Programming/Deep Learning
Contents
Deep Learning
Introduction
Deep learning (also known as deep structured learning or hierarchical learning) is part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms. Learning can be supervised, semi-supervised or unsupervised.
There is a massive amount of possible applications where Deep Learning can be deployed, these include:
- Automatic speech recognition
- Image recognition
- Visual art processing
- Natural language processing
- Drug discovery and toxicology
- Customer relationship management
- Recommendation systems
- Bioinformatics
- Health diagnostics
- Image restoration
- Financial fraud detection
There are 6 Types of Artificial Neural Networks Currently Being:
- Recurrent Neural Network(RNN) – Long Short Term Memory
- Convolutional Neural Network
- Feedforward Neural Network – Artificial Neuron
- Radial basis function Neural Network
- Kohonen Self Organizing Neural Network
- Modular Neural Network
The top two are the most used.
Development Environments
There are the following development environments already part of our HPC
- Python 3.5 with Tensorflow (and Keras), and theano.
- C/C++/Fortran with CUDA GPU programming.
- PGI compiler with openACC programming for C and Fortran.
- Matlab with deep learning libraries.
Deep Learning example
This represents the "Hello World" of the deep learning programs, and the mnist data set represents a large amount of hand written numbers (28 x 28 pixels) with labels. This program trains the neural network as training data and then uses that neural network to test data against the data set.
#!/usr/bin/env python
import tensorflow as tf
mnist = tf.keras.datasets.mnist
(x_train, y_train),(x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(),
tf.keras.layers.Dense(512, activation=tf.nn.relu),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(10, activation=tf.nn.softmax)
])
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics=['accuracy'])
model.fit(x_train, y_train, epochs=5)
model.evaluate(x_test, y_test)
Further Information
- https://www.tensorflow.org/tutorials/
- https://keras.io/
- https://www.nvidia.com/en-gb/deep-learning-ai/education/
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