Training materials for deep learning

List of resources and use cases

Caffe

- Intro to caffe
- 00_MNIST_linear
- 01_MNIST_full_connect
- 02_MNIST_Lenet

sklearn (scikit learn examples)

- Basic classification model
- Basic Regression model
- Control overfit and hyperparameter optimization
- Text classification example

Tensorflow

- 00-Intro_to_keras
- 00-Intro_to_tensorflow
- 01-tensorboard_example (based on https://gist.github.com/dandelionmane/4f02ab8f1451e276fea1f165a20336f1#file-mnist-py)
- 02-text
    - 01-char_languaje_model
    - 02-sentiment_model
    - 03-word_tagging
    - 20newsgroups_keras_model
- 03-image
    - mnist_data_augmentation
    - cifar10_basic_architecture
    - cifar10_resnet
    - transfer_learning examples
    - object_detection
- 04-time_series
    - Basic example
    - CIF dataset example
- 05-others

Tensorflow_old (version 1.0 keras not integrated)

- 00-Intro_to_tensorflow
- 01-Template_session
- 02-template_class
- 03-text_use_cases
- 04-image_use_cases
- 05-others

Torch examples (work in progress)

- Torch basics 

List of deep learning courses

Deep learning course of Google in Udacity https://www.udacity.com/course/deep-learning--ud730

Deep learning course of fast.ai http://course.fast.ai/

Stanford course Deep learning for Natural languaje processing http://web.stanford.edu/class/cs224n/

Stanford course Convolutional Neural Networks for Visual Recognition http://cs231n.stanford.edu/

Machine learning course of Stanford in Coursera https://www.coursera.org/learn/machine-learning

Anaconda environment

1.- Install anaconda3 version 5. All default options.

2.- Start an Anaconda terminal and execute...

# Install jupyter extensions 
conda install anaconda-nb-extensions -c nb-conda

# Create environment and install deep learning packages
conda create -n tf12 python=3.5
activate tf12

conda install graphviz
conda install pandas scikit-learn
conda install -c anaconda jupyter 
conda install matplotlib
conda install pillow 
pip install h5py
pip install pydot-ng
pip install --upgrade https://storage.googleapis.com/tensorflow/windows/cpu/tensorflow-1.2.0-cp35-cp35m-win_amd64.whl
pip install keras