https://github.com/prakhr/coursera-specializations
Contains courses in specializations of coursera on deep learning
https://github.com/prakhr/coursera-specializations
cats-vs-dogs convolutional-neural-networks embedding-layer-keras fashion-mnist-dataset iris-classification keras-implementations mnist mnist-handwriting-recognition mobilenet python3 rock-paper-scissors tensorflow-models tensorflowjs toxicity transfer-learning wdbc
Last synced: 7 months ago
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Contains courses in specializations of coursera on deep learning
- Host: GitHub
- URL: https://github.com/prakhr/coursera-specializations
- Owner: prakHr
- Created: 2020-01-26T17:27:32.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2023-05-20T13:14:21.000Z (over 2 years ago)
- Last Synced: 2025-03-16T16:51:45.050Z (8 months ago)
- Topics: cats-vs-dogs, convolutional-neural-networks, embedding-layer-keras, fashion-mnist-dataset, iris-classification, keras-implementations, mnist, mnist-handwriting-recognition, mobilenet, python3, rock-paper-scissors, tensorflow-models, tensorflowjs, toxicity, transfer-learning, wdbc
- Language: Jupyter Notebook
- Homepage: https://www.deeplearning.ai/deep-learning-specialization/
- Size: 16.2 MB
- Stars: 1
- Watchers: 1
- Forks: 3
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Coursera-Specializations on CNNs
[Coursera Specializations] - Python-Javascript-Programming-Tensorflow-Algorithms-Deployment
Simple predictions and algorithms implemented with tensorflow and google colab(courses has the colab environment setup)
You can download 200 OK! from __[web apps](https://chrome.google.com/webstore/detail/web-server-for-chrome/ofhbbkphhbklhfoeikjpcbhemlocgigb?hl=en)__ and other project material from __[here](https://github.com/lmoroney/dlaicourse "Specializations")__
## Table of contents
* [Learnt Skills](#learnt)
* [Setup](#setup)
* [Modules](#modules)
* [Certificate](#certificates)
---
### Learnt
You will learn how to use different methods of tensorflow, in other words to build a pipeline from __preprocessing steps__ to building models and finally __saving models in a necessary format__. You will also use javascript modules to __read from csv files__ and predict outputs.
Then you will learn what are __the necessary layers to put__ in different courses while building CNNs from scratch.
Apart from that you will learn hypertuning a parameter, how tweaking them affects your accuracy and convergence as well as instability.
---
### Setup
The __Sublime Text__ for writing javascript Environment download, documentation, and programming resources are available at: https://www.sublimetext.com/3.
I prefer setting up a anaconda environment, this way you can install different versions of tensorflow easily.
Or learn by running scripts on google colab using their sweet gpu for increased runtime(Note that your variables and files will get lost after a fresh run in colab).
---
### Modules
* tensorflow
* keras
* shutil
* zipfile
* random
* google.colab
* numpy
* json
* matplotlib
---
### Certificates
you can get a certificate like [this](https://www.coursera.org/account/accomplishments/specialization/certificate/PKMVM99Q5MSE "Certified by coursera people") if not choose to audit the course