Deep Learning is an AI method that shows the power of AI hugely to the world and is a major reason why people are more interested in AI than ever before. In this article, I will explain the concepts of Deep Learning clearly and concisely.
What is a deep learning?
Deep Learning is the modern method that is used to achieve AI using artificial neural networks. These Artificial neural networks are inspired by the way the human brain processes information, and just like in a biological neural network, an artificial neural network has neurons that are interconnected and pass information through layers. Each layer performs simple operations on the data and selectively passes the results to other neurons. A basic artificial neural network consists of three layers, but when it comes to deep learning, it uses more than three layers to capture more deep features and patterns from the data. Those extra layers increase the depth of the neural network. That’s why we call this “Deep Learning”.
Usually, people consider Deep learning as a subset of machine learning But I like to point to Deep Learning as the upgraded version of machine learning which leads to a new generation of AI.
Why do we need deep learning?
Limitations of machine learning
While machine learning algorithms have shown remarkable capabilities in handling specific tasks, they often struggle with complex problems that require a deeper understanding of data. Traditional machine-learning approaches depend heavily on manual feature extraction which is a very time-consuming process and not a good method for capturing deep patterns and features.
How does deep learning overcome those limits?
By using neural networks, Deep Learning is able to automatically extract relevant features from the data. With the help of additional neural layers, it can extract deeper features from complex data, allowing it to identify complex patterns and relationships in the data. This allows deep learning to achieve higher prediction accuracy for complex data, resulting in more flexible and robust AI.
How does deep learning work?
Components of deep learning
Deep learning is using the neural network architecture. So you can see all the components of a neural network also in deep learning. Also, some additional components are used to control model accuracy and generalization in deep learning.
Neural Network Architecture
A basic neural network architecture consists of interconnected neurons organized in main 3 layers input layer, hidden layers, and output layer. But there is more than one hidden layer in deep learning which increases the depth of the neural network. The depth of deep learning refers to the number of hidden layers between the input and output layers. (There are four 4 hidden layers in the above image)
Input Layer:
The input layer is used to feed raw data into the deep-learning neural network.
Hidden Layer:
Hidden Layers are intermediate layers between the input and output layers in deep learning. They process the input data by applying mathematical operations and activation functions.
Output Layer:
The output layer produces the final result or prediction in a deep learning model. The structure of the output layer depends on the specific task, such as classification, regression, or generation.
Data
Deep learning neural networks learn from examples, and their accuracy highly depends on the amount of data available. Therefore, it’s crucial to collect as much data as possible and use tensors to store this information.
For example, If we are trying to build a deep learning model to predict bitcoin value using a dataset containing 10 data samples, each with two labeled data points: time and price. In this case, our input data can be represented in a tensor like this:
Weights & Bias
Neurons in the hidden and output layers receive training values known as weights and biases, which serve as the training parameters in neural networks. These parameters are essential for capturing patterns and features. The weight and bias values of neurons are stored using tensors.
Activation Function
The activation Function introduces non-linearity into the neural networks, allowing the neural network to learn complex relationships and patterns in input data.
Loss Function
The loss function quantifies the difference between the network’s predicted output and the true output.
Optimization Algorithms
Optimization algorithms determine how the network’s weights and biases are updated during training to minimize the loss function.
Hyperparameters
Hyperparameters are settings or configurations that are set prior to training the deep learning model and determine its behavior and performance. They are external to the model and are not learned during the training process.
Learning Rate – The learning rate determines the size of the step taken to minimize the loss function. This also affects the training speed.
Batch Size – The batch size determines the number of training examples used in each iteration of the optimization algorithm. It affects the memory requirements, computational efficiency, and the quality of the weight updates.
Number of Epochs – This determines the number of times the entire training dataset is passed through the model.
When building a deep learning model, we should choose suitable components for our model. Otherwise, it affects badly to model performance.
Steps in deep learning
- Data Gathering and Preprocessing: Our AI model’s accuracy highly depends on the Data so data gathering and preprocessing is a crucial step in deep learning. This step involves collecting data and performing preprocessing tasks such as data cleaning, and normalization.
- Designing Model Architecture: We should choose the appropriate architecture for the deep learning model based on the problem at hand and this step also includes determining the number of layers & neurons, hyperparameter values, the type of neural network(e.g., convolutional, recurrent), the activation functions, the optimization algorithm, and the connections between layers.
- Data Splitting: We should split the available data into training, validation, and testing sets. The training set is used to optimize the deep learning model’s parameters, the validation set is used to tune hyperparameters and monitor model performance, and the testing set is used to evaluate the deep learning model’s final performance. Sometimes, we only use training and testing data(training data are used for both model parameter optimization and hyperparameter tuning)
- Model Initialization: We Initialize the deep learning model’s weights and biases, using random or pre-trained values. Proper initialization is crucial for effective learning. Improper weight initialization can lead to training difficulties or convergence issues. Common weight initialization methods include random initialization(python random library) and Xavier/Glorot initialization. Nowadays, When we define the model in a Deep Learning Framework, they automatically initialize the needed parameters.
- Forward Propagation: Forward propagation is the process of passing the input data through the neural network to obtain the output prediction. This involves calculating the weighted sum of inputs(multiplying the input values with the weights of hidden and output layers), applying activation functions, and forwarding the output through the layers. Afterward, the model’s predictions are compared to the actual values in the training data, and a loss value is computed to quantify the disparity between them.
- Backpropagation: Backpropagation calculates the gradients of the loss function with respect to the weights and biases in the network, enabling the adjustment of these parameters to minimize the loss. In other words, backpropagation determines the necessary changes to each weight and bias in a neuron to minimize the loss values. Subsequently, optimization algorithms are applied to update the weights.
- Hyperparameter Tuning: We should experiment with various hyperparameter values to discover optimal solutions, and this process of hyperparameter tuning helps optimize the model’s performance. Hyperparameter tuning is frequently carried out using techniques such as grid search or random search.
- Generalization: Here we measure the performance of the trained deep learning model on unseen data, which offers an indication of the model’s capacity to generalize to new information. This assessment is used to determine whether the model should be deployed or if alternative models need to be considered.
It’s essential to keep in mind that deep learning training is an iterative process, and certain steps, such as hyperparameter tuning and architecture design, may require additional time to find the optimal solution. Additionally, the speed and efficiency of deep learning can greatly hinge on the availability of computational resources, such as GPUs or specialized hardware.
Major challenges & limitations in deep learning
Although deep learning has shown remarkable accuracy and performance in tasks, it is important to know that it has some limitations and challenges. Here are some essential points to consider:
- Data Requirements: Deep learning models require large amounts of labeled data to train the model effectively. However, obtaining and labeling such massive datasets can be time-consuming and expensive. Sometimes there are only limited data available to the given problem domain. This leads to the poor performance of the deep learning model and limits the accessibility of deep learning for certain applications.
- Hardware Requirements: Deep learning model training can be computationally demanding and requires high-performance hardware, such as GPUs. These hardware requirements can limit the accessibility of deep learning to organizations or individuals with sufficient resources.
- Interpretability: Due to a lack of interpretability, deep learning models are referred to as “black boxes”. Understanding why a deep learning model makes a particular prediction or decision can be challenging. This lack of transparency raises concerns in domains where interpretability is crucial, such as healthcare and finance.
- Overfitting: Deep learning models are prone to overfitting, where they become too specialized in the training data and fail to generalize to new data. Techniques like regularization and dropout are used to mitigate overfitting and improve model performance.
Applications of deep learning
Nowadays, Deep learning is rapidly involved in various industries, including healthcare, finance, manufacturing, retail, and more due to Its ability to do complex tasks very easily and efficiently. AI Bots and Tools like ChatGPT, DALEE, and Midjourney help a lot to improve our productivity and save time.
Deep learning is a powerful and revolutionary method for AI. Nowadays there are a lot of applications and tools of deep learning and they are revolutionizing our lives. With this high involvement of deep learning, we can see further advancements in many fields and applications.