The 3 Most Promising Deep Learning Training Methods

Deep learning is the subfield of machine learning which is comprised of different layers of neural networks. A neural network is the network of nodes and neurons, and the connections between them are referred to as weights. 

Deep learning involves going deep into various layers of a neural network, also referred to as hidden layers. The deeper the layers, the more complex information is learned. Deep learning is simply a neural network with more than three hidden layers. In short, machine learning is the subset of artificial intelligence. Deep learning is the subset of machine learning, and neural networks are the backbones of deep learning.

Training a deep learning network is not an easy process. Generally, the process used to train a deep neural network involves forward propagation of inputs, backward propagation of weights and bias, and optimization and gradient descent. 

But, the three most promising deep learning training methods include normalization techniques, transfer learning, optimization, and gradient descent.

Batch Normalization

Normalization is when the dataset is transformed into the same scale so that each feature becomes equally important. Consider an example of a simple neural network with small input features: number of family members (x) and yearly income (y) such that 1<x<10 and 5000<y<100000. Since the range of the two features differs, the feature “income” will dominantly influence the model as it has a greater value. 

Extending the concept of normalization—batch normalization is a normalization technique in which the output of each neuron in each hidden layer is normalized according to the samples in a mini-batch. The main idea behind a batch normalization technique is to reduce the internal covariate shift. When the data is not normalized, the neural networks become unstable and take longer to converge. Thus, batch normalization is an essential process during deep learning model development.

Transfer Learning

Transfer Learning is the transfer of knowledge gained by one network to another. When a deep learning model is trained, it learns weights and biases. Thus, it is simply the transfer of weights and biases learned by a neural network to another. Transfer learning has gained popularity because it can train deep neural networks with less data than model learning from scratch. 

It is widely popular in Computer Vision (CV), Natural Language Processing (NLP), and other tasks requiring significant time and resources. The data generalization that another network has already learned provides us a huge leap in time and resources. Consider we want to classify vans and already have a pre-trained model of buses classifier. 

In such situations, we can use our vans classifier on top of our buses classifier. Since the vans and the buses share similar features like wheels, shapes, etc., we can use a pre-trained model as a feature extractor. This is just one notable and important approach to training deep learning models.

Gradient Descent

Gradient Descent is an algorithm used to minimize the loss of a neural network by updating weights and biases. Think of an empty bowl. If we put a small ball and slide it from the round edge into the bowl, the ball oscillates and ultimately settles at the lowest point on the bowl. 

Gradient descent bears a similar concept. The process of reaching a minimum point of loss is referred to as gradient descent. Consider a neural network whose weight and bias at the current moment are W and b. E is the error or loss function, and η is the learning rate of the neural network. 

A learning rate is a parameter that controls the rate of learning. A learning rate does not have to be constant throughout training. Using methods like PyTorch learning rate scheduler, the learning rate can also be varied while training deep neural networks. 

Consider the same example of the bowl and the ball; a learning rate can be considered as the speed at which the ball is slid inside the bowl. If thrown rapidly, the ball oscillates to and fro and takes a longer period to settle at the minimum point. 

In the case of real neural networks, if the learning rate is too low, the minimum point of loss might not even be reached. So, the learning rate should be just appropriate, which can be determined using hyperparameter tuning.

The weights and biases in the gradient descent process are updated using the following formula.

wwηdE/dw

Without gradient descent, the weights cannot be updated, and the minimum point of loss can never be reached. Therefore, gradient descent is one promising training approach. 

Three Training Methods

Training deep learning models can be challenging, but batch normalization, transfer learning, and gradient descent are three of the most promising and effective deep learning training methods. They stabilize the model training, optimize the use of time and resources, and minimize the loss of a neural network.

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