neural-networks-and-deep-learning | Code samples for my book "Neural Networks and Deep Learning" | Machine Learning library
kandi X-RAY | neural-networks-and-deep-learning Summary
kandi X-RAY | neural-networks-and-deep-learning Summary
neural-networks-and-deep-learning is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning applications. neural-networks-and-deep-learning has no bugs, it has no vulnerabilities, it has build file available and it has medium support. You can download it from GitHub.
This repository contains code samples for my book on "Neural Networks and Deep Learning". The code is written for Python 2.6 or 2.7. Michal Daniel Dobrzanski has a repository for Python 3 here. I will not be updating the current repository for Python 3 compatibility. The program src/network3.py uses version 0.6 or 0.7 of the Theano library. It needs modification for compatibility with later versions of the library. I will not be making such modifications. As the code is written to accompany the book, I don't intend to add new features. However, bug reports are welcome, and you should feel free to fork and modify the code.
This repository contains code samples for my book on "Neural Networks and Deep Learning". The code is written for Python 2.6 or 2.7. Michal Daniel Dobrzanski has a repository for Python 3 here. I will not be updating the current repository for Python 3 compatibility. The program src/network3.py uses version 0.6 or 0.7 of the Theano library. It needs modification for compatibility with later versions of the library. I will not be making such modifications. As the code is written to accompany the book, I don't intend to add new features. However, bug reports are welcome, and you should feel free to fork and modify the code.
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neural-networks-and-deep-learning has a medium active ecosystem.
It has 14472 star(s) with 6261 fork(s). There are 1097 watchers for this library.
It had no major release in the last 6 months.
neural-networks-and-deep-learning has no issues reported. There are 4 open pull requests and 0 closed requests.
It has a neutral sentiment in the developer community.
The latest version of neural-networks-and-deep-learning is current.
Quality
neural-networks-and-deep-learning has 0 bugs and 0 code smells.
Security
neural-networks-and-deep-learning has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
neural-networks-and-deep-learning code analysis shows 0 unresolved vulnerabilities.
There are 0 security hotspots that need review.
License
neural-networks-and-deep-learning does not have a standard license declared.
Check the repository for any license declaration and review the terms closely.
Without a license, all rights are reserved, and you cannot use the library in your applications.
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neural-networks-and-deep-learning releases are not available. You will need to build from source code and install.
Build file is available. You can build the component from source.
neural-networks-and-deep-learning saves you 675 person hours of effort in developing the same functionality from scratch.
It has 1563 lines of code, 111 functions and 26 files.
It has high code complexity. Code complexity directly impacts maintainability of the code.
Top functions reviewed by kandi - BETA
kandi has reviewed neural-networks-and-deep-learning and discovered the below as its top functions. This is intended to give you an instant insight into neural-networks-and-deep-learning implemented functionality, and help decide if they suit your requirements.
- Make plots from a file
- Plots an overlaid overlays
- Plots the training accuracy
- Plot test accuracy
- Plot test cost
- Plot training cost on the training data
- Wrapper for load_data
- Load training data
- Return vectorized result
- Plot a rotated image
- Generates a matplotlib matplotlib
- Train a network
- Load the MNIST dataset
- Get images from training set
Get all kandi verified functions for this library.
neural-networks-and-deep-learning Key Features
No Key Features are available at this moment for neural-networks-and-deep-learning.
neural-networks-and-deep-learning Examples and Code Snippets
Simple Multilayer Perceptron Example by Farza
Python
Lines of Code : 41
License : No License
Lines of Code : 41License : No License
Copygit clone https://github.com/farzaa/SimpleMultilayerPerceptron.git
cd SimpleMultilayerPerceptron
pip install numpy
python simple_mlp.py
def train(inputs, keys, weights):
for iter in xrange(20000):
prediction = applySigmoid(np.dot(inputs, weight A Fast Tsetlin Machine Implementation Employing Bit-Wise Operators,Data Preparation
CLines of Code : 28License : Permissive (MIT)
Lines of Code : 28License : Permissive (MIT)Copy............................
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PythonLines of Code : 5License : No License
Lines of Code : 5License : No LicenseCopy import mnist_loader
training_data, validation_data, test_data = mnist_loader.load_data_wrapper()
import network
net = network.Network([784, 30, 10])
net.SGD(training_data, 30, 10, 3.0, test_data=test_data)
Community Discussions
Trending Discussions on neural-networks-and-deep-learning
QUESTION
Are the following python code snippets equivalent?
Asked 2021-Sep-11 at 09:38
The following has to do with implementing a neural network in python:
...ANSWER
Answered 2021-Sep-10 at 02:54They are not equivalent. The top one will sum nablas over minibatch. The bottom one will only keep values from the last sample.
QUESTION
Inconsitencies/Inaccuracies between torchvision.datasets.MNIST and Michael Nielsens neuralnetworksanddeeplearning
Asked 2020-Oct-24 at 09:55
I printed the vectorized form of the first training image of the mnist dataset of pytorch and https://github.com/mnielsen/neural-networks-and-deep-learning/tree/master/data . The difference seems too big for just floating point precision error.
Full Diff of first mnist train image: https://www.diffchecker.com/6y6YTFiN
Code to reproduce:
...ANSWER
Answered 2020-Oct-24 at 09:55MNIST images consist of pixel values that are integers in the range 0 to 255 (inclusive). To produce the tensors you are looking at, those integer values have been normalised to lie between 0.0 and 1,0, by dividing them all by some constant factor. It appears that your two sources chose different normalising factors: 255 in one case and 256 in the other.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install neural-networks-and-deep-learning
You can download it from GitHub.
You can use neural-networks-and-deep-learning like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
You can use neural-networks-and-deep-learning like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.
Support
For any new features, suggestions and bugs create an issue on GitHub.
If you have any questions check and ask questions on community page Stack Overflow .
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