All Projects → DSCNEDUET-X-DSCUIT → Fast Track To Data Science 30 Days

DSCNEDUET-X-DSCUIT / Fast Track To Data Science 30 Days

Projects that are alternatives of or similar to Fast Track To Data Science 30 Days

Multivariate Time Series Models In Keras
This repository contains a throughout explanation on how to create different deep learning models in Keras for multivariate (tabular) time series prediction.
Stars: ✭ 94 (-1.05%)
Mutual labels:  jupyter-notebook
Python option pricing
An libary to price financial options written in Python. Includes: Black Scholes, Black 76, Implied Volatility, American, European, Asian, Spread Options
Stars: ✭ 94 (-1.05%)
Mutual labels:  jupyter-notebook
Lstm Odyssey
Implementations of "LSTM: A Search Space Odyssey" variants and their training results on the PTB dataset.
Stars: ✭ 94 (-1.05%)
Mutual labels:  jupyter-notebook
Transformer image caption
Image Captioning based on Bottom-Up and Top-Down Attention model
Stars: ✭ 94 (-1.05%)
Mutual labels:  jupyter-notebook
Build Knowledge Base With Domain Specific Documents
Create a knowledge base using domain specific documents and the mammoth python library
Stars: ✭ 94 (-1.05%)
Mutual labels:  jupyter-notebook
Deep Learning Python
Intro to Deep Learning, including recurrent, convolution, and feed forward neural networks.
Stars: ✭ 94 (-1.05%)
Mutual labels:  jupyter-notebook
Nf Jax
Normalizing Flows in Jax
Stars: ✭ 94 (-1.05%)
Mutual labels:  jupyter-notebook
Kaggle Competitions
All Kaggle competitions
Stars: ✭ 94 (-1.05%)
Mutual labels:  jupyter-notebook
Vae Text Generation
Text Generation Using A Variational Autoencoder
Stars: ✭ 94 (-1.05%)
Mutual labels:  jupyter-notebook
Vissl
VISSL is FAIR's library of extensible, modular and scalable components for SOTA Self-Supervised Learning with images.
Stars: ✭ 1,303 (+1271.58%)
Mutual labels:  jupyter-notebook
Tensorflow Eager Execution
使用 tensorflow eager execution 的机器学习全新教程
Stars: ✭ 94 (-1.05%)
Mutual labels:  jupyter-notebook
Deep Learning With Pytorch Quick Start Guide
Deep Learning with PyTorch Quick Start Guide, published by Packt
Stars: ✭ 94 (-1.05%)
Mutual labels:  jupyter-notebook
Quantandfinancial
This repository contains supporting examples which are referenced from posts published on www.quantandfinancial.com
Stars: ✭ 94 (-1.05%)
Mutual labels:  jupyter-notebook
Automation Repo
Machine learning and process automation
Stars: ✭ 94 (-1.05%)
Mutual labels:  jupyter-notebook
Stingray
Anything can happen in the next half hour (including spectral timing made easy)!
Stars: ✭ 94 (-1.05%)
Mutual labels:  jupyter-notebook
Rgcn With Bert
Graph Convolutional Networks (GCN) with BERT for Coreference Resolution Task [Pytorch][DGL]
Stars: ✭ 94 (-1.05%)
Mutual labels:  jupyter-notebook
Wximage
Stars: ✭ 94 (-1.05%)
Mutual labels:  jupyter-notebook
Python
Python 3
Stars: ✭ 94 (-1.05%)
Mutual labels:  jupyter-notebook
Notebooks
Examples and IPython Notebooks about NetworkX
Stars: ✭ 93 (-2.11%)
Mutual labels:  jupyter-notebook
Neon course
neon tutorials
Stars: ✭ 94 (-1.05%)
Mutual labels:  jupyter-notebook

DSC 30 day workshop

Go to our Youtube channel to watch the daily lectures.

Join us on Discord.

Join DSC-NEDUET

Course Outline

Course Outline

All Slides are accessible in below link

Go to: Slides.

Python Data Science Handbook

Handbook

Installation

Learn about python installation from here. Handbook

Books

Books

1: 100 pages Ml book by Andriy Burkov

2: Aurélien Géron - Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow_ Concepts, Tools, and Techniques to Build Intelligent Systems-O’Reilly Media (2019)

3: Andreas C. Müller, Sarah Guido - Introduction to Machine Learning with Python_ A Guide for Data Scientists-O’Reilly Media (2016)

4: Data Science For Business by Foster

5: Hands-on Deep Learning algorithms Sudharsan Ravichandiran (packt)

6: Deep Learning by Ian GoodFellow

7: Data Science from Scratch First Principles with Python 2nd Ed by Joel Grus (O'reilly)

8: Machine Learning_ A Probabilistic Perspective [Murphy] MIT

9: Pattern Recognition in ML by Bishop

10: Elements of Statistical Learning by Hastie and other

11: Introduction to Statistical Learning by Hastie and other

12: Practical Statistics for Data Scientists_ 50+ Essential Concepts Using R and Python-O'Reilly Media (2020) BYPeter Bruce, Andrew Bruce, Peter Gedeck

All Notes

All viewable notes are available here

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].