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Deep learning has revolutionized the field of artificial intelligence and data science, enabling us to tackle complex problems in various domains. One of the key techniques within deep learning is transfer learning, which allows us to leverage pre-trained models to solve new tasks more efficiently.
But first what is deep learning?
It is basically 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 range of fields, including robotics, computer vision, natural language processing, image recognition, and more. Deep learning models excel at tasks such as 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, such as predicting customer churn, classifying emails as spam or non-spam, and determining whether a bank loan will default or not.
Through classification, we can make accurate predictions and decisions based on input data.
Computer Vision
Computer vision plays a crucial role in 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 common to represent categorical labels as one-hot encoded vectors. One hot encoding is a technique used to convert categorical variables into a binary vector representation. Each category is assigned a unique index, and the corresponding element in the one-hot encoded vector is set to 1, while all other elements are set to 0.
This representation allows the neural network to understand the distinct categories and make predictions accordingly.
Neural network
A neural network classifier is made of several layers of neurons.
Keras is a high-level neural networks API, written in Python, and can run on top of TensorFlow,
Neurons , Weights and Biases
A “neuron” computes a weighted sum of all of its inputs, adds a value called “bias” and feeds the result through a so called “activation function”. The weights and bias are unknown at first. They will be initialized at random and “learned” by training the neural network on lots of known data.
Dense Neural Network
This is the simplest neural network for classifying images. It is made of “neurons” arranged in layers. The first layer processes input data and feeds its outputs into other layers. It is called “dense” because each neuron is connected to all the neurons in the previous layer.
You can feed an image into such a network by flattening the RGB values of all of its pixels into a long vector and using it as inputs.
Activation function (Softmax)
Activation functions play a crucial 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 same number of neurons as the classes to predict the probabilities for each class. Cross-entropy loss is commonly used in classification tasks to compare the predicted probabilities with the actual one-hot encoded labels.
Cross-entropy loss
For classification, cross-entropy is 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 minimize the loss and improve the accuracy of the model, we employ optimization algorithms such as 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 due to their efficiency and ability to converge to better solutions. Training on batches of data, known as mini-batching, further enhances the optimization process.
Transfer Learning
Transfer learning is a technique that leverages the knowledge acquired from pre-trained models on one task to improve the performance of a related task. Instead of training a deep learning model from scratch, we can use a pre-trained model as a starting point and fine-tune it for our specific problem. Transfer learning offers several advantages, including reduced training time, improved generalization, and the ability to achieve good results even with limited labeled data.
Transfer Learning in Keras
Demo:
In this guide, we have built Classification models using the deep learning framework, Keras.