crash course in deeplearning

source — http://adilmoujahid.com/posts/2016/06/introduction-deep-learning-python-caffe/

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The main difference between traditional machine learning and deep learning algorithms is in the feature engineering. In traditional machine learning algorithms, we need to hand-craft the features. By contrast, in deep learning algorithms feature engineering is done automatically by the algorithm. Feature engineering is difficult, time-consuming and requires domain expertise. The promise of deep learning is more accurate machine learning algorithms compared to traditional machine learning with less or no feature engineering.

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3. A Crash Course in Deep Learning

Deep learning refers to a class of artificial neural networks (ANNs) composed of many processing layers. ANNs existed for many decades, but attempts at training deep architectures of ANNs failed until Geoffrey Hinton’s breakthrough work of the mid-2000s. In addition to algorithmic innovations, the increase in computing capabilities using GPUs and the collection of larger datasets are all factors that helped in the recent surge of deep learning.

3.1. Artificial Neural Networks (ANNs)

Artificial neural networks (ANNs) are a family of machine learning models inspired by biological neural networks.

Artificial Neural Networks vs. Biological Neural Networks

Biological Neurons are the core components of the human brain. A neuron consists of a cell body, dendrites, and an axon. It processes and transmit information to other neurons by emitting electrical signals. Each neuron receives input signals from its dendrites and produces output signals along its axon. The axon branches out and connects via synapses to dendrites of other neurons.

A basic model for how the neurons work goes as follows: Each synapse has a strength that is learnable and control the strength of influence of one neuron on another. The dendrites carry the signals to the target neuron’s body where they get summed. If the final sum is above a certain threshold, the neuron get fired, sending a spike along its axon.[1]

Artificial neurons are inspired by biological neurons, and try to formulate the model explained above in a computational form. An artificial neuron has a finite number of inputs with weights associated to them, and an activation function (also called transfer function). The output of the neuron is the result of the activation function applied to the weighted sum of inputs. Artificial neurons are connected with each others to form artificial neural networks.

Feedforward Neural Networks

Feedforward Neural Networks are the simplest form of Artificial Neural Networks.

These networks have 3 types of layers: Input layer, hidden layer and output layer. In these networks, data moves from the input layer through the hidden nodes (if any) and to the output nodes.

Below is an example of a fully-connected feedforward neural network with 2 hidden layers. “Fully-connected” means that each node is connected to all the nodes in the next layer.

Note that, the number of hidden layers and their size are the only free parameters. The larger and deeper the hidden layers, the more complex patterns we can model in theory.

Activation Functions

Activation functions transform the weighted sum of inputs that goes into the artificial neurons. These functions should be non-linear to encode complex patterns of the data. The most popular activation functions are Sigmoid, Tanh and ReLU. ReLU is the most popular activation function in deep neural networks.

Training Artificial Neural Networks

The goal of the training phase is to learn the network’s weights. We need 2 elements to train an artificial neural network:

  • Training data: In the case of image classification, the training data is composed of images and the corresponding labels.
  • Loss function: A function that measures the inaccuracy of predictions.

Once we have the 2 elements above, we train the ANN using an algorithm called backpropagation together with gradient descent (or one of its derivatives). For a detailed explanation of backpropagation, I recommend this article.

3.2. Convolutional Neural Networks (CNNs or ConvNets)

Convolutional neural networks are a special type of feed-forward networks. These models are designed to emulate the behaviour of a visual cortex. CNNs perform very well on visual recognition tasks. CNNs have special layers called convolutional layers and pooling layers that allow the network to encode certain images properties.

Convolution Layer

This layer consists of a set of learnable filters that we slide over the image spatially, computing dot products between the entries of the filter and the input image. The filters should extend to the full depth of the input image. For example, if we want to apply a filter of size 5x5 to a colored image of size 32x32, then the filter should have depth 3 (5x5x3) to cover all 3 color channels (Red, Green, Blue) of the image. These filters will activate when they see same specific structure in the images.

Pooling Layer

Pooling is a form of non-linear down-sampling. The goal of the pooling layer is to progressively reduce the spatial size of the representation to reduce the amount of parameters and computation in the network, and hence to also control overfitting. There are several functions to implement pooling among which max pooling is the most common one. Pooling is often applied with filters of size 2x2 applied with a stride of 2 at every depth slice. A pooling layer of size 2x2 with stride of 2 shrinks the input image to a 1/4 of its original size. [2]

Convolutional Neural Networks Architecture

The simplest architecture of a convolutional neural networks starts with an input layer (images) followed by a sequence of convolutional layers and pooling layers, and ends with fully-connected layers. The convolutional layers are usually followed by one layer of ReLU activation functions.

The convolutional, pooling and ReLU layers act as learnable features extractors, while the fully connected layers acts as a machine learning classifier. Furthermore, the early layers of the network encode generic patterns of the images, while later layers encode the details patterns of the images.

Note that only the convolutional layers and fully-connected layers have weights. These weights are learned in the training phase.