Deep Learning Explained

How do applications generate human-like dialogue? How do applications predict and make accurate decisions in terms of stock market trends? We cover all of these topics in this tutorial.

This beginner’s guide will teach you everything you need to learn about deep learning. It does not matter if you are looking to learn more about data science, are an avid tech enthusiast, or are a business looking to better understand how to use the most current form of AI in order to better reach goals.

What is Deep Learning? A Complete Overview

Deep learning is a branch of machine learning (ML) and of artificial intelligence (AI) that models the structure and function of the brain. In traditional ML we see algorithms that are based on rules that are designed by us; also, we determine the features. In deep learning what we do instead is to use layered neural networks. Also in this approach the computer is able to learn from large sets of unstructured data.

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Deep Learning vs. Traditional Machine Learning

Some Basics First: Traditional Machine Learning vs. Deep Learning for Beginners.

Feature extraction in traditional machine learning: This is done by human experts. First, relevant features for the task at hand have to be identified. Next, these features are extracted from the raw data and then used as input for a machine learning algorithm. Examples for feature extraction for machine learning from raw data are, for example, feature extraction from images or audio files and feature extraction from text. Instead of feature extraction from raw data for deep learning, features are learned automatically from raw input data, such as images, audio files or raw text. These features are also referred to as representations and are usually hierarchical in nature.

Data Scalability: Unlike traditional learning methods that are limited in performance as soon as the amount of data becomes too large, Deep Learning methods get better with higher amounts of training data (up to millions or even billions of samples).
Computational Demands: Deep learning neural networks are designed to perform billions of matrix operations, which require highly specialized compute hardware such as GPUs or TPUs to train a network efficiently.

Deep Learning for Beginners: How Neural Networks Work

But Deep Learning for Beginners isn’t really a complicated set of programs. Instead, there are some simple fundamental concepts to get familiar with.

Deep learning for beginners explained: How neural networks work!

The Anatomy of a Deep Neural Network

A deep neural network is made up of three layers: input layer, hidden layers, and output layer.

Input Layer: In a neural network the ! Layer is the input layer and in the ! Layer every node represents a feature of the input data, like pixels in an image, or word tokens in a sentence.

Hidden Layers. A hidden layer is typically a mathematical cluster, where a layer of neurons creates more complex representations of data as you progress through the layers of the neural network. In the beginning, early layers of neurons detect simple lines and edges, while the neurons in the deeper layers can detect more complex patterns, like a face or even complete objects.

Output Layer: This layer will hold the output from the network which can be a continuous value in a regression problem, a single binary value to indicate yes/no in a classification problem, or a value for each possible class in a problem where there are thousands of classes and each class has a probability associated with it.
Key Mechanics: Weights, Biases, and Activation Functions
Artificial Neurons and Their Predicitions.
Weights ($w$): These are the numbers on the lines connecting neurons (in the one-layer network in this discussion, a single artificial neuron) together.
Bias ($b$): The bias or the activation threshold of the node is shifted left or right by the bias added to the weighted sum.
Activation Function: Non-linear mathematical function for the output of the weighted sum to be input for subsequent neuron(s) in the neural network. As we previously stated, without the activation function, the previous function would simply map out to a linear equation and thus would not be able to learn complex non-linear relationships of input data.

Common Activation Functions:

The activation function of hidden layers can be a simple function that is fast to compute. The Rectified Linear Unit (ReLU) function is used for the hidden layers in most deep learning models. The ReLU function returns the maximum between 0 and the input.

Sigmoid: The Sigmoid function outputs a value between 0 and 1 for a given input.

This function is typically used for the output layer in a binary classification problem. It is used to model the probability of occurrence of an event.

Softmax: This is used for the output layer in multi-class, soft classification. It converts the raw output scores into a normalized probability distribution over all classes.

Statistics for Deep Learning Explained: The Math Under the Hood

Having a good resource to learn statistics for deep learning explained is important to mastering neural networks. Underlying every successful deep learning model is a good amount of linear algebra, calculus, probability theory and statistical inference.

Linear Algebra: Tensors, Matrices, and Vectors

Deep learning can be reduced to the task of doing matrix multiplication on a very large scale. Now that we have introduced the main components of a neural network, we can move on to explain how the information for a sample in deep learning models is stored as a multi-dimensional array called a tensor:

A scalar is a zero-dimensional tensor (a single number).

A vector is a one-dimensional tensor. It is represented as a sequence of numbers.

A matrix is a 2D tensor.

A matrix is a 2D tensor. This can represent a table of numbers.

A 3D/nD tensor is a multi-dimensional array, such as a color image, which would be represented as height $\times$ width $\times$ RGB channels for a given number of images.

Forward Propagation and Loss Functions

As information travels through each layer of a neural network in forward propagation, it makes a prediction. The prediction of the neural network can be very close to the true values or completely wrong. In order to check the error of the neural network's prediction, we use a loss function.

Mean Squared Error (MSE): Mean Squared Error (MSE) is often used in regression to measure the average squared difference between predicted values and true values. The formula for the mean squared error is:

$$MSE = \frac{1}{n} \sum_{i=1}^{n} (y_i - \hat{y}_i)^2$$

Cross-Entropy Loss: The cross-entropy loss is used to measure the difference between a true distribution and an estimated distribution. It is often used for classification. It is calculated by summing over all classes and then computing the product of the true distribution for a class and the natural logarithm of the estimated distribution for that class.

$$L = -\sum_{i} y_i \log(\hat{y}_i)$$

Gradient Descent and Backpropagation

To optimize the weights and biases of a model, the loss function must be minimized. The loss function measures how well a model does on a set of examples.

Backpropagation: In backpropagation, we use the chain rule to calculate the partial derivatives of the loss for all the weights and biases in the network.We do this by propagating the errors backwards from the output layer.

Gradient Descent: A way to find the local minimum of a function (the loss curve of the network) by iteratively increasing or decreasing the parameters of the network in the opposite direction of the gradient. How big each step is, is controlled by the hyperparameter Learning Rate ($\alpha$).

Loss

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| \ <- Starting Point (High Loss)

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| \ Step by Step adjustment via Gradient Descent

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| * Minimum Loss (Optimal Weights)

+----------------------------------------------------> Weights (w)

Probability and Regularization: Overfitting vs. Underfitting

In statistical learning, models face the bias-variance tradeoff:

Underfitting (High Bias): This is when your network has not enough complexity to capture the most important features of your data.

Overfitting (High Variance): When a network in training memorizes noise in training data and gets very high scores for the training cases but scores low for new cases in validation tests. The network is highly varied in its predictions and poor at trying to make predictions for new cases.

To mitigate overfitting, statistical regularization techniques are applied:

Dropout: In each iteration, randomly picks a number of neurons in the networks to not update during the gradient descent for that iteration (to prevent overfitting). This forces the neural network to learn representations that are redundant to the specific ones chosen to not update in that iteration.

L1 & L2 Regularization: Add a penalty term to the loss function, based on the magnitude of the network weights, in order to prevent individual weights from becoming too large.

Batch Normalization: This is used to normalize the inputs to each layer in a training batch. It has been found to greatly stabilize the learning dynamics and to greatly increase the rate of convergence to the minimum loss.

Deep Learning Tutorial: A Step-by-Step Guide to Building a Model

In summary, the best way to learn about deep learning is by practicing it by building, training and evaluating a model of a neural network. We use popular deep learning libraries for Python, i.e. TensorFlow and Keras.

1. Problem Statement & Data Collection for Deep Learning Model: High-performing deep learning models are often built upon solid problems with relevant data.

Identify whether your task is classification, regression, or generative. In the following tutorial, we will use a classification problem to illustrate the deep learning workflow. Gather a good amount of high-quality data for your task. You can have raw images or structured data. Now we separate the data to be used for training, validation, and testing.

2. Data Preprocessing & Normalization: Ensures stable model training.

Normalization refers to scaling feature values within a range of numbers to enable stable model training by gradients. After cleaning of missing values and anomalies of extreme values, all features need to be scaled, in order to ensure stability of model training by gradients (e.g. pixel intensities of images as integers between 0 and 255 need to be scaled to 0.0 … 1.0).

3. Design Neural Network Architecture: Choose an optimizer (e.g., Adam or SGD).

Set an appropriate loss function (e.g., categorical cross-entropy).

An appropriate architecture has to be chosen depending on the type of data that is given. For each problem a corresponding architecture is defined. This architecture consists of an input layer of corresponding size, of hidden layers (of arbitrary depth) with corresponding activation functions, and of an output layer with an appropriate activation function for the specific problem (e.g., Sigmoid for binary problems, Softmax for multi-class problems).

4. Compile the Model: Configure the optimizer, loss function, and metrics.

Set an appropriate loss function (e.g., categorical cross-entropy).

Define evaluation metrics (e.g., accuracy, precision, or F1-score).

5. Train and Evaluate the Model: During the training of the model, several metrics will be

displayed for each epoch, in order to monitor and verify the correct functioning of the model.

Monitor the performance of the model for each epoch to check if it is learning correctly.

validation data set. This model can be regularized (e.g., by using early stopping) when the loss on the validation data set stops decreasing.

Python Code Example: Image Classifier with Keras

Below is a simple example of building a basic feedforward neural network using Keras in Python:

Python

import tensorflow as tf

from tensorflow. keras import layers, models

# 1. Load sample dataset (MNIST hand-written digits)

mnist = tf.keras.datasets.mnist

(X_train, y_train), (X_test, y_test) = mnist.load_data()

# 2. Normalize pixel values between 0.0 and 1.0

X_train, X_test = X_train / 255.0, X_test / 255.0

# 3. Build sequential model architecture

model = models.Sequential([

layers.Flatten(input_shape=(28, 28)), # Input layer: 784 nodes

layers.Dense(128, activation='relu'), # Hidden layer 1

layers.Dropout(0.2), # Regularization layer

layers.Dense(10, activation='softmax') # Output layer: 10 classes

])

# 4. Compile model with Adam optimizer and Cross-Entropy loss

model.compile(

optimizer='adam',

loss='sparse_categorical_crossentropy',

metrics=['accuracy']

)

# 5. Train the model

model.fit(X_train, y_train, epochs=5, validation_split=0.1)

# 6. Evaluate accuracy on test set

test_loss, test_acc = model.evaluate(X_test, y_test, verbose=2)

print(f"\nTest Dataset Accuracy: {test_acc * 100:.2f}%")

Key Types of Deep Learning Architectures

There are numerous architectures, or ways of structuring a neural network, for deep learning, each best suited to a particular data structure.

1. Convolutional Neural Networks (CNNs)

As stated before, Convolutional Neural Networks (CNNs) are special kind of deep learning models, that are optimized for grid-like data, like images, and even video.

Their most basic layers are the so-called convolutional layers. They apply filters (which are called kernels in some countries) to each patch of the image, sliding them over the entire input (where patches might even be overlapping). The spatial structure of features is thereby preserved.

2. Recurrent Neural Networks (RNNs) & LSTMs

Another way of organizing a deep learning model is by using a recurrent neural network (RNN) that can process time-series data.

In a traditional feedforward neural network each input sample is processed independently. However, by including a memory loop in the neural network to store information from previous time steps, the RNN is able to process information from a sequence of steps.

3. Transformers

In the recent years, Transformer-based architectures have taken the center stage in Natural Language Processing (NLP) as well as in the realms of Generative AI. In essence, Transformer architecture replaces the traditional recurrent layers (with their inherent processing of sequences in a sequential manner) with Self-Attention Mechanisms. Subsequently, this architecture can process a sequence of elements (words, in particular) in its entirety (simultaneously), in order to generate a meaningful representation of the input.

4. Generative Adversarial Networks (GANs)

Generative Adversarial Networks (GANs) are typically comprised of two neural networks that are pitted against each other in a game of competition. The first network, the Generator, can attempt to create synthetic data that looks as real as possible. The second network, the Discriminator, can then try to identify whether or not a given piece of data was real or was it created by the Generator through fake data generation. Through competition, the Generator learns how to create the most realistic synthetic images, create audio or even paintings and other forms of visual art and then print them out.

Real-World Deep Learning Applications

Deep learning can be applied to many industries to solve problems in ways that can bring great change to the economy. Here are some of the applications and uses of deep learning in a variety of industries around the world.

Healthcare & Biomedical Imaging

Disease Diagnosis: Using X-ray, CT and MRI images to detect early stage cancer, blindness causing diseases and cardiovascular diseases with high accuracy.
Drug Discovery: The Deep Learning models can predict the molecular interactions and predict the protein folding structures to aid in the pharmaceutical research and development to find cures for diseases in a fraction of the time and cost it would take to perform the experiments in a laboratory.

Autonomous Systems & Transportation

Self-Driving Vehicles: This set of autonomous driving platforms utilizes a combination of models for computer vision, LiDAR processing networks and sensing and learning from data from various sensors such as cameras, lidars, radar to detect and react to pedestrian motion, and to identify and trace lanes, traffic signs, and other road hazard paths which change dynamically.

Natural Language Processing & Voice Assistants

Conversational AI: Transformers and sequence architectures power virtual assistants, instant automated language translation services, real-time speech-to-text conversion, and advanced sentiment analysis engines.

Finance & Fraud Detection

Algorithmic Trading & Risk Assessment: Deep learning is increasingly used on financial platforms to analyze historical data streams and forecast equity prices as well as to assess the probability of default for credits and to monitor ongoing transactions for signs of fraud in real-time.

eCommerce & Personalization

Recommendation Engines: These media streaming platforms and online commerce marketplaces use deep collaborative filtering (DCF) networks (e.g., CNNs, RNNs) of various sizes to build massive Recommendation Engines, Recommendation Systems, or Content Recommendation Systems which recommend customized content, products, services and even TV channels and also movies to individual customers, users and clients on the basis of their past actions.

Current Challenges and the Future of Deep Learning

Deep learning faces several important operational challenges, even though it is already being successfully applied today.

Data Availability & Labeling Costs: The big amounts of data required to train large deep learning models are often expensive to obtain and require a lot of manual work for labeling.

Interpretability (or “The Black Box Problem”): There are millions of parameters in a deep network, making it very hard to explain a decision that was made by such a model. This is a huge challenge, especially for applications in fields like medicine, law, and high-risk finance.

High Computational and Energy Costs: Training of current state-of-the-art transformer models for deep learning is computationally expensive for single machines, and scales exponentially with the number of parameters in the model, often requiring a large number. This makes training such models expensive for hardware, and also creates large energy costs to run such clusters of GPUs for extended periods of time.

Neuro-Symbolic AI, Few-Shot Learning and Quantum Neural Networks are the upcoming paradigms, that promise to tackle limitations of current models and be more efficient, interpretable and require less data for training.

Frequently Asked Questions (FAQs)

1. What is the main difference between Machine Learning and Deep Learning?

Machine Learning in general are algorithms that run on structured data to learn about that data, typically with the involvement of domain experts in feature engineering for these models. Deep Learning, a subset of the Machine Learning universe, utilizes multi-layered neural networks, that so called ‘learn’ to automatically extract features from very large amounts of raw data (such as images, text, audio).

2. Do I need advanced mathematical skills to learn deep learning?

Most of the Math you need to get started with Deep Learning is quite basic, and it’s worth noting that most libraries today handle the heavy Math for you in the background, such as Keras and PyTorch. This way, you can first learn the basics of how to apply Deep Learning, before you dive into the details and math of it all.

3. Which programming language is best for Deep Learning?

Python is currently the best language for learning Deep Learning. As the most popular language for data science, Python has a number of powerful libraries for Deep Learning, such as TensorFlow, PyTorch, Keras, NumPy, and Scikit-Learn.

4. What hardware is required to train deep learning models?

Small models can run on a CPU. However, training deeper neural networks efficiently requires GPUs (Graphics Processing Units) or TPUs (Tensor Processing Units) that can handle the massive amounts of parallel matrix math. There are many cloud platforms, including Google Colab, Kaggle Kernels, and cloud platforms such as AWS and Azure that provide the ability to learn and run Deep Learning models on GPU hardware.

5. How long does it take to learn Deep Learning for beginners?

Learning Deep Learning is relatively quick and requires about 3 to6 months of studying and practical work, if the learner already has basic knowledge of Python programming and statistics.