Hands-on Neural Network
A collections of resources to understand Neural Network.
Getting Started
Prerequisites
Install conda https://docs.anaconda.com/anaconda/install/ for python 3.7.
Make sure you have added theforge channel:
conda config --append channels conda-forge
Then in the projec directory run:
conda create --name dl_env python=3.7 --file enviroment.yaml
This will create our conda enviroment.
If conda does not find all the packages please add :
conda config --env --add channels conda-forge
Slides
Slides day 1: https://drive.google.com/drive/folders/1d2KK9VzWeueTnDu3x6mddWFZFF8JQrvG?usp=sharing Slides day 2: https://drive.google.com/drive/u/1/folders/12WLWF5XDUJok5bvQqAXt2ic5e-0wTe-W
Run
To activate the enviroment you can run in a shell
conda activate dl_env
To run tensorboard simply type:
tensorboard --logdir logs/1
To see if an environment is already running:
$ conda info --envs
# conda environments:
#
base * /Users/nickager/anaconda3
dl_env /Users/nickager/anaconda3/envs/dl_env
$ conda activate dl_env
Running Jupyter notebook
(dl_env) $ jupyter notebook
Contributing
Feel free to contribute with a pull request.
See also
- Markov chains
- https://victorzhou.com/blog/intro-to-random-forests/
- https://github.com/ipython-contrib/jupyter_contrib_nbextensions
- https://colab.research.google.com/notebooks/basic_features_overview.ipynb#scrollTo=4hfV37gxpP_c
- https://en.wikipedia.org/wiki/Scale-invariant_feature_transform#Competing_methods
- https://www.meetup.com/London-Reinforcement-Learning/
- https://www.meetup.com/London-Reinforcement-Learning/members/?op=leaders
- https://www.meetup.com/LondonArtificialIntelligence/
- https://towardsdatascience.com/googles-adanet-uses-neural-networks-to-build-better-neural-networks-36c07eefd4d3
- https://realpython.com/python-type-checking/
- https://en.wikipedia.org/wiki/MNIST_database
- https://www.nist.gov/itl/iad/image-group/emnist-dataset
- https://deepmind.com/research/open-source/
- https://github.com/deepmind?page=2
- https://github.com/deepmind/lab
- https://www.openmined.org
- https://github.com/OpenMined/PySyft/tree/master/examples/tutorials
- https://kogence.com/app/
- https://play.battlesnake.io
- https://blog.jle.im/entries/series/+functional-models.html - trainable models with Haskell
- Awesome Haskell Deep Learning
- https://mmhaskell.com/haskell-ai
- Machine Learning: The High-Interest Credit Card of Technical Debt