto COCO-QA Dataset
- COCO — Common Objects in Context
- Google AI Blog: Conceptual Captions: A New Dataset and Challenge for Image Captioning
- Visual Geometry Group Home Page
- 04_Reverse_Image_Caption
- models/detection_model_zoo.md at master · tensorflow/models
- IMAGENET VGG16 — Convolutional Network for Classification and Detection
- IAPR TC-12 Benchmark | ImageCLEF / LifeCLEF — Multimedia Retrieval in CLEF
- IAPR TC-12 | Kaggle
- Multi label datasets
- DFC15_multilabel — Google Drive
- 2015 IEEE GRSS Data Fusion Contest — GRSS | IEEE | Geoscience & Remote Sensing Society
- Mapillary — Street-level imagery, powered by collaboration and computer vision
- The Big Bad NLP Database — Quantum Stat
- multilabel datasets
- OpenML Dataset
- VizWiz — Algorithms to assist people who are blind
- Translation Task — ACL 2016 First Conference on Machine Translation
- Amazon question/answer data
- The Stanford Natural Language Processing Group
- dataTurkey
- Top 5 Sources For Analytics and Machine Learning Datasets — GreatLearning
- GitHub — deeplearningturkiye/turkce-yapay-zeka-kaynaklari: Türkiye’de yapılan derin öğrenme (deep learning) ve makine öğrenmesi (machine learning) çalışmalarının derlendiÄŸi sayfa.
- Veri Kümelerimiz — KEMÄ°K
- Deniz Yuret’s Homepage: Turkish Language Resources
- LONWEB PARALLEL TEXTS TURKISH — DAISY STORIES — THE SURPRISE
- google-research-datasets/Objectron: Objectron is a dataset of short, object-centric video clips. In addition, the videos also contain AR session metadata including camera poses, sparse point-clouds and planes. In each video, the camera moves around and ab
- VisualData — Search Engine for Computer Vision Datasets
- GitHub — ashishpatel26/500-AI-Machine-learning-Deep-learning-Computer-vision-NLP-Projects-with-code: 500 AI Machine learning Deep learning Computer vision NLP Projects with code
Demos
- Deep Learning to Identify Features and Automatically Tag in an Image — Algorithmia
- Image Annotation — Image tagging services for Computer Vision
- Lab41
- Inpainting Demo
- (8) Research at NVIDIA: AI Reconstructs Photos with Realistic Results — YouTube
- 18 Impressive Applications of Generative Adversarial Networks (GANs)
- Image Annotation — Image tagging services for Computer Vision
- OpenALPR Cloud API
- This Person Does Not Exist
- How Deepfakes Scramble Our Sense of True and False | WIRED
- RunwayML | Machine learning for creators.
- 3D Photography using Context-aware Layered Depth Inpainting
- Artificial Intelligence Is Rushing Into Patient Care — And Could Raise Risks — Scientific American
- (1) aydao (@AydaoGMan) / Twitter
- (1) Two Minute Papers 📜 (@twominutepapers) / Twitter
- Cyril Diagne on Twitter: “4/10 — Cut & paste your surroundings to Photoshop Code: https://t.co/cVddH3u3ik Book: @HOLOmagazine Garment: SS17 by @thekarentopacio Type: Sainte Colombe by @MinetYoann @ProductionType Technical Insights: ↓ #ML #AR #AI #AI
- DeepFaceDrawing Generates Photorealistic Portraits from Freehand Sketches — Synced
- Artificial intelligence makes blurry faces look more than 60 times sharper
- Face Image Motion Model (Photo-2-Video) Eng.ipynb — Colaboratory
- Text to Speech Demo
- (1) Bomze (@tg_bomze) / Twitter
- Talk to Transformer — InferKit
- L1 Introduction — CS294–158-SP20 Deep Unsupervised Learning — UC Berkeley, Spring 2020 — YouTube
- CloudCV
- (6) Generating coherent speech and gesture from text — YouTube
- Learning to Fix Programs from Error Messages | SAIL Blog
- (29) One-Shot Free-View Neural Talking-Head Synthesis for Video Conferencing — YouTube
- TensorSpace.js
- MyHeritage — MyHeritage
- Google AI Blog: The Technology Behind Cinematic Photos
- GPT-3 Examples, Demos, Showcase, and NLP Use-cases | GPT-3 Demo
- AllenNLP — Demo
- Interactive text-driven image manipulation — YouTube
- 30 amazing applications of deep learning — Yaron Hadad
- Mindwriting: Software That Transform Thoughts Into Words and Sentences
- PimEyes: Face Recognition Search Engine and Reverse Image Search
- What Have Language Models Learned?
- Checking out a 6-Billion parameter GPT model, GPT-J, from Eleuther AI — YouTube
- GitHub Copilot · Your AI pair programmer
Deployment
- Tutorial 6 :Deployment of Machine Learning Models in Google Cloud Platform — YouTube
- How to Deploy a FastAPI App on Heroku for Free | by Shinichi Okada | Towards Data Science
Ensembles
Explanatory Data Analysis
- Facets — Visualizations for ML datasets
- Approaching (Almost) Any Machine Learning Problem | Abhishek Thakur | No Free Hunch
- pandas_profiling API documentation
Fastapi
- How to properly ship and deploy your machine learning model
- Tutorial: How to deploy your ConvNet classifier with Keras and FastAPI — MachineCurve
- Using fetch to Send HTTP Requests in JavaScript
- Jinja2 Explained in 5 Minutes! — codeburst
- Semantic UI
- FastAPI and Scikit-Learn: Easily Deploy Models
- What is React
Flask
- Python Flask Windows Development Environment Setup — Timmy Reilly’s Blog
- How to build a web application using Flask and deploy it to the cloud
- Building a simple Keras + deep learning REST API
GAN
- GAN — What is Generative Adversary Networks GAN? — Jonathan Hui — Medium
- Introductory guide to Generative Adversarial Networks (GANs)
- GAN by Example using Keras on Tensorflow Backend — Towards Data Science
- Generative Adversarial Networks Tutorial (article) — DataCamp
- [1807.07560] Compositional GAN: Learning Image-Conditional Binary Composition
- Generative Adversarial Network(GAN) using Keras — Data Driven Investor — Medium
- Deep Learning — Generative Adversarial Network(GAN)
- Keep Calm and train a GAN. Pitfalls and Tips on training Generative Adversarial Networks
- A Beginner’s Guide to Generative Adversarial Networks (GANs) | Skymind
- How to Develop a Conditional GAN (cGAN) From Scratch
- GitHub — soumith/ganhacks: starter from “How to Train a GAN?” at NIPS2016
- GitHub — How to Train a GAN? Tips and tricks to make GANs work
- How to Develop an Information Maximizing GAN (InfoGAN) in Keras
- How to Develop an Auxiliary Classifier GAN (AC-GAN) From Scratch with Keras
- Using the latest advancements in deep learning to predict stock price movements
- Generative Adversarial Networks for Text Generation — Part 1
- Generative Adversarial Networks for Text Generation — Part 2: RL
- How to Implement CycleGAN Models From Scratch With Keras
- GANs vs. Autoencoders: Comparison of Deep Generative Models
- [Vinesauce] Vinny — Text to Image — YouTube
- 18 Impressive Applications of Generative Adversarial Networks (GANs)
- How to use the UpSampling2D and Conv2DTranspose Layers in Keras
- GANs in computer vision — Introduction to generative learning | AI Summer
- GANs in computer vision — Conditional image and object generation | AI Summer
- Generative Adversarial Networks and TF-GAN (ML Tech Talks) — YouTube
Image Captioning
- Google AI Blog: A picture is worth a thousand (coherent) words: building a natural description of images
- image-caption-generator | Kaggle
- image-caption-generator | Kaggle
- A Gentle Introduction to Calculating the BLEU Score for Text in Python
- Entity Extraction Using NLP in Python | Opensense Labs
- Tutorial_10_final.ipynb — Colaboratory
- Image Captioning with Keras — Towards Data Science
- www.captionbot.ai
- How to Prepare Text Data for Deep Learning with Keras
- Sentiment Analysis using 1D Convolutional Neural Networks in Keras
- Automatic Image Captioning : Building an image-caption generator from scratch !
- How to Develop a Deep Learning Photo Caption Generator from Scratch
- Python based Project — Learn to Build Image Caption Generator with CNN & LSTM — DataFlair
- TF-IDF/Term Frequency Technique: Easiest explanation for Text classification in NLP using Python (Chatbot training on words)
Image Detection
- ArunMichaelDsouza/tensorflow-image-detection: A generic image detection program that uses Google’s Machine Learning library, Tensorflow and a pre-trained Deep Learning Convolutional Neural Network model called Inception.
- Object Detection with 10 lines of code — Towards Data Science
- YOLO for detection of bounding boxes — TensorFlow | Kaggle
- Object Localization and Detection · Artificial Inteligence
- rykov8/ssd_keras: Port of Single Shot MultiBox Detector to Keras
- keras-team/keras-applications: Reference implementations of popular deep learning models.
- What’s new in YOLO v3? — Towards Data Science
- tzutalin/labelImg: LabelImg is a graphical image annotation tool and label object bounding boxes in images
- Training a custom object detection model — Dana Marie Yu — Medium
- Tutorial: Build your own custom real-time object classifier
- Logo detection in Images using SSD — Towards Data Science
- Classification evaluation examples
- Tutorial on using Keras flow_from_directory and generators
- Hands-On AI Part 14: Image Data Preprocessing and Augmentation | Intel® Software
- Bulk Image Converter download | SourceForge.net
- A Brief History of CNNs in Image Segmentation: From R-CNN to Mask R-CNN
- Conv Nets: A Modular Perspective — colah’s blog
- How to Retrain an Image Classifier for New Categories | TensorFlow
- Learn | Kaggle
- An Intuitive Explanation of Convolutional Neural Networks — the data science blog
- pierluigiferrari/ssd_keras: A Keras port of Single Shot MultiBox Detector
- Keras: Multiple outputs and multiple losses — PyImageSearch
- How to Normalize, Center, and Standardize Images With the ImageDataGenerator in Keras
- How-To: Python Compare Two Images — PyImageSearch
- How to Train an Object Detection Model with Keras
- python — Implementing a custom loss function for object detection — Stack Overflow
- Getting Started With Bounding Box Regression In TensorFlow
- Faster R-CNN (object detection) implemented by Keras for custom data from Google’s Open Images Dataset V4
Image Tagging
- How To Use Deep Learning And Transfer Learning To Tag Images
- Automated image tagging at Booking.com — Booking.com Data Science
- Flexible Image Tagging with Fast0Tag — Gab41
- Illustration2Vec | SIGGRAPH Asia 2015 Technical Briefs
- Advances in deep learning approaches for image tagging
- How I handled imbalanced text data — Yogesh Kothiya — Medium
Leaders
- Andrej Karpathy — Quora
- Ian Goodfellow — Quora
- Dzmitry Bahdanau
- Tomas Mikolov — Facebook Research
- Ilya Sutskever’s home page
- Home — deeplearning.ai
- Stanford Computer Vision Lab
- Andrej Karpathy blog
- Make Your Own Neural Network
- François Chollet (@fchollet) | Twitter
- AAAI 20 / AAAI 2020 Keynotes Turing Award Winners Event / Geoff Hinton, Yann Le Cunn, Yoshua Bengio — YouTube
Loss Functions
- The Unknown Benefits of using a Soft-F1 Loss in Classification Systems
- Understanding binary cross-entropy / log loss: a visual explanation
- Advanced Keras — Constructing Complex Custom Losses and Metrics
- Understanding Categorical Cross-Entropy Loss, Binary Cross-Entropy Loss, Softmax Loss, Logistic Loss, Focal Loss and all those confusing names
- On Custom Loss Functions in Keras — Jafar Ali Habshee — Medium
LSTM GRU
- Illustrated Guide to LSTM’s and GRU’s: A step by step explanation
- Time Series Analysis with LSTM using Python’s Keras Library
- Understanding LSTM and its quick implementation in keras for sentiment analysis.
- Number of parameters in keras lstm
- deep learning — Number of parameters in an LSTM model — Data Science Stack Exchange
- Start Here with Machine Learning
- Dissecting The Role of Return_state and Return_seq Options in LSTM Based Sequence Models | by Suresh Pasumarthi | Medium
- Difference Between Return Sequences and Return States for LSTMs in Keras
- A ten-minute introduction to sequence-to-sequence learning in Keras
- Stacked Long Short-Term Memory Networks
- LSTM layer
- LSTM layer
- colah.github.io
- Animated RNN, LSTM and GRU. Recurrent neural network cells in GIFs | by Raimi Karim | Towards Data Science
- Counting No. of Parameters in Deep Learning Models by Hand | by Raimi Karim | Towards Data Science
Measure the Success
- Testing Classifier Accuracy
- Simple guide to confusion matrix terminology
- How to Use Metrics for Deep Learning with Keras in Python
- Metrics To Evaluate Machine Learning Algorithms in Python
- machine learning — What is the difference between loss function and metric in Keras? — Stack Overflow
- Practical tips for class imbalance in binary classification
- Introduction to ROC curves
- How and When to Use ROC Curves and Precision-Recall Curves for Classification in Python
- GitHub — fmfn/BayesianOptimization: A Python implementation of global optimization with gaussian processes.
- Keras accuracy (metrics) · sagr4019/ResearchProject Wiki
- General Terminology · sagr4019/ResearchProject Wiki
- Keras accuracy (metrics) · sagr4019/ResearchProject Wiki
- evaluation — Micro Average vs Macro average Performance in a Multiclass classification setting — Data Science Stack Exchange
- Why are precision, recall and F1 score equal when using micro averaging in a multi-class problem? — Simon’s blog
- Calculate mean Average Precision (mAP) for multi-label classification
- Breaking Down Mean Average Precision (mAP) — Towards Data Science
- sklearn.metrics.average_precision_score — scikit-learn 0.22.2 documentation
- mAP (mean Average Precision) for Object Detection — Jonathan Hui — Medium
- Precision-Recall — scikit-learn 0.22.2 documentation
- 3.3. Metrics and scoring: quantifying the quality of predictions — scikit-learn 0.22.2 documentation
- sklearn.metrics.average_precision_score — scikit-learn 0.22.2 documentation
- 6 More Evaluation Metrics Data Scientists Should Be Familiar with — Lessons from A High-rank Kagglers’ New Book
- 3.3. Metrics and scoring: quantifying the quality of predictions — scikit-learn 0.22.2 documentation
- 3. Model selection and evaluation — scikit-learn 0.22.2 documentation
- GitHub — Cartucho/mAP: mean Average Precision — This code evaluates the performance of your neural net for object recognition.
- ROC Curves and Precision-Recall Curves for Imbalanced Classification
- Idiot’s Guide to Precision, Recall and Confusion Matrix | Hacker Noon
- Deep Double Descent (OpenAI): Classical Machine Learning vs. Modern Deep Learning (preview) — YouTube
- Keras Metrics: Everything You Need To Know | Neptune blog
- keras/metrics.py at 1c630c3e3c8969b40a47d07b9f2edda50ec69720 · keras-team/keras
- F-beta score for Keras | Kaggle
ML
- Top 10 Machine Learning Algorithms for Beginners
- Machine Learning For Beginners — Towards Data Science
- Machine Learning Tutorial for Beginners | Kaggle
- A Beginner’s Guide to AI/ML 🤖👶 — Machine Learning for Humans — Medium
- (4) Introduction to Bayesian data analysis — part 1: What is Bayes? — YouTube
Multi Stream CNN
- Deep Learning for Videos: A 2018 Guide to Action Recognition
- Driving behaviour recognition from still images by using multi-stream fusion CNN | SpringerLink
Multi-label
- Multi-label image classification Tutorial with Keras ImageDataGenerator
- Multi-label classification with Keras — PyImageSearch
- 1.12. Multiclass and multilabel algorithms — scikit-learn 0.20.3 documentation
- Multi-label classification with keras | Kaggle
- Multi-Label text classification in TensorFlow Keras — knowledge Transfer
- Performing Multi-label Text Classification with Keras | mimacom
- Explore overfitting and underfitting | TensorFlow Core
- Guide to multi-class multi-label classification with neural networks in python — Depends on the definition
- Classifying genres of movies by looking at the poster — A neural approach — Depends on the definition
- classification — How to set class weights for imbalanced classes in Keras? — Data Science Stack Exchange
- Multi-Label Classification and Class Activation Map on Fashion-MNIST
- Deep dive into multi-label classification..! (With detailed Case Study)
- Multi-Label Image Classification with Neural Network | Keras
- Multi-Label Image Classification in TensorFlow 2.0 — Towards Data Science
- GitHub — ashrefm/multi-label-soft-f1: Train a multi-label image classifier with macro soft-F1 loss in TensorFlow 2.0
- scikit-multilearn: Multi-Label Classification in Python — Multi-Label Classification for Python
- scikit-multilearn: Multi-Label Classification in Python — Multi-Label Classification for Python
NLP
- 9 functions that make natural language pre-processing a piece of cake | by Alejandra Vlerick | Towards Data Science
- fastText
- Perplexity in Language Models. Evaluating language models using the… | by Chiara Campagnola | Towards Data Science
- How to evaluate Text Generation Models? Metrics for Automatic Evaluation of NLP Models | by Divish Dayal | Towards Data Science
- Step-by-Step Natural Language Processing Workshop: From Data to Deployment — YouTube
NLTK
- Tokenization and Parts of Speech(POS) Tagging in Python’s NLTK library
- Remove Stop Words
- Text Preprocessing in Python: Steps, Tools, and Examples
- (Tutorial) Text ANALYTICS for Beginners using NLTK — DataCamp
- Deep Learning Illustrated: Building Natural Language Processing Models — Data Science Blog by Domino
NumPy
OCR
- HarilalOP/OCR-Deep-Learning: OCR Deep Learning
- keras/image_ocr.py at master · keras-team/keras
- Deep Learning OCR using TensorFlow and Python — nicholastsmith
- Build a Handwritten Text Recognition System using TensorFlow
- python — How can I use the Keras OCR example? — Stack Overflow
- 🔥 Latest Deep Learning OCR with Keras and Supervisely in 15 minutes
- image_ocr.ipynb — Colaboratory
- A gentle introduction to OCR — Towards Data Science
- LPRNet: License Plate Recognition via Deep Neural Networks
- models/research/attention_ocr at master · tensorflow/models
- FAQ: Build a Handwritten Text Recognition System using TensorFlow
- 500 Lines or Less | Optical Character Recognition (OCR)
- Scanned Digits Recognition using k-Nearest Neighbor (k-NN)
- OpenCV Text Detection (EAST text detector) — PyImageSearch
- Text Recognition Data Set
OpenCV
- Getting Started with Videos — OpenCV 3.0.0-dev documentation
- Deep learning: How OpenCV’s blobFromImage works — PyImageSearch
Pandas
- Working with missing data — pandas 0.22.0 documentation
- Pandas in Jupyter — Quickstart and Useful Snippets — Nikolay Grozev
- Visualization — pandas 0.22.0 documentation
- Resampling time series data with pandas — Ben Alex Keen
- Summarising, Aggregating, and Grouping data in Python Pandas | Shane Lynn
Preprocessing
Privacy
Production
- Deploying Keras models using TensorFlow Serving and Flask
- Train and serve a TensorFlow model with TensorFlow Serving | TensorFlow Serving | TensorFlow
- A guide to deploying Machine/Deep Learning model(s) in Production
- Training and Serving ML models with tf.keras — TensorFlow — Medium
- Deploy a Keras Model for Text Classification using TensorFlow Serving (Part 1 of 2)
- Tensorflow serving example. Part 1: Data Labeling for the Trading Strategy — YouTube
- Deploy Machine Learning Model Using TensorFlow 2.0 Serving | Full Tutorial — YouTube
- Deploy Machine Learning Model Using TensorFlow 2.0 Serving | Full Tutorial — YouTube
- Train and serve a TensorFlow model with TensorFlow Serving | TFX
- rest_simple.ipynb — Colaboratory
- TensorFlow Serving with Docker | TFX
- Introducing TFServe: Simple and easy HTTP server for tensorflow model inference
- Using TensorFlow Serving’s RESTful API | Towards Data Science
- How to deploy TensorFlow models to production using TF Serving
- Data project checklist · fast.ai
Python
- First Steps With Python — Real Python
- Python Tensorflow Programming Tutorials
- Introduction · A Byte of Python
- cs228-material/cs228-python-tutorial.ipynb at master · kuleshov/cs228-material · GitHub
- Train/Test Split and Cross Validation in Python — Towards Data Science
- How to start Deep Learning With Python
- Use Keras Deep Learning Models with Scikit-Learn in Python
- Python Programming Tutorials
- Practice Deep Learning in Python
- Python Enumerate Explained (With Examples) — Afternerd
- Python Numpy Tutorial (with Jupyter and Colab)
- How to Get Started With Python?
Question Answer
- Master defense 24.01.2020 -Serhii Tiutiunnyk- Context-based Question-answering System — YouTube
- Applying BERT to Question Answering (SQuAD v1.1) — YouTube
Question Generation
Recurrent Neural Networks
- LSTM in Python: Stock Market Predictions (article) — DataCamp
- Understanding LSTM Networks — colah’s blog
- The Unreasonable Effectiveness of Recurrent Neural Networks
- Predict Stock Prices Using RNN: Part 1
- Time Series Forecasting with the Long Short-Term Memory Network in Python
- Multivariate Time Series Forecasting with LSTMs in Keras
- Multi-step Time Series Forecasting with Long Short-Term Memory Networks in Python
- Python Time Series Analysis Tutorial (article) — DataCamp
- NYC Open Data | Kaggle
- pandas Time Series Basics
- How to predict Bitcoin and Ethereum price with RNN-LSTM in Keras
- Mohamed Abdulaziz
- Mohamed Abdulaziz
- How to Make Predictions with Long Short-Term Memory Models in Keras
- The 5 Step Life-Cycle for Long Short-Term Memory Models in Keras
- Using LSTMs to forecast time-series — Towards Data Science
- LSTM Neural Network for Time Series Prediction | Jakob Aungiers
- LSTM Tutorial
- How to use Different Batch Sizes when Training and Predicting with LSTMs
- (3) RNN W1L11 : Bidirectional RNN — YouTube
- (3) Recurrent Neural Networks | Sequence Models (Course 5 Deep Learning Andrew Ng) — YouTube
- A Visual Guide to Recurrent Layers in Keras
- A friendly introduction to Recurrent Neural Networks — YouTube
Regression
- Regression Models with multiple target variables — Towards Data Science
- Support Vector Machines(SVM) — An Overview — Towards Data Science
- classification — How does a Support Vector Machine (SVM) work? — Cross Validated
Reinforcement Learning
- Google Research Football League
- Reinforcement Q-Learning from Scratch in Python with OpenAI Gym — LearnDataSci
- Gym
- OpenAI Gym from scratch — Towards Data Science
- dan lee — Medium
- Google AI Blog: Mastering Atari with Discrete World Models
Self-Supervised
seq2seq
- Word Level English to Marathi Neural Machine Translation using Encoder-Decoder Model
- tensorflow/nmt: TensorFlow Neural Machine Translation Tutorial
- How to Develop a Seq2Seq Model for Neural Machine Translation in Keras
- How to Develop an Encoder-Decoder Model for Sequence-to-Sequence Prediction in Keras
- [1409.3215] Sequence to Sequence Learning with Neural Networks
- Encoder-Decoder Recurrent Neural Network Models for Neural Machine Translation
- Introduction to Neural Machine Translation with GPUs (part 1) | NVIDIA Developer Blog
- Introduction to Neural Machine Translation with GPUs (Part 2) | NVIDIA Developer Blog
- Introduction to Neural Machine Translation with GPUs (part 3) | NVIDIA Developer Blog
- Text Summarization from scratch using Encoder-Decoder network with Attention in Keras | by Varun Saravanan | Towards Data Science
- Keras implementation of an encoder decoder for time series prediction
- GitHub — guillaume-chevalier/seq2seq-signal-prediction: Signal forecasting with a Sequence-to-Sequence (seq2seq) Recurrent Neural Network (RNN) model in TensorFlow — Guillaume Chevalier
- You searched for Encoder decoder — Machine Learning Mastery
- Google AI Blog: Introducing tf-seq2seq: An Open Source Sequence-to-Sequence Framework in TensorFlow
- Character-level recurrent sequence-to-sequence model
- keras/lstm_seq2seq.py at master · keras-team/keras · GitHub
- Sequence to sequence learning for performing number addition
- Seq2Seq Model | Sequence To Sequence With Attention
- lstm_seq2seq — Colaboratory
- lstm_seq2seq — Colaboratory
Setup
- How to Install Tensorflow-GPU version with Jupyter (Windows 10) in 8 easy steps.
- Access your Jupyter Notebook running on Windows 10 from any computer
- Running a notebook server — Jupyter Notebook 6.0.0.dev0 documentation
- JupyterHub — JupyterHub 1.0.0.dev documentation
- Compatible Video Card for Windows Server 2012
- Windows Server Catalog
- Build and Setup Your Own Deep Learning Server From Scratch
- Keras as a simplified interface to TensorFlow: tutorial
- Question — Quora
- Jupyter Notebook Extensions — Towards Data Science
- Building your own Deep Learning dream machine — Yanda’s Blog — Medium
- Picking a GPU for Deep Learning — Slav
- The $1700 great Deep Learning box: Assembly, setup and benchmarks
- Which GPU(s) to Get for Deep Learning
SOM
- Self Organizing Maps — Towards Data Science
- Introduction to Self-Organizing Maps (SOMs) — Heartbeat
- Self-Organizing Maps Tutorial — Algobeans
Speech
Start
- What is Deep Learning? — Machine Learning Mastery
- A Beginner’s Guide to Neural Networks and Deep Learning | Skymind
- Deep Learning eBook
- Why do we need machine learning [13 min] (1) — YouTube
- Neural networks and deep learning
- Python Online Interactive Open Course
- A Complete Guide on Getting Started with Deep Learning in Python
- Learn R, Python & Data Science Online | DataCamp
- CSE352 ARTIFICIAL INTELLIGENCE
- A Neural Network Playground
- A Beginner’s Guide To Understanding Convolutional Neural Networks — Adit Deshpande — CS Undergrad at UCLA (‘19)
- Multiclass SVM optimization demo
- Deep Learning
- CMSC 35246 Deep Learning — University of Chicago
- courses:deeplearning2015:start | CILVR Lab @ NYU
- Deep Learning in Computer Vision — Winter 2016
- MNIST For ML Beginners | TensorFlow
- Neural Networks Tutorial — A Pathway to Deep Learning — Adventures in Machine Learning
- Dive into Deep Learning — Dive into Deep Learning 0.14.3 documentation
- Deep Learning
- start [Machine Learning Engineering book by Andriy Burkov]
- GitHub — jermwatt/machine_learning_refined: Notes, examples, and Python demos for the textbook “Machine Learning Refined” (published by Cambridge University Press).
- (Tutorial) Probability Distributions in Python — DataCamp
- Intro to Deep Learning (ML Intensive at X) — YouTube
- Linear Algebra — Math for Machine Learning — YouTube
- Scikit-learn Crash Course — Machine Learning Library for Python — YouTube
SVN
- The Scuffle Between Two Algorithms -Neural Network vs. Support Vector Machine
- Transfer learning from pre-trained models — Towards Data Science
- Implementing SVM and Kernel SVM with Python’s Scikit-Learn
T2T
- Google AI Blog: Accelerating Deep Learning Research with the Tensor2Tensor Library
- GitHub — tensorflow/tensor2tensor: Library of deep learning models and datasets designed to make deep learning more accessible and accelerate ML research.
- Training Cutting-Edge Neural Networks with Tensor2Tensor and 10 lines of code
- Tensor2Tensor and One Model to Learn them all — Towards Data Science
Text Classification
- Multi-Class Text Classification with Scikit-Learn | by Susan Li | Towards Data Science
- Text classification from scratch
- Basic text classification | TensorFlow Core
- Introduction | Text classification guide | Google Developers
Text Generation
- Novel Methods For Text Generation Using Adversarial Learning & Autoencoders | TOPBOTS
- GitHub — CR-Gjx/LeakGAN: The codes of paper “Long Text Generation via Adversarial Training with Leaked Information” on AAAI 2018. Text generation using GAN and Hierarchical Reinforcement Learning.
- Neural text generation: How to generate text using conditional language models | by Neil Yager | Phrasee | Medium
- Unsupervised Sentiment Neuron
- Plug and Play Language Models: A Simple Approach to Controlled Text Generation | AISC — YouTube
- How to generate text: using different decoding methods for language generation with Transformers
- machine learning — What is the difference between word-based and char-based text generation RNNs? — Data Science Stack Exchange
- Text Generation | Papers With Code
- Controllable Neural Text Generation
- How to sample from language models | by Ben Mann | Towards Data Science
- COLING 2020: Exploring Controllable Text Generation Techniques — Shrimai Prabhumoye — YouTube
- COLING 2020: Exploring Controllable Text Generation Techniques — Shrimai Prabhumoye — YouTube
- A Distributional Approach to Controlled Text Generation | NLP Journal Club — YouTube
- TextXD2018 — See — Controlling text generation for a better chatbot — YouTube
- CoCon: A Self-Supervised Approach for Controlled Text Generation — YouTube
- Recent Advances in Language Model Fine-tuning
- A guide to language model sampling in AllenNLP | by Jackson Stokes | AI2 Blog | Medium
- How to Implement a Beam Search Decoder for Natural Language Processing
- Text generation with a miniature GPT
- Generate Blog Posts with GPT2 & Hugging Face Transformers | AI Text Generation GPT2-Large — YouTube
- jsvine/markovify: A simple, extensible Markov chain generator.
- A neural probabilistic language model | The Journal of Machine Learning Research
- Conditional Text Generation by Fine Tuning GPT-2 | by Ivan Lai | Jan, 2021 | Towards Data Science
- [1810.04805] BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
- DialoGPT — Microsoft Research
- Google’s Next AI Move: Teaching Foreign Languages — The Information
- Falsehoods more likely with large language models | VentureBeat
- Google AI Blog: Introducing FLAN: More generalizable Language Models with Instruction Fine-Tuning
Text Summarization
- [1801.10198] Generating Wikipedia by Summarizing Long Sequences
- Generate Summaries using Google’s Pegasus library | by Chetan Ambi | Medium
- Text Summarization made easy(2) , Text Representation | by amr zaki | HackerNoon.com | Medium
Text to Image
- GitHub — llSourcell/how_to_convert_text_to_images: This is the code for “How to Convert Text to Images — Intro to Deep Learning #16' by Siraj Raval on YouTube
- (16) How to Convert Text to Images — Intro to Deep Learning #16 — YouTube
- [1903.05854] MirrorGAN: Learning Text-to-image Generation by Redescription
- Text-to-Image Synthesis — Data Driven Investor — Medium
Text Topic Keyword Extraction
- Beginners Guide to Topic Modeling in Python and Feature Selection
- NLP: Extracting the main topics from your dataset using LDA in minutes | by Priya Dwivedi | Towards Data Science
- Keyword Extraction Techniques using Python | by Ajay Alex | Analytics Vidhya | Medium
- 10 Popular Keyword Extraction Algorithms in Natural Language Processing | by Prakhar Mishra | MLearning.ai | Medium
TF Keras
- TensorFlow
- Simple end-to-end TensorFlow examples — Bcomposes
- TensorFlow
- Installing TensorFlow on Windows | TensorFlow
- TensorFlow Tutorials
- A Guide to TF Layers: Building a Convolutional Neural Network | TensorFlow
- TensorFlow or Keras? Which one should I learn? — Imploding Gradients — Medium
- Learning TensorFlow :: Arrays and working with Images
- GitHub — aymericdamien/TensorFlow-Examples: TensorFlow Tutorial and Examples for Beginners with Latest APIs
- Feeding your own data set into the CNN model in TensorFlow — Knowledge Transfer
- Step by Step, A Tutorial on How to Feed Your Own Image Data to Tensorflow | yeephycho
- Keras Documentation
- Evaluate the Performance Of Deep Learning Models in Keras
- Deep learning using Keras — The Basics | Learn OpenCV
- Keras Tutorial : Using pre-trained ImageNet models | Learn OpenCV
- How to Use the Keras Functional API for Deep Learning
- FAQ — Keras Documentation
- python — Keras, How to get the output of each layer? — Stack Overflow
- Visualize CNN with keras | Kaggle
- Understanding Keras tensors — Emmanuel Caradec — Medium
- Guide to the Functional API — Keras Documentation
- Dissecting Keras neural networks: accessing weights and hidden layers — ITN Spotlight
- Dissecting Keras neural networks: accessing weights and hidden layers — ITN Spotlight
- Advanced Keras — Accurately Resuming a Training Process
- A detailed example of data generators with Keras
- Keras data generators and how to use them — Towards Data Science
- *** Creating custom data generator for training Deep Learning Models-Part 1
- *** Creating custom data generator for training Deep Learning Models-Part 2
- 4 Awesome things you can do with Keras and the code you need to make it happen
- Advanced Keras — Constructing Complex Custom Losses and Metrics
- Keras vs. tf.keras: What’s the difference in TensorFlow 2.0? — PyImageSearch
- tf.keras for Researchers: Crash Course.ipynb — Colaboratory
- Ten Important Updates from TensorFlow 2.0 (article) — DataCamp
- TensorFlow Core
- flowers_tf_lite.ipynb — Colaboratory
- Automatically upgrade code to TensorFlow 2 | TensorFlow Core
- Introduction to 1D Convolutional Neural Networks in Keras for Time Sequences | by Nils Ackermann | Good Audience
tf.data
- [TensorFlow 2.0] Load Images to tensorflow | by A Ydobon | Medium
- Building a data pipeline
- Load text | TensorFlow Core
- python — How to use windows created by the Dataset.window() method in TensorFlow 2.0? — Stack Overflow
Time Series
- A Gentle Introduction to Autocorrelation and Partial Autocorrelation
- Time Series Data Visualization with Python
- Complete guide to create a Time Series Forecast (with Codes in Python)
- How To Resample and Interpolate Your Time Series Data With Python
- How to Create an ARIMA Model for Time Series Forecasting in Python
- How to do step by step time series ARIMA analysis in Python — Quora
- Time Series Analysis in Python | Time Series Forecasting | Data Science with Python | Edureka — YouTube
- etcrago/Tutorial-Arima-w-jeffrey-yau
- (9) Jeffrey Yau | Applied Time Series Econometrics in Python and R — YouTube
- (9) Autocorrelation — YouTube
- (9) Autoregressive model for forecast errors — YouTube
- (9) Business Analytics Using Forecasting (Intro) — YouTube
- Methods to improve Time series forecast (including ARIMA, Holt’s winter)
- Using AIC to Test ARIMA Models | CoolStatsBlog
- Lesson 1: Time Series Basics | STAT 510
- Basic Time Series Manipulation with Pandas — Towards Data Science
- Handling Missing Values In Time Series
- The tale of missing values in Python — Towards Data Science
- Understand Time Series Forecast Uncertainty Using Confidence Intervals with Python
- CO2 Emission Forecast with Python (Seasonal ARIMA) | Kaggle
- Autocorrelation
- Hourly Time Series forecasting with XGBoost | Kaggle
- Using XGBoost in Python (article) — DataCamp
- Open Machine Learning Course. Topic 9. Part 1. Time series analysis in Python
- YouTube
- YouTube
- Playing with time series data in python — Towards Data Science
- Forecasting Time-Series data with Prophet — Part 1 — Python Data
- Time Series Forecasts using Facebook’s Prophet (with Python & R codes)
- A Guide to Time Series Forecasting with Prophet in Python 3 | DigitalOcean
- [Tutorial] Time Series forecasting with Prophet | Kaggle
- sklearn.model_selection.TimeSeriesSplit — scikit-learn 0.20.0 documentation
- Prophet | Prophet is a forecasting procedure implemented in R and Python. It is fast and provides completely automated forecasts that can be tuned by hand by data scientists and analysts.
- Data Science — Part X — Time Series Forecasting — YouTube
- Time Series Analysis — Artificial Neural Networks | Kaggle
- Share GPU memory for different users in keras and tensorflow — Stack Overflow
- Applying Deep Learning to Time Series Forecasting with TensorFlow | MapR
- Are we producing enough FOOD?? Simple Time Series | Kaggle
- Time Series Forecast : A basic introduction using Python.
Transfer Learning
- Transfer Learning in Keras with Computer Vision Models
- ImageNet: VGGNet, ResNet, Inception, and Xception with Keras — PyImageSearch
- Multi-Label Classification of Satellite Photos of the Amazon Rainforest
Transformer
- Google AI Blog: Transformer: A Novel Neural Network Architecture for Language Understanding
- The Illustrated Transformer — Jay Alammar — Visualizing machine learning one concept at a time.
- A Transformer Chatbot Tutorial with TensorFlow 2.0 — TensorFlow — Medium
- LSTM is dead. Long Live Transformers! — YouTube
- The Annotated Transformer
- Tensor2Tensor Intro — Colaboratory
- google/trax: Trax — Deep Learning with Clear Code and Speed
- Illustrated Guide to Transformers Neural Network: A step by step explanation — YouTube
- Illustrated Guide to Transformers Neural Network: A step by step explanation — YouTube
- MultiHeadAttention layer
- tf.keras.layers.MultiHeadAttention | TensorFlow Core v2.4.0
- Transformer model for language understanding | TensorFlow Core
- keras-pos-embd · PyPI
- Hugging Face: State-of-the-Art Natural Language Processing in ten lines of TensorFlow 2.0 | by Lysandre Debut | TensorFlow | Medium
- Visual Guide to Transformer Neural Networks — (Part 1) Position Embeddings — YouTube
- 🤗 Transformers Notebooks — transformers 4.4.1 documentation
- The Illustrated GPT-2 (Visualizing Transformer Language Models) — Jay Alammar — Visualizing machine learning one concept at a time.
- The Illustrated Transformer — Jay Alammar — Visualizing machine learning one concept at a time.
- Hugging Face — The AI community building the future.
- openai/gpt-2: Code for the paper “Language Models are Unsupervised Multitask Learners”
- Jay Alammar — Visualizing machine learning one concept at a time.
- Rasa Algorithm Whiteboard — Transformers & Attention 1: Self Attention — YouTube
- A Practical Survey on Faster and Lighter Transformers
- How GPT3 Works — Visualizations and Animations — Jay Alammar — Visualizing machine learning one concept at a time.
- The Illustrated GPT-2 (Visualizing Transformer Language Models) — Jay Alammar — Visualizing machine learning one concept at a time.
- GPT-3’s free alternative GPT-Neo is something to be excited about | VentureBeat
- Text classification with Transformer
- [2105.12723] Aggregating Nested Transformers
Trikcs
- Batch normalization in Neural Networks — Towards Data Science
- How to Accelerate Learning of Deep Neural Networks With Batch Normalization
- Alan Bertl | Pretraining a GAN using an autoencoder
- How to Reduce Overfitting With Dropout Regularization in Keras
- Multi-label classification with class weights in Keras — Stack Overflow
- Handling Imbalanced Datasets in Deep Learning — Towards Data Science
- Machine Learning — Multiclass Classification with Imbalanced Dataset
- Machine Learning — Multiclass Classification with Imbalanced Dataset
- Image Segmentation: tips and tricks from 39 Kaggle competitions | Neptune Blog
- Classification on imbalanced data | TensorFlow Core
- Hyperopt Documentation
- What my first Silver Medal taught me about Text Classification and Kaggle in general?
- Rules of Machine Learning: | ML Universal Guides | Google Developers
- Why machine learning algorithms are hard to tune (and the fix)
- Keras Tuner
Truba
- TRUBA-barbun — TRUBA Wiki Sayfası
- Portal Kullanımı — TRUBA Wiki Sayfası
- Kullanıcı El Kitabı — TRUBA Wiki Sayfası
- Anasayfa — Türk Ulusal Bilim e-Altyapısı
- Anasayfa — Türk Ulusal Bilim e-Altyapısı
- TRUBA Wiki Sayfası
- Yeni TRUBA Kullanıcı Dökümanları
Validation Testing
- K-fold Cross Validation with TensorFlow and Keras — MachineCurve
- K-Fold Cross Validation for Deep Learning Models using Keras | by Siladittya Manna | The Owl | Medium
- 3.1. Cross-validation: evaluating estimator performance — scikit-learn 0.24.2 documentation
Video Object Detection
Voice
- You can now speak using someone else’s voice with Deep Learning
- Listen to this AI voice clone of Bill Gates created by Facebook’s engineers — The Verge
- This AI-generated Joe Rogan fake has to be heard to be believed — The Verge
Word Embedding
- A Detailed Explanation of Keras Embedding Layer | Kaggle
- A Word2Vec Keras tutorial — Adventures in Machine Learning
- Building Recipe Skill Representations Using Skip-Thought Vectors
- How to Use Word Embedding Layers for Deep Learning with Keras
- Introduction to Word Embedding and Word2Vec — Towards Data Science
- Keras LSTM tutorial — How to easily build a powerful deep learning language model — Adventures in Machine Learning
- machine learning — Explain with example: how embedding layers in keras works — Stack Overflow
- My thoughts on Skip-Thoughts — Sanyam Agarwal — Medium
- Neural Network Embeddings Explained — Towards Data Science
- Sentiment detection with Keras, word embeddings and LSTM deep learning networks · Blog · Liip
- Understanding Word Embeddings — Hacker Noon
- Using a Keras Embedding Layer to Handle Text Data — Heartbeat
- Vector Representations of Words | TensorFlow | TensorFlow
- Word embeddings: exploration, explanation, and exploitation (with code in Python)
- Word2Vec word embedding tutorial in Python and TensorFlow — Adventures in Machine Learning
- Word embeddings | TensorFlow Core
- How to Use Word Embedding Layers for Deep Learning with Keras
XGBoost
Keep Deep Learning :)
Comments or Questions?
Please share your Comments or Questions.
Thank you in advance.
Do not forget to check out the next parts!
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