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Deep Learning with Python

Collection of deep-learning code examples, tutorial-style Jupyter notebooks, and projects.

Many of the Jupyter notebooks are built on Google Colab 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).

Authored and maintained by Dr. Tirthajyoti Sarkar (Website, LinkedIn profile)

Here is the Github Repo.


Requirements

NOTE: Most of the Jupyter notebooks in this repo are built on Google Colaboratory using Google GPU cluster 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.


Utility function

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

Notebooks

Deep learning vs. linear model

Simple Conv Net

Using Keras ImageDataGenerator and other utilities

Transfer learning

Activation maps

Adding object-oriented programming style to deep learning workflow

Keras Callbacks using ResNet

Simple RNN

Text generation using LSTM

Bi-directional LSTM for sentiment classification

Generative adversarial network (GAN)

Scikit-learn wrapper for Keras