https://github.com/SidharthRai/Regression-and-Technical-Analysis-of-Stock-Market

Regression and Technical Analysis of Stock Market

by Sidharth Rai

Link to Project Report: http://bit.ly/Analysis_Project

This project is based on a complete mathematical analysis using Technical Analysis which is used to calculate the unknown patterns in the behaviors and changes of Stock Prices over a period of the month because of wide acceptability of Equity-based market.

• Stars - 6
• Forks - 5

This is my research project too, find my research work on https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3356304

thanks to :Sidharth Rai

Course 3 of the deeplearning.ai TensorFlow Specialization is now available on Coursera! You’ll learn how to process text using tokenization and train LSTMs to create original poetry. You can enroll in the Specialization for $49/month or audit for free: http://bit.ly/2ZkmPRY
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Recent developments in pre-trained context based embeddings like BERT, GPT has already pushed the boundaries in NLP. Now there is a new entrant to the list - XLNet

XLNet - Generalized Autoregressive Pretraining for Language Understanding is outperforming BERT and achieving SOTA results on multiple NLP tasks.

Code : https://lnkd.in/fp8rRVc
Paper : https://lnkd.in/fb5FGUh

#nlp #deeplearning
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This is a very dense and highly mathematical A-Z overview of text mining. The book is not one for the beach but is packed with useful information and practical tips.

Many working in text mining would probably find it a useful reference. It may also be of interest to marketing science people who use the results of text mining in advanced analytics or who are involved in marketing research R&D.

The author, Charu C. Aggarwal, is a Distinguished Research Staff Member at the IBM T. J. Watson Research Center. He has published more than 350 papers in refereed conferences and journals on data mining topics.

Though his writing style is no-nonsense, Aggarwal's enthusiasm for the topic comes through loud and clear. It is telling that in this and others of his I've read he uses the term "AI" sparingly. The book is not a hyped sales pitch in disguise. Far from it.

https://www.springer.com/gp/book/9783319735306

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Text mining and Natural Language Processing are highly specialized fields with many specializations within. I'm not an expert in these areas but have read up on them because of the nature my work.

While I would hesitate to call them statistics, familiar statistical methods do play a role and a statistician completely new to these fields probably would not find them bizarre. The same holds for AI. IMO.

Here are a few other books on these subjects I've found helpful if sometimes challenging:

- Foundations of Computational Linguistics (Hausser)
- The Handbook of Computational Linguistics (Clark et al.)
- Sentiment Analysis: Mining Opinions, Sentiments, and Emotions (Liu)
- Neural Network Methods in Natural Language Processing (Goldberg)
- Social Media Intelligence (Moe and Schweidel)
- Natural Language Processing for Social Media (Farzindar and Inkpen)
- Machine Translation (Poibeau)
- Text Mining in Practice with R (Kwartler)

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One of the BEST #MachineLearning Glossary by Google

It will definitely come in handy - https://lnkd.in/gNiE9JT

Link to learn more about Machine Learning:

✅ Course 1 : A comprehensive Learning Path to become Data Scientist in 2019
Link : https://bit.ly/2HOthei

✅ Course 2 : Experiments with Data
Link : https://bit.ly/2HQuQbw

✅ Course 3 : Python for Data Science
Link : https://bit.ly/2HOG5RG

✅ Course 4 : Twitter Sentiments Analysis
Link : https://bit.ly/2HR8O8A

✅ Course 5 : Creating Time Series Forecast with Python
Link : https://bit.ly/2XniU6r

✅ Course 6 : A comprehensive path for learning Deep Learning in 2019
Link : https://bit.ly/2HO1VVJ

✅ Course 7 : Loan Prediction Practice problem
Link : https://bit.ly/2IcynQl

✅ Course 8 : Big mart Sales Problem using R
Link : https://bit.ly/2JUlZIb

#announcements #datascientist #machinelearning #datascience #artificialintelligence

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If you're interested in learning a simple and powerful data cleaning framework for your work, have a look at this post.

Data cleaning takes nearly 60 - 70 % of our time and all the fancy models & visualizations are created after slogging hours of cleaning the data.

If you need a shiny report at the end which will answer all the business questions, you have to go through the time consuming process for yourself.

Real world data is not as clean as kaggle datasets but still you can find datasets which are not ready made for analysis in UCI or Kaggle to work on.

Try these things on a dataset this weekend and share your work with the community.

Link to first post <- https://lnkd.in/fQmem8d

Link to the second post <- https://lnkd.in/ffrQqgC

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When to Trust Your Model: Model-Based Policy Optimization

Janner et al.: https://lnkd.in/eTmBjA9

#ArtificialIntelligence #DeepLearning #ReinforcementLearning

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open sourcing PyRobot, a lightweight, high-level interface that lets #AI researchers get up and running with #robotics experiments in just hours. No specialized robotics expertise needed! https://ai.facebook.com/blog/open-sourcing-pyrobot-to-accelerate-ai-robotics-research/

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11 things I learned from the Machine Learning for Coders course at fastdotai"
https://medium.com/yottabytes/11-things-i-learned-from-the-machine-learning-for-coders-course-at-fast-ai-799468b089bc?source=friends_link&sk=337416e814280d88e7bfad994cac8533
#machinelearning #datascience #ml #python #bigdata

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the #CVPR2019 Low-Power Image Recognition Challenge (LPIIRC) winning teams from Amazon, Alibaba, Expasoft, Tsinghua, MIT and Qualcomm. Learn more about the challenge at
https://rebootingcomputing.ieee.org/lpirc .

✴️ @AI_Python_EN

#CVPR2019 presenting Sim-to-Real via Sim-to-Sim: Data-efficient Robotic Grasping via Randomized-to-Canonical Adaptation Networks (RCAN).

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have released the code and data for our #CVPR2019 paper on hand-object reconstruction.
http://www.di.ens.fr/willow/research/obman/

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the #CVPR2019 Google Booth will host demos featuring work on Increasing AR Realism Using Lighting
http://goo.gle/2KwK5ce
and teaching people how to dance with the Dance Like app.
http://goo.gle/2X18ddS .

✴️ @AI_Python_EN

Check out Off-Policy Classification, a new method to evaluate the performance of #reinforcementlearning agents trained entirely on data from prior agents, enabling selective testing of only the most promising models on real-world robots. Learn more below!

https://ai.googleblog.com/2019/06/off-policy-classification-new.html

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Waymo just announced the release of large open dataset at #CVPR2019

https://waymo.com/open

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Artificial Intelligence Based Cloud Distributor (AI-CD): Probing Low Cloud Distribution with a Conditional Generative Adversarial Network

https://arxiv.org/abs/1905.08700

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The 2019 IEEE Conference on Computer Vision and Pattern Recognition

Best Papers

https://syncedreview.com/2019/06/18/cvpr-2019-attracts-9k-attendees-best-papers-announced-imagenet-honoured-10-years-later/

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NLP + Deep Leaning checked. Was painfully awesome. Now what's next? Can't waste it... or maybe CNN and RL? #cs224n #deeplearning #NLP

🐼🤹‍♂️ pandas trick:
Two easy ways to reduce DataFrame memory usage:
1. Only read in columns you need
2. Use 'category' data type with categorical data

Example:
df = pd.read_csv('file.csv', usecols=['A', 'C', 'D'], dtype={'D':'category'})

#Python #DataScience

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Predicting demands using AI? Just focus on the things that matter

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