Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/tirthajyoti/deep-learning-with-python

Deep learning codes and projects using Python
https://github.com/tirthajyoti/deep-learning-with-python

artificial-intelligence cnn computer-vision convolutional-neural-networks deep-learning generative-adversarial-network google-colab image-classification keras machine-learning neural-networks object-detection recurrent-neural-networks resnet rnn vgg16

Last synced: 5 days ago
JSON representation

Deep learning codes and projects using Python

Awesome Lists containing this project

README 

        
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

[![GitHub forks](https://img.shields.io/github/forks/tirthajyoti/Deep-Learning-with-Python.svg)](https://github.com/tirthajyoti/Deep-Learning-with-Python/network)

[![GitHub stars](https://img.shields.io/github/stars/tirthajyoti/Deep-Learning-with-Python.svg)](https://github.com/tirthajyoti/Deep-Learning-with-Python/stargazers)

[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg)](https://github.com/tirthajyoti/Deep-Learning-with-Python/pulls)



# Deep Learning with Python ([Website](https://dl-with-python.readthedocs.io/en/latest/))

Collection of a variety of Deep Learning (DL) code examples, tutorial-style Jupyter notebooks, and projects. 



Quite a few of the Jupyter notebooks are built on **[Google Colab](https://colab.research.google.com/)** and may employ special functions exclusive to Google Colab (for example uploading data or pulling data directly from a remote repo using standard Linux commands).



Here is the **[Github Repo](https://github.com/tirthajyoti/Deep-learning-with-Python)**.



---



Authored and maintained by **Dr. Tirthajyoti Sarkar ([Website](https://tirthajyoti.github.io))**. 


Here is my **[LinkedIn profile](https://www.linkedin.com/in/tirthajyoti-sarkar-2127aa7/)**



---



## Requirements

* Python 3.6+

* NumPy (`pip install numpy`)

* Pandas (`pip install pandas`)

* MatplotLib (`pip install matplotlib`)

* Tensorflow (`pip install tensorflow` or `pip install tensorflow-gpu`)

> Of course, to use a local GPU correctly, you need to do lot more work setting up proper GPU driver and CUDA installation. 


> If you are using Ubuntu 18.04, [here is a guide](https://mc.ai/tensorflow-gpu-installation-on-ubuntu-18-04/). 


> If you are on Windows 10, [here is a guide](https://towardsdatascience.com/installing-tensorflow-with-cuda-cudnn-and-gpu-support-on-windows-10-60693e46e781) 


> It is also highly recommended to **install GPU version in a separate virtual environment**, so as to not mess up the default system install.

* Keras (`pip install keras`)



**NOTE**: Most of the Jupyter notebooks in this repo are built on **[Google Colaboratory](https://colab.research.google.com/)** using **[Google GPU cluster](https://cloud.google.com/gpu/)** and a virtual machine. Therefore, you may not need to install these packages on your local machine if you also want to use Google colab. You can **directly launch the notebooks in your Google colab environment by clicking on the links provided in the notebooks** (of course, that makes a copy of my notebook on to your Google drive).



> For more information about using **Google Colab** for your deep learning work, [check their FAQ here](https://research.google.com/colaboratory/faq.html).



---



## Utility modules



### Utility module for example notebooks

I created a utility function file called `DL_utils.py` in the `utils` directory under `Notebooks`. We use functions from this module whenever possible in the Jupyter notebooks.



You can download the module (raw Python file) from here: [DL-Utility-Module](https://raw.githubusercontent.com/tirthajyoti/Deep-learning-with-Python/master/Notebooks/utils/DL_utils.py)



### General-purpose regression module (for tabular dataset)

I also implemented a general-purpose trainer module (`NN_trainer.py`) for regression task with tabular datasets. The idea is that you can simply read a dataset (e.g. a CSV file), choose the input and target variables, build a densely-connected neural net, train, and predict. The module gives you back a prediction function (trained) which can be used for any further prediction, analytics, or optimization task. 



Check out the module [here](https://github.com/tirthajyoti/Deep-learning-with-Python/blob/master/utils/NN_trainer.py) and an example notebook [here](https://github.com/tirthajyoti/Deep-learning-with-Python/blob/master/Notebooks/Demo_general_purpose_regression_module.ipynb).



## Notebooks



### Deep learning vs. linear model

* We show a nonlinear function approximation task performed by linear model (polynomial degree) and a simple 1/2 hidden layer (densely connected) neural net to illustrate the difference and the capacity of deep neural nets to take advantage of larger datasets ([Here is the Notebook](https://github.com/tirthajyoti/Deep-learning-with-Python/blob/master/Notebooks/Function%20approximation%20by%20linear%20model%20and%20deep%20network.ipynb)).



### Demo of a general-purpose regression module

* We implemented a general-purpose trainer module for regression task with tabular datasets. The idea is that you can simply read a dataset (e.g. a CSV file), choose the input and target variables, build a densely-connected neural net, train, predict, and save the model for deployment. This the demo notebook for that module ([Here is the Notebook](https://github.com/tirthajyoti/Deep-learning-with-Python/blob/master/Notebooks/Demo_general_purpose_regression_module.ipynb)).



### Simple Conv Net

* [Fashion MNIST](https://github.com/zalandoresearch/fashion-mnist) image classification using densely connected network and 1/2/3 layer CNNs ([Here is the Notebook](https://github.com/tirthajyoti/Computer_vision/blob/master/Notebooks/Fashion_MNIST_using_CNN.ipynb)).



### Using Keras `ImageDataGenerator` and other utilities



* _Horse or human_ image classification using Keras `ImageDataGenerator` and **Google colaboratory** platform. ([Here is the Notebook](https://github.com/tirthajyoti/Computer_vision/blob/master/Notebooks/Horse_or_Human_with_ImageGenerator.ipynb))



* Classification on the [flowers dataset](https://www.kaggle.com/alxmamaev/flowers-recognition) and the famous [Caltech-101 dataset](http://www.vision.caltech.edu/Image_Datasets/Caltech101/) using `fit_generator` and `flow_from_directory()` method of the `ImageDataGenerator`. Illustrates how to streamline CNN model building from a single storage of image data using these utility methods. ([Here is the Notebook](https://github.com/tirthajyoti/Deep-learning-with-Python/blob/master/Notebooks/Keras_flow_from_directory.ipynb))



###  Transfer learning

* Simple illustration of [transfer learning](https://machinelearningmastery.com/transfer-learning-for-deep-learning/) using CIFAR-10 dataset ([Here is the Notebook](https://github.com/tirthajyoti/Deep-learning-with-Python/blob/master/Notebooks/Transfer_learning_CIFAR.ipynb))



* Transfer learning with the famous [Inception v3 model](https://www.analyticsvidhya.com/blog/2018/10/understanding-inception-network-from-scratch/) - building a classifier of pneumonia from chest X-ray images. ([Here is the Notebook](https://github.com/tirthajyoti/Deep-learning-with-Python/blob/master/Notebooks/Transfer-learning-InceptionV3.ipynb))



### Activation maps

* We illustrate how to show the activation maps of various layers in a deep CNN model with just a couple of lines of code using `Keract` library. ([Here is the Notebook](https://github.com/tirthajyoti/Deep-learning-with-Python/blob/master/Notebooks/Keract-activation.ipynb)) 



### Adding object-oriented programming style to deep learning workflow

* Adding simple [Object-oriented Programming (OOP)](https://realpython.com/python3-object-oriented-programming/) principle to your deep learning workflow ([Here is the Notebook](https://github.com/tirthajyoti/Computer_vision/blob/master/Notebooks/OOP_principle_deep_learning.ipynb)).



### Keras `Callbacks` using ResNet

* [ResNet](https://medium.com/@14prakash/understanding-and-implementing-architectures-of-resnet-and-resnext-for-state-of-the-art-image-cf51669e1624) on [CIFAR-10 dataset](https://www.cs.toronto.edu/~kriz/cifar.html), showing how to use Keras Callbacks classes like `ModelCheckpoint`, `LearningRateScheduler`, and `ReduceLROnPlateau`. You can also change a single parameter to generate ResNet of various depths. ([Here is the Notebook](https://github.com/tirthajyoti/Deep-learning-with-Python/blob/master/Notebooks/ResNet-on-CIFAR10.ipynb)).



### Simple RNN

* Time series prediction using simple RNN (a single RNN layer followed by a densely connected layer). We show that a complicated time-series signal is correctly predicted by a simple RNN even when trained with only 25% of the data. ([Here is the Notebook](https://github.com/tirthajyoti/Deep-learning-with-Python/blob/master/Notebooks/SimpleRNN-time-series.ipynb)) 



### Text generation using LSTM

* Automatic text generation (based on simple character vectors) using [LSTM network](https://colah.github.io/posts/2015-08-Understanding-LSTMs/). Play with character sequence length, LSTM architecture, and hyperparameters to generate synthetic texts based on a particular author's style! ([Here is the Notebook](https://github.com/tirthajyoti/Deep-learning-with-Python/blob/master/Notebooks/LSTM_text_gen_Dickens.ipynb)).



### Bi-directional LSTM for sentiment classification

* [Bi-directional LSTM with embedding](https://machinelearningmastery.com/develop-bidirectional-lstm-sequence-classification-python-keras/) applied to the IMDB sentiment classification task ([Here is the Notebook](https://github.com/tirthajyoti/Deep-learning-with-Python/blob/master/Notebooks/LSTM_bidirectional_IMDB_data.ipynb))



### Generative adversarial network (GAN)

* Simple demo of building a GAN model from scratch using a one-dimensional algebraic function ([Here is the Notebook](https://github.com/tirthajyoti/Deep-learning-with-Python/blob/master/Notebooks/GAN_1D.ipynb))



### Scikit-learn wrapper for Keras

* [Keras Scikit-learn wrapper](https://keras.io/scikit-learn-api/) example with 10-fold cross-validation and exhaustive grid search ([Here is the Notebook](https://github.com/tirthajyoti/Deep-learning-with-Python/blob/master/Notebooks/Keras_Scikit_Learn_wrapper.ipynb))