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data-science-interview-questions-and-answers-general-

Data Science Questions and Answers (General) for beginner

data-science-learning

Repository of code and resources related to different data science and machine learning topics. For learning, practice and teaching purposes.

data-science-resources

Resources about data science and related topics in the field of data analytics (books, papers, websites)

data-structures-algorithms-coursera-ucsd-hse

Data Structures and Algorithms Coursera Specialization from UC San Diego and HSE

data-structures-algorithms-python

This tutorial playlist covers data structures and algorithms in python. Every tutorial has theory behind data structure or an algorithm, BIG O Complexity analysis and exercises that you can practice on.

data_engineering

data_science_fundamentals

data_structures_algorithms_in_python

My implementation of 80+ popular data structures and algorithms and interview questions in Python 3

datacamp_solutions_python

My solutions to DataCamp projects (now only Python)

dataengineering_gcp_course4_serverless-machine-learning-with-tensorflow

Explore and create ML datasets. Sample the dataset and create training, validation, and testing datasets for local development of TensorFlow models. Create a benchmark to evaluate the performance of ML. TensorFlow is used for numerical computations, using directed graphs. Getting started with TensorFlow. Explore the TensorFlow python API, build a graph, run a graph, feed values into a graph. Find areas of a triangle using TensorFlow. Learning from tf.estimator. Read from python’s pandas dataframe into tf.constant, create feature columns for estimator, perform linear regression with tf.Estimator framework. Execute Deep Neural Network regression. Use benchmark dataset. Refactoring to add batching and feature creation. Refactor the input. Refactor the way the features are created. Create and train the model, Evaluate the model. Distributed training and monitoring. Create features out of input data. Train and evaluate. Monitor with Tensorboard. To run TensorFlow at scale, use Cloud ML Engine. Package up the code. Find absolute paths to data. Run the python module from the command line. Run locally using GCloud. Submit training job using GCloud. Deploy model. Make predictions. Train on a 1-million row dataset. Feature Engineering. Working with feature columns. Adding feature crosses in TensorFlow. Reading data from BigQuery. Creating datasets using Dataflow. Using a wide-and-deep model.

datascience

Curated list of Python resources for data science.

datascience-exam

Repo supporting data science team interview exams.

dcgan-tensorflow

A tensorflow implementation of "Deep Convolutional Generative Adversarial Networks"

decanlp

The Natural Language Decathlon: A Multitask Challenge for NLP

deep-generation

I used in this project a reccurent neural network to generate c code based on a dataset of c files from the linux repository.

deep-learn-oil

Deep learning tools for predicting oil well data

deep-learning

deep-learning-1

Repo for the Deep Learning Nanodegree Foundations program.

deep-learning-21-examples

《21个项目玩转深度学习———基于TensorFlow的实践详解》配套代码

deep-learning-assignments-and-projects

Assignments done throughout the Deep Learning specialization ( a series of five interconnected courses covering a range of topics and applications in deep learning such as neural networks, CNN, RNN, Residual Networks , Inception network, YOLO, Attention model, NLP, word embeddings, GRU, LSTM, etc) taught by Andrew Ng

deep-learning-book

Repository for "Introduction to Artificial Neural Networks and Deep Learning: A Practical Guide with Applications in Python"

deep-learning-boot-camp

A community run, 5-day PyTorch Deep Learning Bootcamp

deep-learning-coursera

Deep Learning Specialization by Andrew Ng on Coursera.

deep-learning-drizzle

Drench yourself in Deep Learning, Reinforcement Learning, Machine Learning, Computer Vision, and NLP by learning from these exciting lectures!!

deep-learning-for-tracking-and-detection

Collection of papers, datasets, code and other resources for object tracking and detection using deep learning

deep-learning-in-production

In this repository, I will share some useful notes and references about deploying deep learning-based models in production.

deep-learning-interview-book

深度学习面试宝典（含数学、机器学习、深度学习、计算机视觉、自然语言处理和SLAM等方向）

deep-learning-notes

Experiments with Deep Learning

deep-learning-papers-reading-roadmap

Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech!

deep-learning-semantic-segmentation

A paper list of semantic segmentation using deep learning.