Ujjawal

Trading Stock Options || IIT Roorkee

Not SEBI Registered

[Ujjawal @algo_ticks]

Minor scalping on the same lines

[Ujjawal @algo_ticks]

The daily candle chart of ICICIGI looks interesting. 12 days of red candles straight with one Doji candle in between, RSI value on a decade low. Can go long with this!!? (obv w reasonable SL)

PS - not SEBI registered!

[Ujjawal @algo_ticks]

NIFTY daily candle RSI-14 is at 22, implying NIFTY is at its second most oversold situation post COVID.

[Ujjawal @algo_ticks]

@AshishGupta325 CAS to your rescue :P

[Ujjawal @algo_ticks]

*multi year low

[Thomas Kipf @tkipf]

Zero-shot Recognition via Semantic Embeddings and Knowledge Graphs (CVPR2018): Graph Convolutional Networks (GCNs) for zero-shot learning - massive improvements on a challenging ImageNet task https://t.co/UPtxONNGNC

[Miles Brundage @Miles_Brundage]

"An efficient framework for learning sentence representations," Logeswaran and Lee: https://t.co/DC1NIoGXLG

[Tomasz Malisiewicz @quantombone]

IONet: Learning to Cure the Curse of Drift in Inertial Odometry https://t.co/baqyDyhNkM #robotics #aaai #slam #DeepLearning

[hardmaru @hardmaru]

DeepMind releases "dm_control" a suite of continuous control reinforcement learning environments, based on the proprietary MuJoCo framework.
github: https://t.co/CpXKH1mgqi
paper: https://t.co/2vRaYKj1nR

[ipfconline @ipfconline1]

[#NLP #AI #DeepLearning]
How #Chatbots Work?
Let’s Look At The Inner Workings Of An Artificial Neural Network For Text Classification Which Is A Fundamental Piece of Machinery Inside a Chatbot

https://t.co/HyKI6V3jRY v/ @usejournal
Cc @MikeQuindazzi @evankirstel @jblefevre60

[Thomas Kipf @tkipf]

We just released a TensorFlow implementation of our Graph Convolutional Matrix Completion paper: https://t.co/PM8uWMwggP (with @vdbergrianne) - A recommender system based on graph neural networks

[hardmaru @hardmaru]

Neural Speed Reading via Skim-RNN. Inspired by speed-reading, Skim-RNN can dynamically decide to use a big RNN (read) or a small RNN (skim) at each time step, depending on importance of the input. Skim-RNN uses significantly lower computation vs normal RNN https://t.co/2teMGYvtKt

[Ilya Sutskever @ilyasut]

The ML wish of 2018: may all your local minima be global, your variance bounded, your labelled data plentiful, and your compute massive!

[Denny Britz @dennybritz]

I wrote up a (not so brief) summary of AI developments that stood out to me in 2017 https://t.co/3oLEOlC1GE

[Richard Socher @RichardSocher]

A reinforcement learning agent that learns to program new neural network architectures.
Same/better results as LSTMs but with funky nonlinearities (sine, SeLus, etc) and new connections that result in different activation patterns😯
https://t.co/WPLEJgT0pO
https://t.co/UEqgpBuG14

[Hugo Larochelle @hugo_larochelle]

Consider applying! I'll be participating, as well as many other great speakers. Note also that MLSS has some Fellowships available for students. https://t.co/sUNcEFeu8Y

[Richard Socher @RichardSocher]

Hierarchical and interpretable multi-task reinforcement learning.
This tackles some major limitations in RL: Training agents to solve complex problems that require multiple subtasks and have them explain themselves
Blog: https://t.co/CVshPlzgZx
Paper: https://t.co/usHK75XSsw

[Google DeepMind @GoogleDeepMind]

Introducing NarrativeQA: human questions & answers about entire books, plays and movies to help improve understanding of complex narratives https://t.co/rT8TsYfXuC

[Google AI @GoogleAI]

Building on TTS models like 'Tacotron' and deep generative models of raw audio like 'Wavenet', we introduce 'Tacotron 2' a neural network architecture for speech synthesis directly from text. Learn more, and check out some audio samples → https://t.co/4dD0KPES0L

[Google AI @GoogleAI]

Learn about a new end-to-end automatic speech recognition (ASR) model from the Google Brain and Speech teams that achieves a 16% relative improvement in the word error rate (WER) when compared to a conventional production system → https://t.co/KUVxiIEXRc