Colleen  Little

Colleen Little

1590162780

Stock Price Prediction: Single Neural Network with Tensorflow

Let’s learn how to predict stock prices using a single layer neural network with the help of TensorFlow Backend. You’ll be in awe when you see how marvelous such a simple architecture performs on a dataset of stock prices.
The content of this blog is inspired by the Coursera Series: Sequences, Time Series and Prediction.

#tensorflow #network

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Stock Price Prediction: Single Neural Network with Tensorflow
Mckenzie  Osiki

Mckenzie Osiki

1623135499

No Code introduction to Neural Networks

The simple architecture explained

Neural networks have been around for a long time, being developed in the 1960s as a way to simulate neural activity for the development of artificial intelligence systems. However, since then they have developed into a useful analytical tool often used in replace of, or in conjunction with, standard statistical models such as regression or classification as they can be used to predict or more a specific output. The main difference, and advantage, in this regard is that neural networks make no initial assumptions as to the form of the relationship or distribution that underlies the data, meaning they can be more flexible and capture non-standard and non-linear relationships between input and output variables, making them incredibly valuable in todays data rich environment.

In this sense, their use has took over the past decade or so, with the fall in costs and increase in ability of general computing power, the rise of large datasets allowing these models to be trained, and the development of frameworks such as TensforFlow and Keras that have allowed people with sufficient hardware (in some cases this is no longer even an requirement through cloud computing), the correct data and an understanding of a given coding language to implement them. This article therefore seeks to be provide a no code introduction to their architecture and how they work so that their implementation and benefits can be better understood.

Firstly, the way these models work is that there is an input layer, one or more hidden layers and an output layer, each of which are connected by layers of synaptic weights¹. The input layer (X) is used to take in scaled values of the input, usually within a standardised range of 0–1. The hidden layers (Z) are then used to define the relationship between the input and output using weights and activation functions. The output layer (Y) then transforms the results from the hidden layers into the predicted values, often also scaled to be within 0–1. The synaptic weights (W) connecting these layers are used in model training to determine the weights assigned to each input and prediction in order to get the best model fit. Visually, this is represented as:

#machine-learning #python #neural-networks #tensorflow #neural-network-algorithm #no code introduction to neural networks

Colleen  Little

Colleen Little

1590162780

Stock Price Prediction: Single Neural Network with Tensorflow

Let’s learn how to predict stock prices using a single layer neural network with the help of TensorFlow Backend. You’ll be in awe when you see how marvelous such a simple architecture performs on a dataset of stock prices.
The content of this blog is inspired by the Coursera Series: Sequences, Time Series and Prediction.

#tensorflow #network

Dominic  Feeney

Dominic Feeney

1619240400

How to Predict Stock Prices with LSTM

How to Predict Stock Prices with LSTM

A Practical Example of Stock Prices Predictions with LSTM using Keras TensorFlow

In a previous post, we explained how to predict stock prices using machine learning models. Today, we will show how we can use advanced artificial intelligence models such as the Long-Short Term Memory (LSTM). In the previous post, we have used the LSTM models for Natural Language Generation (NLG) models, like the word-based and the character-based NLG models.The LSTM ModelLong short-term memory (LSTM) is an artificial recurrent neural network (RNN) architecture used in the field of deep learning having feedback connections. Not only can process single data points such as images, but also entire sequences of data such as speech or video. For example, LSTM is applicable to tasks such as unsegmented, connected handwriting recognition, speech recognition, machine translation, anomaly detection, time series analysis, etc.

he LSTM models are computationally expensive and require many data points. Usually, we train the LSTM models using GPU instead of CPU. Tensorflow is a great library for training LSTM models.

#lstm #tensorflow #stock-price-prediction #prediction-markets #python

Training Neural Networks for price prediction with TensorFlow

Using Deep Neural Networks for regression problems might seem like overkill (and quite often is), but for some cases where you have a significant amount of high dimensional data they can outperform any other ML models.

When you learn about Neural Networks you usually start with some image classification problem like the MNIST dataset — this is an obvious choice as advanced tasks with high dimensional data is where DNNs really thrive.

Surprisingly, when you try to apply what you learned on MNIST on a regression tasks you might struggle for a while before your super-advanced DNN model is any better than a basic Random Forest Regressor. Sometimes you might never reach that moment…

In this guide, I listed some key tips and tricks learned while using DNN for regression problems. The data is a set of nearly 50 features describing 25k properties in Warsaw. I described the feature selection process in my previous article: feature-selection-and-error-analysis-while-working-with-spatial-data so now we will focus on creating the best possible model predicting property price per m2 using the selected features.

The code and data source used for this article can be found on GitHub.

1. Getting started

When training a Deep Neural Network I usually follow these key steps:

  • A) Choose a default architecture — no. of layers, no. of neurons, activation
  • B) Regularize model
  • C) Adjust network architecture
  • D) Adjust the learning rate and no. of epochs
  • E) Extract optimal model using CallBacks

Usually creating the final model takes a few runs through all of these steps but an important thing to remember is: DO ONE THING AT A TIME. Don’t try to change architecture, regularization, and learning rate at the same time as you will not know what really worked and probably spend hours going in circles.

#deep-learning #regression #tensorflow #machine-learning #neural-networks #deep learning

Neural Networks: Importance of Optimizer Selection

When constructing a neural network, there are several optimizers available in the Keras API in order to do so.

An optimizer is used to minimise the loss of a network by appropriately modifying the weights and learning rate.

For regression-based problems (where the response variable is in numerical format), the most frequently encountered optimizer is the **Adam **optimizer, which uses a stochastic gradient descent method that estimates first-order and second-order moments.

The available optimizers in the Keras API are as follows:

  • SGD
  • RMSprop
  • Adam
  • Adadelta
  • Adagrad
  • Adamax
  • Nadam
  • Ftrl

The purpose of choosing the most suitable optimiser is not necessarily to achieve the highest accuracy per se — but rather to minimise the training required by the neural network to achieve a given level of accuracy. After all, it is much more efficient if a neural network can be trained to achieve a certain level of accuracy after 10 epochs than after 50, for instance.

#machine-learning #neural-network-algorithm #data-science #keras #tensorflow #neural networks