Data Science Digest (July 2019)
Summer is in full swing, and if you plan to be in Odessa on July 5th, I invite you to the ODS mitap and data bar , which is organized by the Odessa ODS.ai team. I remind you that the digest has its own Telegram channel and pages on social networks ( Facebook , Twitter , LinkedIn , Medium ), where I publish links to useful materials daily. Join now!
In the meantime, I offer a fresh selection of materials under the cut.
- 18 Impressive Applications of Generative Adversarial Networks - An overview of
18interesting GAN applications to help you understand where it can be used and useful.
- Hardware acceleration of deep neural networks: GPU, FPGA, ASIC, TPU, VPU, IPU, DPU, NPU, RPU, NNP and other letters.
- Time Series Forecasting with TensorFlow.js - in this article you will learn how to extract stock prices from the online API and perform forecasts using a recurrent neural network and long-term short-term memory (LSTM) and TensorFlow.js.
- Initializing neural networks - the article explains how to effectively initialize the parameters of a neural network in order to accelerate its training and avoid common mistakes.
- Deep learning: the final frontier for signal processing and time series analysis?
- The Third Wave Data Scientist - what a modern data scientist should know and be able to do.
- 16 OpenCV Functions to Start your Computer Vision journey (with Python code) is an excellent article for beginners, which describes the basic functions of the OpenCV library and allows you to quickly start working with it.
- Whether the machine learning bubble burst, or the start of a new dawn .
- The Best and Most Current of Modern Natural Language Processing is a good review article that provides links to useful resources on the topic of natural language processing and helps you learn about the latest trends in this area.
- Distributed Deep Learning Pipelines with PySpark and Keras
- Text Preprocessing in Python: Steps, Tools, and Examples - in this article you will learn about the main stages of text preprocessing, which are necessary for translating text from human language into a machine-readable format for further work with it.
- Rekko Challenge - how to take 2nd place in the competition for the creation of recommendation systems .
- Introducing TensorFlow Graphics - An overview of the new TensorFlow add-on, which is expected to allow research at the intersection of deep learning and computer graphics.
- Automatic task assignment in Jira using ML .
- A Hands-On Introduction to Deep Q-Learning using OpenAI Gym in Python - this article will help you take the first steps into the world of deep learning with reinforcement using the OpenAI Gym example.
- Data Science Cheatsheets is an excellent collection of cheat sheets on the following topics: Artificial Intelligence, Big Data Analytics, Big Data, Data Engineering, Data Mining, Data Science, Data Visualization, Deep Learning, Machine Learning, Python and others.
- Artificial Intelligence cheatsheets for Stanford's CS 221 - This repository summarizes all the important things described in the Stanford CS 221 Artificial Intelligence Course and includes cheat sheets for it.
- ICLR 2019 posters is a project that is dedicated to collecting posters from leading machine learning conferences in one place. First posters with ICLR added.
- Machine Learning and Data Science Applications in Industry is a repository that contains a large list of examples of the use of machine learning and data science in various industries.
- Deep Learning Boot Camp - video presentations from the Deep Learning Boot Camp, which took place from May 28 to May 31 in Berkeley.
- AUTOML: METHODS, SYSTEMS, CHALLENGES - this book presents the first comprehensive overview of the general methods of automatic machine learning (AutoML), describes existing systems based on these methods, and discusses the problems of AutoML systems.
- ODS.ai Odessa Meetup & Data Bar - July 5, Odessa is the first meeting of the Open Data Science community in Odessa. Informal communication and interesting topics on the eve of the EECVC conference . Participation is free, registration is required.
- AI Ukraine 2019 -
September 21-22,Kiev - one of the most powerful AI conferences in Ukraine this year will be held in 3 streams: Data Science and Machine Learning; Big Data and Analytics; AI Business and Startups. The first topics of reports are already on the site. For readers of the digest 7% discount promotional code: DSDigest-AI2019.
Thank you for reading this issue. I hope everyone found something useful for themselves. I would be grateful for any suggestions for the next digest.
← Previous release: Data Science Digest (May 2019)