Time Series Forecasting with TensorFlow.js

In this article, you will learn how to pull stock prices from online API and perform predictions using Recurrent Neural Network & Long Short Term Memory (LSTM) with TensorFlow.js framework.

http://bit.ly/2QSb0PU

Initializing neural networks

Initialization can have a significant impact on convergence in training deep neural networks. Simple initialization schemes can accelerate training, but they require care to avoid common pitfalls. In this post, we’ll explain how to initialize neural network parameters effectively.

http://bit.ly/2WVBBkA

Create TensorFlow Name Scopes For TensorBoard

Use TensorFlow Name Scopes (tf.name_scope) to group graph nodes in the TensorBoard web service so that your graph visualization is legible.

http://bit.ly/2QWAvjc

ICLR 2019 posters

For those who weren’t at ICLR and want to browse the papers that were presented there, this is for you; it lets you check out many of the posters from the official ICLR poster session. In the future, founders plan to publish poster sessions from other top machine learning conferences too.

http://bit.ly/2R0m8KY

End-to-End Object Detection for Furniture Using Deep Learning

In this article, you will learn how to build an object detection algorithm using a CNN-based algorithm called “You Only Look Once” to identify, classify, and localize different types of furniture in images and videos.

http://bit.ly/2R4gPKn

16 OpenCV Functions to Start your Computer Vision journey (with Python code)

In this article, you will learn about OpenCV library and basic functions: Reading, Writing and Displaying Images, Changing Color Spaces, Resizing Images, Image Rotation, Image Translation, Simple Image Thresholding, Adaptive Thresholding, Image Segmentation (Watershed Algorithm), Bitwise Operations, Edge Detection, Image Filtering, Image Contours, Scale Invariant Feature Transform (SIFT), Speeded-Up Robust Features (SURF), Feature Matching, Face Detection.

http://bit.ly/2R8Dsx3

JavaScript for Machine Learning using TensorFlow.js

In this article, you will find how to build a simple classifier step by step, in a beginner-friendly process.

http://bit.ly/2RbLBRr

A Hands-On Introduction to Deep Q-Learning using OpenAI Gym in Python

This article will help you to take your first steps into the world of deep reinforcement learning. We’ll use one of the most popular algorithms in RL, deep Q-learning, to understand how deep RL works. We will implement all learnt in an awesome case study using Python.

http://bit.ly/2ReUZEc

​​June 18 Moscow is going to host AWS Dev Day! Are you all fired up?

What Is AWS Dev Day Moscow?
AWS Dev Day Moscow is a free, full-day technical event launched by Amazon Web Services as part of their global series of AWS Dev Day events. At the event, IT professionals and developers can learn the latest trends in the cloud and strengthen their AWS skills.

Why Is AWS Dev Day Moscow Worth Visiting?
Ten strong tech luminaries who are ready to share their knowledge and expertise; two tracks diving deep into the cloud and AWS products; «Ask an AWS Architect» sessions where every attendee can speak with AWS Architects, and more!

Among other keynotes, it’s worth noting a few: «Machine learning with Amazon SageMaker», «Containers CI/CD Pipeline on AWS», «Service Mesh Magic», «Everything as a Code: Two Years with AWS ECS in Production», «Best Practices for Integrating Amazon Rekognition into Your Own Applications», and more.

When?
Thursday, June 18, 9 AM — 5 PM

Where?
«Vesna» space, 2c1 Spartakovsky L, 7 drive

The participation is free, yet you need to register using the link below:
http://bit.ly/2XLte8z

Learn more: http://bit.ly/2XLtep5

A practical guide to Deep Learning in 6 months

This post offers a detailed roadmap to how you can learn Deep Learning well enough to get a Deep Learning internship as well as a full-time job within 6 months. This post is practical, result oriented and follows a top-down approach. It is aimed for beginners strapped for time, as well as for intermediate practitioners.

http://bit.ly/2IhKwVE

Machine Learning and Data Science Applications in Industry

A curated list of applied machine learning and data science notebooks and libraries across different industries.

http://bit.ly/2XNmvLt

The Third Wave Data Scientist

A long-awaited update of the data science skill portfolio

http://bit.ly/2IkKEDF

Pythia for vision and language multimodal AI models

Pythia is a deep learning framework that supports multitasking in the vision and language domain. Built on our open-source PyTorch framework, the modular, plug-and-play design enables researchers to quickly build, reproduce, and benchmark AI models. Pythia is designed for vision and language tasks, such as answering questions related to visual data and automatically generating image captions.

http://bit.ly/2Y132ah

​​Data Science Digest readers can get over 20% off on any RE•WORK Summit using code DSDIGEST. Meet with Bengio, Goodfellow and more https://bit.ly/2JXpWfb

Generalized Additive Models in R

This short course will teach you how to use these flexible, powerful tools to model data and solve data science problems. GAMs offer a middle ground between simple linear models and complex machine-learning techniques, allowing you to model and understand complex systems.

http://bit.ly/2InqoRO

Deep Learning Models

A collection of various deep learning architectures, models, and tips for TensorFlow and PyTorch in Jupyter Notebooks. This collection has been up for about two weeks and has ~7,000 stars already!

http://bit.ly/2IrofEK

18 Impressive Applications of Generative Adversarial Networks

This post offers a review of a large number of interesting applications of GANs to help you develop an intuition for the types of problems where GANs can be used and useful.

http://bit.ly/31K35JI

Machine learning datasets

A list of the biggest datasets for machine learning from across the web. Computer vision, natural language processing, audio, and medical datasets.

http://bit.ly/2Fp9q3P

​​
Optimizing your R code – a guided example

Optimizing R code is not always the priority. But when you run out of memory, or it just takes too long, you start to wonder if there are better ways to do things! In this article, the author demonstrates the methods of optimizing R code and the process behind it.

http://bit.ly/2ZFvH4O

Distributed Deep Learning Pipelines with PySpark and Keras

An easy approach to data pipelining using PySpark and doing distributed deep learning with Keras.

http://bit.ly/2ZFao3s

Machine Learning for Everyone

The best general intro article about Machine Learning. Only real-world problems, practical solutions, simple language, and no high-level theorems. One and for everyone.

http://bit.ly/2ZGQBRd