deep-learning-from-scratch | Deep Learning from Scratch & quot ; ( O & # 39 ; Reilly Japan | Machine Learning library

by oreilly-japan Jupyter Notebook Version: Current License: MIT

X-Ray Key Features Code Snippets Community Discussions(2)Vulnerabilities Install Support

kandi X-RAY | deep-learning-from-scratch Summary

deep-learning-from-scratch is a Jupyter Notebook library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Tensorflow applications. deep-learning-from-scratch has no bugs, it has no vulnerabilities, it has a Permissive License and it has medium support. You can download it from GitHub.

"Deep Learning from Scratch" (O'Reilly Japan, 2016)

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deep-learning-from-scratch has a medium active ecosystem.

It has 3560 star(s) with 3146 fork(s). There are 223 watchers for this library.

It had no major release in the last 6 months.

There are 10 open issues and 20 have been closed. On average issues are closed in 260 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of deep-learning-from-scratch is current.

Quality

deep-learning-from-scratch has 0 bugs and 51 code smells.

Security

deep-learning-from-scratch has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

deep-learning-from-scratch code analysis shows 0 unresolved vulnerabilities.

There are 1 security hotspots that need review.

License

deep-learning-from-scratch is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

deep-learning-from-scratch releases are not available. You will need to build from source code and install.

Installation instructions are not available. Examples and code snippets are available.

deep-learning-from-scratch saves you 878 person hours of effort in developing the same functionality from scratch.

It has 2009 lines of code, 154 functions and 59 files.

It has medium code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed deep-learning-from-scratch and discovered the below as its top functions. This is intended to give you an instant insight into deep-learning-from-scratch implemented functionality, and help decide if they suit your requirements.

Compute the gradient of the network
Computes the loss of the last layer
Predict the given input
Calculates the gradient of the network
Calculate the loss function
Provide prediction for each layer
Load MNIST dataset
Change one hot label
Calculate the numerical gradient of the loss function
Predict input data
Visualize filters
Compute the accuracy of the model
Calculate accuracy
Compute the reduction of dout
Predict the value of a function
Backward computation
Gradient descent
Forward the image
Compute the convolution matrix
Shuffle a dataset
Compute the gradient of the gradients
Compute the gradient of the function
Calculates the numerical gradient of the loss function
Calculate numerical gradient
Calculate the gradient of the network
Calculate the numerical gradient of a function

Get all kandi verified functions for this library.

deep-learning-from-scratch Key Features

No Key Features are available at this moment for deep-learning-from-scratch.

deep-learning-from-scratch Examples and Code Snippets

No Code Snippets are available at this moment for deep-learning-from-scratch.

Community Discussions

Trending Discussions on deep-learning-from-scratch

Dealing with interdependent files in graph-parallel computation

Python: The implementation of im2col which takes the advantages of 6 dimensional array?

QUESTION

Dealing with interdependent files in graph-parallel computation

Asked 2020-Mar-28 at 17:25

I’m trying to parallelize the following code (MCVE) by creating a task graph using dask.delayed (or by implementing a computational graph myself):

...

ANSWER

Answered 2020-Mar-28 at 17:25

I would recommend your current approach of passing through dependencies explicitly.

Source https://stackoverflow.com/questions/60898361

QUESTION

Python: The implementation of im2col which takes the advantages of 6 dimensional array?

Asked 2018-May-13 at 18:53

I'm reading an implementation of im2col from a deep learning book(At chapter 7, CNN), which its purpose is to transform a 4 dimensional array into 2 dimensional. I don't know why there is a 6 dimensional array in the implementation. I'm very interested about what's the idea behind the algorithm the author used.

I've tried to search many papers of the implementation of im2col, but none of them using high dimensional array like this. The currently materials I found useful for visualization of the process of im2col is the picture of this paper - HAL Id: inria-00112631

...

ANSWER

Answered 2018-May-13 at 14:04

It looks like this function is just rearranging each of the C colour-channels in each of the N images into a (out_h x out_w) grid of overlapping image patches of size (filter_h x filter_w), and then flattening that into a 2d array where each row is a vector of pixels in an image patch.

The dimensions of the 6-D col (before being transposed and reshaped) are:

[sample, channel, y_position_within_patch, x_position_within_patch, y_patch_index, x_patch_index]

So for example col[n, c, :, :, i, j] will be a 2-d image patch (the i'th-patch from the top, and j'th from the left in the grid of image patches).

After the transpose and reshape, col[n*c*i*j, :] will refer to this same image patch, but with all the pixels flattened into a vector.

Source https://stackoverflow.com/questions/50292750

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install deep-learning-from-scratch

You can download it from GitHub.

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