Time series basics

Time series — data, with points having timestamps. Some might think that #timeseries are mostly used in algorithmic trading, but they often used in malware detection, network data analysis or any other field, dealing with some flow of time-labeled data. These two resources provide deep and easy #introduction into #TS analysis.

Github: https://github.com/akshaykapoor347/Time-series-modeling-basics
Data Camp presentation: https://s3.amazonaws.com/assets.datacamp.com/production/course_5702/slides/chapter3.pdf

#beginner #novice #python #entrylevel

IBM Cognitive classes is one way to learn Data Science and Machine Learning, and it is absolutely FREE.

*DON'T SKIP THE LAB EXERCISE., IT IS HELPFUL*

This is the Learning Path

1)Introduction to Data Science
(https://lnkd.in/fF79bEj)

2)Data Science Tools
(https://lnkd.in/fYf2ZC8)

3)Data Science Methodology
(https://lnkd.in/fY6Kwqd)

4)Statistics 101
(https://lnkd.in/fpgJf7D)

5)Predictive Modeling Fundamentals I
(https://lnkd.in/f9_Y7UZ)

6)Python for Data Science
(https://lnkd.in/fy8E2wH)

7)Data Analysis with Python
(https://lnkd.in/fRQWByd)

😎Data Visualization with Python
(https://lnkd.in/fFu93ME)

9)Machine Learning with Python
(https://lnkd.in/f_7r534)

10)Deep Learning Fundamentals
(https://lnkd.in/fNvPvix)

11)Deep Learning with TensorFlow
(https://lnkd.in/ftfRtvQ)

#datavisualization #deeplearning #datascience #python #predictivemodeling #machinelearning

Another major release of #SpaCy

👽 ULMFit/BERT/ELMo-like language model pretraining
📐 Better matching & rule-based NER
🧮 Platform-independent matrix multiplication
🚀 2-3 times faster tokenization
⏳ Installation without local compilers

Link: https://explosion.ai/blog/spacy-v2-1

#NLP #python

​​A comprehensive beginner’s guide to create a Time Series Forecast (with Codes in Python)

A middle-level article on #TS forecasting in #Python.

Link: https://www.analyticsvidhya.com/blog/2016/02/time-series-forecasting-codes-python/

​​An End-to-End Project on Time Series Analysis and Forecasting with Python

Today’s second article about #TS forecasting, to cover basics and provide knowledge about how to approach TS data mining.

Link: https://towardsdatascience.com/an-end-to-end-project-on-time-series-analysis-and-forecasting-with-python-4835e6bf050b

#Python

TensorBoard is a tool for providing the measurements and visualizations needed during the machine learning workflow. It enables tracking experiment metrics like loss and accuracy, visualizing the model graph, projecting embeddings to a lower dimensional space, and much more.

#ts, #python, #tensorflow, #machine, #learning, #visualization

Ignite is a high-level library to help with training and evaluating neural networks in PyTorch flexibly and transparently.

Ignite is a library that provides three high-level features:

- Extremely simple engine and event system
- Out-of-the-box metrics to easily evaluate models
- Built-in handlers to compose training pipeline, save artifacts and log parameters and metrics

#python, #pytorch, #ml, #neural, #network

DGL is an easy-to-use, high performance and scalable Python package for deep learning on graphs. DGL is framework agnostic, meaning if a deep graph model is a component of an end-to-end application, the rest of the logics can be implemented in any major frameworks, such as PyTorch, Apache MXNet or TensorFlow.

#python, #scalable, #deeplearning, #dl, #graphs

Fairseq is a sequence modeling toolkit that allows researchers and developers to train custom models for translation, summarization, language modeling and other text generation tasks.

#python, #sequence, #modeling, #toolkit, #ai

Python examples of popular machine learning algorithms with interactive Jupyter demos and math being explained.

The purpose of this repository is not to implement machine learning algorithms by using 3rd party library one-liners but rather to practice implementing these algorithms from scratch and get better understanding of the mathematics behind each algorithm. That's why all algorithms implementations are called "homemade" and not intended to be used for production.

#python, #machine, #learning, #ml, #jupyter, #notebooks, #algorithms

Rasa is a open source machine learning framework to automate text- and voice-based conversations: NLU, dialogue management, connect to Slack, Facebook, and more - Create chatbots and voice assistants.

#python, #nlp, #ml, #bots, #botkit, #wit, #conversational, #ai, #chatbot, #framework

LabML Neural Networks.

This is a collection of simple PyTorch implementations of neural networks and related algorithms.

These implementations are documented with explanations, and the website renders these as side-by-side formatted notes.

#python, #ml, #dl, #neural, #networks

TensorTrade is an open source Python framework for building, training, evaluating, and deploying robust trading algorithms using reinforcement learning. The framework focuses on being highly composable and extensible, to allow the system to scale from simple trading strategies on a single CPU, to complex investment strategies run on a distribution of HPC machines.

#python, #reinforcement, #learning

Hub is fastest unstructured dataset management for TensorFlow/PyTorch. Stream data real-time & version-control it.

Software 2.0 needs Data 2.0, and Hub delivers it. Most of the time Data Scientists/ML researchers work on data management and preprocessing instead of training models. With Hub, we are fixing this. We store your (even petabyte-scale) datasets as single numpy-like array on the cloud, so you can seamlessly access and work with it from any machine. Hub makes any data type (images, text files, audio, or video) stored in cloud usable as fast as if it were stored on premise. With same dataset view, your team can always be in sync.

#python, #tensorflow, #pytorch, #training, #data, #science, #ml, #ai, #dl, #distributed, #dataset, #mlops

PySyft is a Python library for secure and private Deep Learning. PySyft decouples private data from model training, using Federated Learning, Differential Privacy, and Encrypted Computation (like Multi-Party Computation (MPC) and Homomorphic Encryption (HE) within the main Deep Learning frameworks like PyTorch and TensorFlow.

#python #cryptography #privacy #deeplearning #pytorch #secure #computation

Extract Musical Notes from Audio in Python with FFT - YouTube
https://www.youtube.com/watch?v=rj9NOiFLxWA