Deep learning has revolutionized the sphere of artificial intelligence and data science, enabling us to tackle complex problems in various domains. Certainly one of the important thing techniques inside deep learning is transfer learning, which allows us to leverage pre-trained models to unravel latest tasks more efficiently.

But first what’s deep learning?

It is essentially a branch of machine learning that focuses on training neural networks with multiple layers to extract patterns and representations from data.

Its applications span across a wide selection of fields, including robotics, computer vision, natural language processing, image recognition, and more. Deep learning models excel at tasks similar to classification, where the goal is to predict a categorical label based on input features.

Deep Learning 101 Concepts

Classification is a supervised machine learning algorithm used to predict categorical labels. It finds applications in various real-world scenarios, similar to predicting customer churn, classifying emails as spam or non-spam, and determining whether a bank loan will default or not.

Through classification, we are able to make accurate predictions and decisions based on input data.

Computer Vision

Computer vision plays a vital role within the implementation of deep learning classification models. Computer vision techniques are employed to preprocess and extract meaningful features from images.

One Hot Encoding

In classification tasks, it is not uncommon to represent categorical labels as one-hot encoded vectors. One hot encoding is a method used to convert categorical variables right into a binary vector representation. Each category is assigned a singular index, and the corresponding element within the one-hot encoded vector is ready to 1, while all other elements are set to 0.

This representation allows the neural network to know the distinct categories and make predictions accordingly.

Neural network

Neural networks are the constructing blocks of deep learning models, including classifiers. A neural network classifier consists of multiple layers of interconnected neurons. Each neuron performs a computation by taking the weighted sum of its inputs, adding a bias value, and passing the result through an activation function.

These weights and biases are initially unknown and randomly initialized. Through the training process, the neural network learns to regulate these weights and biases by analyzing large amounts of labeled data.

Dense Neural Network

That is the only neural network for classifying images. It’s product of “neurons” arranged in layers. The primary layer processes input data and feeds its outputs into other layers. It is named “dense” because each neuron is connected to all of the neurons within the previous layer.

You possibly can feed a picture into such a network by flattening the RGB values of all of its pixels into an extended vector and using it as inputs.

Activation function (Softmax)

Activation functions play a vital role in neural networks by introducing non-linearity and enabling the model to capture complex patterns. Common activation functions include relu (rectified linear unit) and softmax. The last layer of a classification model typically uses softmax activation with the identical variety of neurons because the classes to predict the chances for every class. Cross-entropy loss is often utilized in classification tasks to check the anticipated probabilities with the actual one-hot encoded labels.

Cross-entropy loss

For classification, cross-entropy is essentially the most commonly used loss function, comparing the one-hot encoded labels (i.e. correct answers) with probabilities predicted by the neural network.

Reducing Loss

To reduce the loss and improve the accuracy of the model, we employ optimization algorithms similar to gradient descent. Gradient descent adjusts the weights and biases of the neural network based on the gradients of the loss function. Optimizers like AdamOptimizer with momentum are commonly used attributable to their efficiency and talent to converge to higher solutions. Training on batches of knowledge, referred to as mini-batching, further enhances the optimization process.

Transfer Learning

Transfer learning is a method that leverages the knowledge acquired from pre-trained models on one task to enhance the performance of a related task. As a substitute of coaching a deep learning model from scratch, we are able to use a pre-trained model as a start line and fine-tune it for our specific problem. Transfer learning offers several benefits, including reduced training time, improved generalization, and the flexibility to realize good results even with limited labeled data.

Implementing Transfer Learning in Keras

Demo:

On this guide, now we have built Classification models using the deep learning framework, Keras

By incorporating pre-trained models into our pipeline, we are able to profit from their learned representations and expedite the training process.