Putting on LSTM tends The right time Assortment Numbers that Strengthener Gaining knowledge of for Transaction Procedures

Link to this course:
Applying LSTM to Time Series Data – Reinforcement Learning for Trading Strategies
Machine Learning for Trading Specialization
In the final course from the Machine Learning for Trading specialization, you will be introduced to reinforcement learning (RL) and the benefits of using reinforcement learning in trading strategies. You will learn how RL has been integrated with neural networks and review LSTMs and how they can be applied to time series data. By the end of the course, you will be able to build trading strategies using reinforcement learning, differentiate between actor-based policies and value-based policies, and incorporate RL into a momentum trading strategy.

To be successful in this course, you should have advanced competency in Python programming and familiarity with pertinent libraries for machine learning, such as Scikit-Learn, StatsModels, and Pandas. Experience with SQL is recommended. You should have a background in statistics (expected values and standard deviation, Gaussian distributions, higher moments, probability, linear regressions) and foundational knowledge of financial markets (equities, bonds, derivatives, market structure, hedging).
Reinforcement Learning Model Development, Reinforcement Learning Trading Algorithm Optimization, Reinforcement Learning Trading Strategy Development, Reinforcement Learning Trading Algo Development
It was easy to follow but not easy. I learned a lot and I now have the confidence to implement Reinforcement learning to my own FX trading strategies. Thank you so much.,Great introduction to some very interesting concepts. Lots of hands on examples, and plenty to learn
In the previous module, reinforcement learning was discussed before neural networks were introduced. In this module, we look at how reinforcement learning has been integrated with neural networks. We also look at LSTMs and how they can be applied to time series data.
Applying LSTM to Time Series Data – Reinforcement Learning for Trading Strategies
Copyright Disclaimer under Section 107 of the copyright act 1976, allowance is made for fair use for purposes such as criticism, comment, news reporting, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favour of fair use.

Statistics Assessment applying DeepLearning RNN ( LSTM ) how to Own Numbers

Data Prediction using DeepLearning RNN ( LSTM ) - Own Data

Data Prediction using DeepLearning Recurrent Neural Network LSTM – Own Data… Any help pls whatsapp +91 9994444414 , josemebin@gmail.com ,www.jitectechnologies.in
Video Rating: / 5

181 through Multivariate moment in time series forecasting taking advantage of LSTM

For a dataset just search online for ‚yahoo finance GE‘ or any other stock of your interest. Then select history and download csv for the dates you are interested.

Code generated in the video can be downloaded from here:

End to complete Multivariate Time frame Group Representing using LSTM

#datascience #deeplearning #LSTM

Entire Time Series Course – https://www.youtube.com/playlist?list=PL3N9eeOlCrP5cK0QRQxeJd6GrQvhAtpBK

In this video we will see how we can build a multi variate time series model using Deep learning LSTM sequence model. We will see end to end time series model building process in this video
Video Rating: / 5

Python: Real-time Automated Long Short Term Memory (LSTM) Short-term Load Forecasting & Plotting

Introduction 00:00:00
Introduction of LSTM 00:00:52
Introduction of RNN 00:13:03
From RNN to LSTM 00:22:56
How to build a LSTM 00:31:41
* How to build a Neural Network https://www.youtube.com/watch?v=UJ38TToVJgM
Programming Exercise 00:42:59
Details of short-term load forecasting problem 00:43:02
– Data Preparation 00:44:00
– Developing LSTM 01:03:57
– Real-time Model Prediction 01:18:19
– Real-time Plotting 1:28:10

Support FREE content: https://www.buymeacoffee.com/eMasterClass

Multivariate Time Series Prediction with LSTM and Multiple features (Predict Google Stock Price)

Build a Artificial Neural Network (ANN) with Long-Short Term Memory (LSTM) to predict value which can be impacted by multiple different features.

In this video I demonstrate how to use LSTM to predict Google Stock price (you can use any other case) by taking into consideration multiple predictors (features). Let’s say, the final stock price can be predicted by finding importance of such features as historical low price, high price, volume, adj. price, etc.

Link to github notebook: https://github.com/vb100/multivariate-lstm/blob/master/LSTM_model_stocks.ipynb

The video has 3 parts:
– Part 01. Data pre-processing (4:11)
| Step 01: Read data.
| Step 02: Data pre-processing (shaping and transformations).

– Part 02. Create a LSTM model and train it. (10:39)
| Step 03: Building-up the LSTM based Neural Network.
| Step 04: Start training.

– Part 03. Make future predictions. (13:50)
| Step 05: Make predictions for future date.
| Step 06: Visualize the predictions.

In this tutorial I used Tensorflow 1.15.0 and Keras 2.3.1

Download data from: https://finance.yahoo.com/quote/GOOG/history (check 1:59 in video).

This is real life Python code example for demonstration purposes, so the model is not very accuracy and of course could be improved or tuned.

My goal of this Python tutorial is to demonstrate how to perform LSTM predictions with multiple features (complex dataset).

Hoping it will help to undersant the way it could be implemented in real Data Science or Data Analysis projects. TIme Series forecasting with LSTM is the good choice if you want to manipulate with multiple different data features and see which ones has impact to predictions and which ones do not.

If you are interested how to run Tensorboard on this LSTM Keras model, check this tutorial: https://youtu.be/-9-Hy5dWKLE

Sorry for video quality. There were some unexpected issues with resolution.
Video Rating: / 5

Keras Tutorial #4 that in fact LSTM Content Era

This video is about building a model that can generate text using Keras. We are using an LSTM network to generate the text.

Please subscribe. That would make me happy and encourage me to keep making my content better and better.

The code for this video:

Twitter: https://twitter.com/Tanner__Gilbert
Github: https://github.com/TannerGilbert
Website: https://gilberttanner.com/
Video Rating: / 5

Time series data, in today’s age, is ubiquitous. With the emerge of sensors, IOT devices it is spanning over all the modern aspects of life from basic household devices to self-driving cars affecting all for lives. Thus classification of time series is of unique importance in current time. With the advent of deep learning techniques , there have been influx of focus on Recurrent Neural Nets (RNN) in solving tasks related with sequence and rightly so. In this talk, I would attempt to describe the reason for success of RNN’s in sequence data. Eventually we would divert towards other techniques which should be looked into when working on such problems. I will phrase examples from healthcare domain and delve into some of the other usefull techniques that can be used from Deep Learning Domain and their usefullness.

Aditya Patel is the head of data science at Stasis and has 7+ years of experience spanning over the fields of Machine Learning and Signal Processing. He graduated with Dual Master’s degree in Biomedical and Electrical Engineering from University of Southern California. He has presented his work in Machine learning at multiple peer reviewed conferences concerning healthcare domain, across the geography. He also contributed to first generation “Artificial Pancreas” project in Medtronic, Los Angeles. In his current role he is leading the advent of smart hospitals in Indian healthcare.
Video Rating: / 5

Continual Neural Networks like twitter (LSTM / RNN) Application by having Keras is that Python

#RNN #LSTM #RecurrentNeuralNetworks #Keras #Python #DeepLearning

In this tutorial, we implement Recurrent Neural Networks with LSTM as example with keras and Tensorflow backend. The same procedure can be followed for a Simple RNN.

We implement Multi layer RNN, visualize the convergence and results. We then implement for variable sized inputs.

Recurrent Neural Networks RNN / LSTM / GRU are a very popular type of Neural Networks which captures features from time series or sequential data. It has amazing results with text and even Image Captioning.

In this example we try to predict the next digit given a sequence of digits. Same concept can be extended to text images and even music.

Find the codes here
GitHub : https://github.com/shreyans29/thesemicolon
Good Reads : http://karpathy.github.io/

Check out the machine learning, deep learning and developer products

Data Science book Recommendations :

US :
Python Reinforcement Learning : https://amzn.to/30MSlIU
Machine Learning : https://amzn.to/30OuRmw
Deep Learning Essentials : https://amzn.to/336opJ9
Deep Learning : https://amzn.to/2OoSY8J
Pattern Recognition : https://amzn.to/2MgUveD

India :
Pattern Recognition : https://amzn.to/2ViNWfJ
Deep Learning : https://amzn.to/2Vp3UVC
Reinforcement Learning : https://amzn.to/2LQz0SY
Python Deep Learning : https://amzn.to/2LQvXKj
Machine Learning : https://amzn.to/2Ml6NSX

Laptop Recommendations for Data Science :

Asus : https://amzn.to/338roku
MSI : https://amzn.to/2OvdDIB
Lenovo : https://amzn.to/2OmpzMr

Dell : https://amzn.to/2OnFeet
Asus : https://amzn.to/2LPQqyZ
Lenovo : https://amzn.to/2AS7XQx

Computer Science book Recommendations :

Algorithms and Datastructures : https://amzn.to/3555P69
C programming : https://amzn.to/2nnuYrJ
Networking : https://amzn.to/2ItnOcN
Operating Systems : https://amzn.to/2LOjXsI
Database Systems : https://amzn.to/32ZqczM

India :
Computer Systems Architecture : https://amzn.to/336IxuM
Database Systems : https://amzn.to/2nntKN9
Operating Systems : https://amzn.to/2Vj1tUr
Networking : https://amzn.to/2IrnpHL
Algorithms and Datastructures : https://amzn.to/358jA3S
C programming : https://amzn.to/2oXKXNm

Book Recommendations for Developers :

Design Patterns : https://amzn.to/2Mo0M8q
Refactoring : https://amzn.to/2AItLhJ
Enterprise Application Architecture : https://amzn.to/2VgoA21
Pragmatic Programmer : https://amzn.to/2IslX89
Clean Code : https://amzn.to/2ImBKVV
Clean Coder : https://amzn.to/33845Y0
Code Complete : https://amzn.to/2OnX696
Mythical Man month : https://amzn.to/2LTGOTX

Design Patterns : https://amzn.to/2VhrPWH
Refactoring : https://amzn.to/2MmT8uG
Enterprise Application Architecture : https://amzn.to/31Q6J4t
Pragmatic Programmer : https://amzn.to/2p1fTwb
Clean Code : https://amzn.to/2LPmcvL
Code Complete : https://amzn.to/2LNUU9g
Mythical Man month : https://amzn.to/31QjFXL

Developer Laptop Recommendations :

Microsoft Surface : https://amzn.to/2nknEgk
Lenovo Thinkpad : https://amzn.to/356RNRj
Macbook Pro : https://amzn.to/2oZDzRy
Dell XPS : https://amzn.to/338tkcK

India :
Lenovo Think Pad : https://amzn.to/30Ryet4
Microsoft Surface : https://amzn.to/2VjyD6w
Dell XPS : https://amzn.to/35d6nGU
Macbook Pro : https://amzn.to/33887PW
Video Rating: / 5