1. The document describes how to implement regression with neural networks using TensorFlow on Amazon Web Services. It involves launching an EC2 instance, connecting to it, and running a Jupyter notebook to generate sample data and build a neural network model to predict outputs.
2. A neural network with three dense layers is created using Keras API to predict a numeric output value. The model is trained on a training set for 500 epochs and tested on a held-out test set.
3. Regression is performed to predict a value based on a single input feature, with the goal of minimizing mean squared error loss. The model learns from the training data through backpropagation and tweaks the weights to improve predictions.
Introduction to use machine learning in python and pascal to do such a thing like train prime numbers when there are algorithms in place to determine prime numbers. See a dataframe, feature extracting and a few plots to re-search for another hot experiment to predict prime numbers.
In this article you will learn hot to use tensorflow Softmax Classifier estimator to classify MNIST dataset in one script.
This paper introduces also the basic idea of a artificial neural network.
This tutor shows the train and test set split with histogram and a probability density function in scikit-learn on synthetic datasets. The dataset is very simple as a reference of understanding.
Introduction to use machine learning in python and pascal to do such a thing like train prime numbers when there are algorithms in place to determine prime numbers. See a dataframe, feature extracting and a few plots to re-search for another hot experiment to predict prime numbers.
In this article you will learn hot to use tensorflow Softmax Classifier estimator to classify MNIST dataset in one script.
This paper introduces also the basic idea of a artificial neural network.
This tutor shows the train and test set split with histogram and a probability density function in scikit-learn on synthetic datasets. The dataset is very simple as a reference of understanding.
Explanation on Tensorflow example -Deep mnist for expert홍배 김
you can find the exact and detailed network architecture of 'Deep mnist for expert' example of tensorflow's tutorial. I also added descriptions on the program for your better understanding.
Video: https://youtu.be/dYhrCUFN0eM
Article: https://medium.com/p/the-gentlest-introduction-to-tensorflow-248dc871a224
Code: https://github.com/nethsix/gentle_tensorflow/blob/master/code/linear_regression_one_feature.py
This alternative introduction to Google's official Tensorflow (TF) tutorial strips away the unnecessary concepts that overly complicates getting started. The goal is to use TF to perform Linear Regression (LR) that has only a single-feature. We show how to model the LR using a TF graph, how to define the cost function to measure how well the an LR model fits the dataset, and finally train the LR model to find the best fit model.
An introduction to Google's AI Engine, look deeper into Artificial Networks and Machine Learning. Appreciate how our simplest neural network be codified and be used to data analytics.
Cheat Sheet for Machine Learning in Python: Scikit-learnKarlijn Willems
Get started with machine learning in Python thanks to this scikit-learn cheat sheet, which is a handy one-page reference that guides you through the several steps to make your own machine learning models. Thanks to the code examples, you won't get lost!
Pybelsberg is a project allowing constraint-based programming in Python using the Z3 theorem prover [1].
It is available on Github [2] and is licensed under the BSD 3-Clause License.
By Robert Lehmann, Christoph Matthies, Conrad Calmez, Thomas Hille.
See also Babelsberg/R [4] and Babelsberg/JS [5].
[1] https://github.com/Z3Prover/z3
[2] https://github.com/babelsberg/pybelsberg
[3] http://opensource.org/licenses/BSD-3-Clause
[4] https://github.com/timfel/babelsberg-r
[5] https://github.com/timfel/babelsberg-js
Gentlest Introduction to Tensorflow - Part 3Khor SoonHin
Articles:
* https://medium.com/all-of-us-are-belong-to-machines/gentlest-intro-to-tensorflow-part-3-matrices-multi-feature-linear-regression-30a81ebaaa6c
* https://medium.com/all-of-us-are-belong-to-machines/gentlest-intro-to-tensorflow-4-logistic-regression-2afd0cabc54
Video: https://youtu.be/F8g_6TXKlxw
Code: https://github.com/nethsix/gentle_tensorflow
In this part, we:
* Use Tensorflow for linear regression models with multiple features
* Use Tensorflow for logistic regression models with multiple features. Specifically:
* Predict multi-class/discrete outcome
* Explain why we use cross-entropy as cost function
* Explain why we use softmax
* Tensorflow Cheatsheet #1
* Single feature linear regression
* Multi-feature linear regression
* Multi-feature logistic regression
* Logistic regression, logistic loss (log loss)
* stochastic optimization
* adding new features, generalized linear model
* Kernel trick, intro to SVM
* Overfitting
* Decision trees for classification and regression
* Building trees greedily: Gini index, entropy
* Trees fighting with overfitting: pre-stopping and post-pruning
* Feature importances
Explanation on Tensorflow example -Deep mnist for expert홍배 김
you can find the exact and detailed network architecture of 'Deep mnist for expert' example of tensorflow's tutorial. I also added descriptions on the program for your better understanding.
Video: https://youtu.be/dYhrCUFN0eM
Article: https://medium.com/p/the-gentlest-introduction-to-tensorflow-248dc871a224
Code: https://github.com/nethsix/gentle_tensorflow/blob/master/code/linear_regression_one_feature.py
This alternative introduction to Google's official Tensorflow (TF) tutorial strips away the unnecessary concepts that overly complicates getting started. The goal is to use TF to perform Linear Regression (LR) that has only a single-feature. We show how to model the LR using a TF graph, how to define the cost function to measure how well the an LR model fits the dataset, and finally train the LR model to find the best fit model.
An introduction to Google's AI Engine, look deeper into Artificial Networks and Machine Learning. Appreciate how our simplest neural network be codified and be used to data analytics.
Cheat Sheet for Machine Learning in Python: Scikit-learnKarlijn Willems
Get started with machine learning in Python thanks to this scikit-learn cheat sheet, which is a handy one-page reference that guides you through the several steps to make your own machine learning models. Thanks to the code examples, you won't get lost!
Pybelsberg is a project allowing constraint-based programming in Python using the Z3 theorem prover [1].
It is available on Github [2] and is licensed under the BSD 3-Clause License.
By Robert Lehmann, Christoph Matthies, Conrad Calmez, Thomas Hille.
See also Babelsberg/R [4] and Babelsberg/JS [5].
[1] https://github.com/Z3Prover/z3
[2] https://github.com/babelsberg/pybelsberg
[3] http://opensource.org/licenses/BSD-3-Clause
[4] https://github.com/timfel/babelsberg-r
[5] https://github.com/timfel/babelsberg-js
Gentlest Introduction to Tensorflow - Part 3Khor SoonHin
Articles:
* https://medium.com/all-of-us-are-belong-to-machines/gentlest-intro-to-tensorflow-part-3-matrices-multi-feature-linear-regression-30a81ebaaa6c
* https://medium.com/all-of-us-are-belong-to-machines/gentlest-intro-to-tensorflow-4-logistic-regression-2afd0cabc54
Video: https://youtu.be/F8g_6TXKlxw
Code: https://github.com/nethsix/gentle_tensorflow
In this part, we:
* Use Tensorflow for linear regression models with multiple features
* Use Tensorflow for logistic regression models with multiple features. Specifically:
* Predict multi-class/discrete outcome
* Explain why we use cross-entropy as cost function
* Explain why we use softmax
* Tensorflow Cheatsheet #1
* Single feature linear regression
* Multi-feature linear regression
* Multi-feature logistic regression
* Logistic regression, logistic loss (log loss)
* stochastic optimization
* adding new features, generalized linear model
* Kernel trick, intro to SVM
* Overfitting
* Decision trees for classification and regression
* Building trees greedily: Gini index, entropy
* Trees fighting with overfitting: pre-stopping and post-pruning
* Feature importances
This tutor introduces the basic idea of machine learning with a very simple example. Machine learning teaches machines (and me too) to learn to carry out tasks and concepts by themselves. It is that simple, so here is an overview:
http://www.softwareschule.ch/examples/machinelearning.jpg
An Artificial Neural Network (ANN) is a computational model inspired by the structure and functioning of the human brain's neural networks. It consists of interconnected nodes, often referred to as neurons or units, organized in layers. These layers typically include an input layer, one or more hidden layers, and an output layer.
How to Build a Neural Network and Make PredictionsDeveloper Helps
Lately, people have been really into neural networks. They’re like a computer system that works like a brain, with nodes connected together. These networks are great at sorting through big piles of data and figuring out patterns to solve hard problems or guess stuff. And you know what’s super cool? They can keep on learning forever.
Creating and deploying neural networks can be a challenging process, which largely depends on the specific task and dataset you’re dealing with. To succeed in this endeavor, it’s crucial to possess a solid grasp of machine learning concepts, along with strong programming skills. Additionally, a deep understanding of the chosen deep learning framework is essential. Moreover, it’s imperative to prioritize responsible and ethical usage of AI models, especially when integrating them into real-world applications.
Learn from : https://www.developerhelps.com/how-to-build-a-neural-network-and-make-predictions/
Deep learning is a technique that basically mimics the human brain. So, the Scientist and Researchers taught can we make machines learn in the same way so, there is where the deep learning concept came that led to the invention called the neural network
MATLAB/SIMULINK for Engineering Applications day 2:Introduction to simulinkreddyprasad reddyvari
3 days Hands on workshop on MATLAB/SIMULINK for Engineering Applications:
this workshop aims to make students to aware of MATLAB to do own projects in engineering life with best available technology E-Simulink Softwares and tools.
First project in DNN
The steps for first project are as follows:
Load Data.
Define Keras Model.
Compile Keras Model.
Fit Keras Model.
Evaluate Keras Model.
Make Predictions
Brief introduction of neural network including-
1. Fitting Tool
2. Clustering data with a self-organising map
3. Pattern Recognition Tool
4. Time Series Toolbox
Similar to Neural networks using tensor flow in amazon deep learning server (20)
CW RADAR, FMCW RADAR, FMCW ALTIMETER, AND THEIR PARAMETERSveerababupersonal22
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6th International Conference on Machine Learning & Applications (CMLA 2024)ClaraZara1
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Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
NUMERICAL SIMULATIONS OF HEAT AND MASS TRANSFER IN CONDENSING HEAT EXCHANGERS...ssuser7dcef0
Power plants release a large amount of water vapor into the
atmosphere through the stack. The flue gas can be a potential
source for obtaining much needed cooling water for a power
plant. If a power plant could recover and reuse a portion of this
moisture, it could reduce its total cooling water intake
requirement. One of the most practical way to recover water
from flue gas is to use a condensing heat exchanger. The power
plant could also recover latent heat due to condensation as well
as sensible heat due to lowering the flue gas exit temperature.
Additionally, harmful acids released from the stack can be
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heat and mass transfer processes. A computer program for
numerical simulations of water (H2O) and sulfuric acid (H2SO4)
condensation in a flue gas condensing heat exchanger was
developed using MATLAB. Governing equations based on
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using an iterative solution technique with calculations of heat
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Using recycled concrete aggregates (RCA) for pavements is crucial to achieving sustainability. Implementing RCA for new pavement can minimize carbon footprint, conserve natural resources, reduce harmful emissions, and lower life cycle costs. Compared to natural aggregate (NA), RCA pavement has fewer comprehensive studies and sustainability assessments.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
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Neural networks using tensor flow in amazon deep learning server
1. RAMCO INSTITUTE OF TECHNOLOGY
RAJAPALAYAM
13.04.2020
Prepared by Dr.M.Kaliappan, Associate Professor/CSE
--------------------------------------------------------------------------------------------------------------------
Topic: Implementation of Regression with Neural Networks using TensorFlow in Amazon
Deep Learning Server
---------------------------------------------------------------------------------------------------------------------
1. Visit to https://aws.amazon.com/deep-learning/
2. Sign in to console , if not, signup with your data and make payment of Rs.2/-
2. 3. Click on services then, click on EC2
4. Click on EC2 Dashboard and launch instances
3. 5. Press Ctrl+F (search) : Search a keyword deep learning
6. Select Deep learning AMI(Amazon Linux2)
7. Choose an instance type
8. Click Review and launch
9. Click launch
4. 10. Select create a new keypair
11. Give key file name: key(any name)
12. Download key pair
A key.pem file is downloaded and saved in your computer(Download section)
6. Copy the Public DNS: ec2-3-133-153-1.us-east-2.compute.amazonaws.com
15. Open command prompt go to Downloads
Type the following command
ssh -L localhost:8888:localhost:8888 -i key1.pem ec2-user@ec2-3-133-153-1.us-east-
2.compute.amazonaws.com
9. The jupyter notebook is running at the following URL.
17. Type this URL in browser.
Click on new button. Select Environment (conda_tensorflow_p36)
Regression with Neural Networks using TensorFlow Keras API
(Blue color represents code, copy and paste in jupyter notebook)
Figure 1: Neural Network
10. In regression, the computer/machine should be able to predict a value – mostly numeric.
Generate Data: Here we are going to generate some data using our own function. This function
is a non-linear function and a usual line fitting may not work for such a function
def myfunc(x):
if x < 30:
mult = 10
elif x < 60:
mult = 20
else:
mult = 50
return x*mult
Let us check what does this function return.
print(myfunc(10))
print(myfunc(30))
print(myfunc(60))
It should print something like:
100
600
3000
Now, let us generate data. Let us import numpy library as np. here x is a numpy array of input
values. Then, we are generating values between 0 and 100 with a gap of 0.01 using arange
function. It generates a numpy array.
import numpy as np
x = np.arange(0, 100, .01)
print(x)
data:
array([0.000e+00, 1.000e-02, 2.000e-02, ..., 9.997e+01, 9.998e+01, 9.999e+01])
To call a function repeatedly on a numpy array we first need to convert the function using
vectorize. Afterwards, we are converting 1-D array to 2-D array having only one value in the
second dimension – you can think of it as a table of data with only one column.
myfuncv = np.vectorize(myfunc)
y = myfuncv(x)
11. X = x.reshape(-1, 1)
Print(X)
[[0.000e+00],
[1.000e-02],
[2.000e-02],
...
[9.997e+01],
[9.998e+01],
[9.999e+01]]
Now, we have X representing the input data with single feature and y representing the output.
We will now split this data into two parts: training set (X_train, y_train) and test set (X_test
y_test). We are going make neural ne
to produce y from X – we are going to test the model on the test set.
import sklearn.model_selection as sk
X_train, X_test, y_train, y_test = sk.train_test_split(X,y,test_size=0.33, random_state
Let us visualize how does our data looks like. Here, we are plotting only X_train vs y_train. You
can try plotting X vs y, as well as, X_test vs y_test.
import matplotlib.pyplot as plt
plt.scatter(X_train,y_train)
plt.scatter(X_test,y_test)
Now, we have X representing the input data with single feature and y representing the output.
We will now split this data into two parts: training set (X_train, y_train) and test set (X_test
y_test). We are going make neural network learn from training data, and once it has learnt
we are going to test the model on the test set.
import sklearn.model_selection as sk
X_train, X_test, y_train, y_test = sk.train_test_split(X,y,test_size=0.33, random_state
Let us visualize how does our data looks like. Here, we are plotting only X_train vs y_train. You
can try plotting X vs y, as well as, X_test vs y_test.
Figure 2: X_train vs y_train
Now, we have X representing the input data with single feature and y representing the output.
We will now split this data into two parts: training set (X_train, y_train) and test set (X_test
twork learn from training data, and once it has learnt – how
X_train, X_test, y_train, y_test = sk.train_test_split(X,y,test_size=0.33, random_state = 42)
Let us visualize how does our data looks like. Here, we are plotting only X_train vs y_train. You
12. Let us import TensorFlow libraries and check the version.
import tensorflow as tf
print(tf.__version__)
Now, let us create a neural network using Keras API of TensorFlow.
# Import the kera modules
from keras.layers import Input, Dense
from keras.models import Model
# This returns a tensor. Since the input only has one column
inputs = Input(shape=(1,))
# a layer instance is callable on a tensor, and returns a tensor
# To the first layer we are feeding inputs
x = Dense(32, activation='relu')(inputs)
# To the next layer we are feeding the result of previous call here it is h
x = Dense(64, activation='relu')(x)
x = Dense(64, activation='relu')(x)
# Predictions are the result of the neural network. Notice that the predictions are also having one
column.
predictions = Dense(1)(x)
# This creates a model that includes
# the Input layer and three Dense layers
model = Model(inputs=inputs, outputs=predictions)
# Here the loss function is mse -
model.compile(optimizer='rmsprop',
loss='mse',
Figure 2: X_test vs y_test
et us import TensorFlow libraries and check the version.
us create a neural network using Keras API of TensorFlow.
from keras.layers import Input, Dense
from keras.models import Model
# This returns a tensor. Since the input only has one column
# a layer instance is callable on a tensor, and returns a tensor
# To the first layer we are feeding inputs
x = Dense(32, activation='relu')(inputs)
# To the next layer we are feeding the result of previous call here it is h
)(x)
x = Dense(64, activation='relu')(x)
# Predictions are the result of the neural network. Notice that the predictions are also having one
# This creates a model that includes
# the Input layer and three Dense layers
odel = Model(inputs=inputs, outputs=predictions)
Mean Squared Error because it is a regression problem.
model.compile(optimizer='rmsprop',
# Predictions are the result of the neural network. Notice that the predictions are also having one
Mean Squared Error because it is a regression problem.
13. metrics=['mse'])
Let us now train the model. First, it would initialize the weights of each neuron with random
values and the using backpropagation it is going to tweak the weights in order to get the
appropriate result. Here we are running the iteration 500 times and we are feeding 100 records of
X at a time.
model.fit(X_train, y_train, epochs=500, batch_size=100) # starts training
Once we have trained the model. We can do predictions using the predict method of the model.
In our case, this model should predict y using X. Let us test it over our test set. Plot the results.
y_test = model.predict(X_test)
plt.scatter(X_test, y_test)
References:
1. https://www.tutorialspoint.com/python_deep_learning/python_deep_learning_artificial_n
eural_networks.htm
2. https://cloudxlab.com/blog/regression
. First, it would initialize the weights of each neuron with random
values and the using backpropagation it is going to tweak the weights in order to get the
appropriate result. Here we are running the iteration 500 times and we are feeding 100 records of
model.fit(X_train, y_train, epochs=500, batch_size=100) # starts training
Once we have trained the model. We can do predictions using the predict method of the model.
In our case, this model should predict y using X. Let us test it over our test set. Plot the results.
https://www.tutorialspoint.com/python_deep_learning/python_deep_learning_artificial_n
https://cloudxlab.com/blog/regression-using-tensorflow-keras-api/
. First, it would initialize the weights of each neuron with random
values and the using backpropagation it is going to tweak the weights in order to get the
appropriate result. Here we are running the iteration 500 times and we are feeding 100 records of
Once we have trained the model. We can do predictions using the predict method of the model.
In our case, this model should predict y using X. Let us test it over our test set. Plot the results.
https://www.tutorialspoint.com/python_deep_learning/python_deep_learning_artificial_n