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Introduction to Neural Networks
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Welcome, class! Today we'll be learning about Neural Networks. Think of them as computational models inspired by how our brains work. Can anyone tell me what the basic components of a neural network are?
Are they like little neurons?
Exactly! Each neuron, or perceptron, takes inputs through connections that have weights and biases. These are structured in layers: input, hidden, and output. Its easy to remember that as I H-O-P; Input, Hidden, Output Layer. Now, what can you tell me about these weights?
The weights adjust as the network learns, right?
Correct! Theyβre updated during training to minimize error. Now, who can explain what happens during forward propagation?
Itβs when the input data goes through the network to produce an output?
Well done! Forward propagation is essential for making predictions. In summary, neural networks mimic the human brain, using layers of perceptrons to process and learn from data.
Activation Functions
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Now, letβs dive into activation functions. Who knows why they are important in a neural network?
They introduce non-linearity to the model, right?
Exactly! When we have layers stacked up, without activation functions, the model would just be a linear transformation, which isnβt useful for complex problems. Let's remember: S-T-R for Sigmoid, Tanh, and ReLU. Can anyone describe the Sigmoid function?
It squashes input values to be between 0 and 1.
Very good! This can be especially useful for binary classification. Can someone explain how Tanh is different?
It outputs values between -1 and 1.
Right! Tanh centers everything around zero which can help with convergence. In summary, activation functions are crucial to enable the model to learn complex relationships.
Training Deep Networks
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Next, letβs explore how we train our networks. What is one of the main algorithms we use?
Backpropagation, right?
You got it! Backpropagation calculates gradients of the loss function in order to adjust weights. Can anyone explain why we use gradient descent?
To minimize the loss function!
Absolutely! There are different variants like batch and stochastic gradient descent. Remember B-S-G for Batch, Stochastic, and Gradients. What challenges do we face during training?
Vanishing gradients and overfitting?
Exactly! These are common issues that we need to address to train effective models. Great job summarizing! Training involves adjusting weights through methods like backpropagation and managing challenges with regularization techniques.
Applications of Deep Learning
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Finally, let's talk about where we see deep learning applied in the real world. Can anyone name a field where deep learning is making a significant impact?
Healthcare! Itβs used for medical imaging.
Correct! Deep learning has revolutionized healthcare with tasks like analyzing medical images. What about in finance?
Fraud detection is one area.
Yes! These applications leverage complex pattern recognition. To remember, think about H-F-R for Healthcare, Finance, and Retail. Can anyone recap the ethical considerations we must keep in mind?
Issues like bias in training data and privacy concerns.
Excellent summary! Understanding the ethical side is crucial to ensure responsible AI development.
Introduction & Overview
Read a summary of the section's main ideas. Choose from Basic, Medium, or Detailed.
Quick Overview
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This section delves into the essence of Deep Learning and Neural Networks, outlining the structure of artificial neural networks, the significance of activation functions, and the principles behind deep learning architectures. It also highlights training methods, regularization techniques, and real-world applications that showcase the impact of deep learning across various domains.
Detailed
Detailed Summary of Deep Learning and Neural Networks
Deep Learning represents a significant advancement in machine learning, characterized by its ability to model complex patterns through artificial neural networks (ANNs). ANNs consist of interconnected nodes (neurons), organized into layers: the input layer, one or more hidden layers, and the output layer. Each connection in this network has a weight, which adjusts during training.
8.1 Fundamentals of Neural Networks
It kicks off with the definition and structure of ANNs and introduces essential components:
- Neuron (Perceptron): The basic unit of computation, which processes inputs through an activation function to produce output.
- Activation Functions: These functions introduce non-linearity into the network, vital for learning complex relationships. Popular choices include Sigmoid, Tanh, ReLU, and Softmax.
8.2 Deep Neural Networks (DNNs)
A network is termed 'deep' when it includes multiple hidden layers, which facilitates the learning of intricate features. This section elaborates on crucial processes:
- Forward Propagation: The method of passing data through the model.
- Loss Functions: These measure the efficiency of predictions, such as Mean Squared Error for regression and Cross-Entropy Loss for classification.
8.3 Training Deep Networks
Key training strategies include:
- Backpropagation: The algorithm used for training that updates weights based on errors.
- Gradient Descent Variants: Techniques such as Batch and Stochastic Gradient Descent that optimize the training process.
Challenges in this domain involve issues like vanishing gradients and overfitting, while regularization methods like Dropout and L1/L2 Regularization combat these problems.
8.5 Types of Deep Learning Architectures
Different architectures serve various purposes:
- Convolutional Neural Networks (CNNs): Best for processing image data.
- Recurrent Neural Networks (RNNs): Suited for sequential data, such as in language modeling.
- Autoencoders and GANs: Utilized for unsupervised learning tasks.
8.6 Transfer Learning and Frameworks
The section discusses the benefits of transfer learning and popular frameworks such as TensorFlow and PyTorch, supporting efficient development across diverse applications.
8.9 Real-World Applications and Ethical Considerations
Finally, it explores the diverse real-world applications of deep learning across sectors like healthcare and finance, as well as ethical considerations practitioners must navigate.
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Introduction to Deep Learning
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Deep Learning is a subfield of machine learning inspired by the structure and function of the human brain. It is based on artificial neural networks (ANNs), particularly deep neural networks with many layers. Deep learning has transformed fields such as computer vision, natural language processing, speech recognition, and autonomous systems, enabling machines to achieve unprecedented performance. This chapter explores the fundamentals of deep learning and neural networks, the architecture of deep models, training techniques, popular frameworks, and real-world applications. Whether you are training a neural network from scratch or leveraging pre-trained models, understanding the underlying principles is critical for success in advanced data science.
Detailed Explanation
Deep Learning refers to a specialized area within machine learning that mimics how our brain works using structures called artificial neural networks (ANNs). These networks have multiple layers, allowing them to learn from vast amounts of data and improve performance in various tasks, including images and text analysis. This chapter discusses not just what deep learning is, but also how neural networks are structured, how they are trained, the tools available for development, and their applications across industries. Grasping these concepts is important for anyone aspiring to work in data science and artificial intelligence.
Examples & Analogies
Think of deep learning like training a chef. Just like a chef starts with basic cooking skills and learns complex recipes over time, deep learning models begin with simple tasks and gradually learn to recognize patterns from larger datasets, improving their abilities as they βpracticeβ.
What is a Neural Network?
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An Artificial Neural Network (ANN) is a computational model inspired by the human brain's network of neurons. It consists of layers of interconnected nodes (neurons), where each connection has an associated weight and bias.
β’ Neuron (Perceptron): Basic unit that takes weighted inputs, applies an activation function, and produces an output.
β’ Layers:
o Input Layer
o Hidden Layer(s)
o Output Layer
Detailed Explanation
A Neural Network is designed to simulate the way human brains process information. The 'neurons' in these networks receive inputs, adjust these according to weights assigned to them, and then apply activation functions to produce an output. The network is structured in layers. The input layer receives the initial data, the hidden layers perform computations, and the output layer gives the final result. This layered approach allows the network to learn complex functions and representations from raw data.
Examples & Analogies
Imagine a group of people (neurons) working together on a project. The input layer consists of their initial ideas, the hidden layers are where they discuss and refine those ideas, and the output layer is the finished project. The way they collaborate and process information mimics how neural networks function.
Activation Functions
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Activation functions introduce non-linearity into the network. Common activation functions include:
| Function | Formula | Purpose |
|---|---|---|
| Sigmoid | π(π₯) = 1 / (1 + π^βπ₯) | Squashes input to range (0, 1) |
| Tanh | tanh(π₯) = (π^π₯ β π^βπ₯) / (π^π₯ + π^βπ₯) | Output in range (-1, 1) |
| ReLU | ReLU(π₯)= max(0,π₯) | Fast convergence, handles sparsity |
| Leaky ReLU | max(0.01π₯,π₯) | Avoids dying neurons problem |
| Softmax | π^π§π / βπ^π§π | Used for multi-class classification |
Detailed Explanation
Activation functions play a crucial role in determining how a neural network reacts to its inputs. They introduce non-linearity, allowing the network to model complex relationships. Different types of activation functions serve various purposes: Sigmoid squashes values to a range between 0 and 1, ideal for binary outcomes, while Tanh outputs values between -1 and 1, making it useful for zero-centered data. ReLU and Leaky ReLU help with the speed of learning and mitigate issues like 'dying neurons' where some neurons might not activate. Softmax is particularly essential in multi-class classification problems, ensuring outputs sum up to 1.
Examples & Analogies
Consider activation functions like the dimmer switch in your room. A regular switch can either be off (no light) or on (full light); this is similar to linear functions. The dimmer switch allows you to control how much light is emitted (non-linearity), which gives you better functionality, just like activation functions control how effective a neuron is in a neural network.
Deep Neural Networks (DNNs)
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A neural network is considered deep when it contains multiple hidden layers. Depth allows the model to learn complex features and hierarchical representations.
Detailed Explanation
Deep Neural Networks (DNNs) are characterized by having multiple hidden layers between the input and output layers. The increased number of layers allows DNNs to learn more complex patterns. Each layer extracts different features from the input data; for instance, in image recognition, the first layer might detect edges, the next layer may identify shapes, and deeper layers could discern specific objects. This hierarchical learning mimics human perception, where we recognize objects step by step.
Examples & Analogies
Think of a DNN like building a brick wall. Each layer of bricks represents depth, and each row contributes to the overall strength and structure. If you build just one layer, you might have something simple, but as you add more bricks (layers), the wall can become much stronger and capable of withstanding more forces (complex tasks).
Forward Propagation
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Forward propagation is the process of passing input data through the network to produce an output.
Detailed Explanation
Forward propagation is how a neural network processes inputs to generate outputs. It involves feeding the input data into the input layer, and the data is then passed sequentially through the hidden layers to the output layer. At each layer, the data undergoes calculations based on the weights and activation functions defined for that layer. This step is crucial as it determines how input data will be transformed through the network into something meaningful.
Examples & Analogies
Consider forward propagation like baking a cake. You have your raw ingredients (input data) and you mix them in steps (layer by layer). Each step adds flavor and texture (transformations) until you finally have a delicious cake (output). Just like careful mixing leads to the best cake, precise calculations in each layer lead to accurate predictions.
Loss Functions
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Loss functions quantify the error between predicted and actual values.
β’ MSE (Mean Squared Error) β for regression tasks
β’ Cross-Entropy Loss β for classification tasks
Detailed Explanation
Loss functions are essential in training neural networks as they measure how well the modelβs predictions match the actual data. The Mean Squared Error (MSE) is used mainly for regression tasks, evaluating the average squared differences between predicted and actual values. It's useful for numerical predictions. On the other hand, Cross-Entropy Loss is typically used for classification tasks, determining the difference between the predicted probability distribution and the actual distribution, helping to guide the network to improve its classifications.
Examples & Analogies
Imagine youβre trying to shoot arrows at a target. Each time you shoot, you assess how far off you were from the bullseye (actual value) with each arrow (predicted value). MSE is like measuring the average distance of your shots from the target to improve your aim, while Cross-Entropy helps you understand how accurate you are in hitting different areas of the target.
Backpropagation
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Backpropagation is the algorithm for training neural networks. It computes the gradient of the loss function with respect to each weight using the chain rule and updates the weights using gradient descent.
Detailed Explanation
Backpropagation is a core algorithm that enables neural networks to learn from errors. It calculates the gradient, or the rate of change, of the loss function concerning each weight in the network. This is done using the chain rule of calculus to propagate gradients backward through the network. Once computed, it updates the weights in a way that reduces the error by moving in the direction of the steepest descent; this process is known as gradient descent. This step is vital for refining the model to make accurate predictions.
Examples & Analogies
Think of backpropagation like learning from your mistakes in sports. If you miss a shot, you analyze what went wrong (calculating the gradient), understand where to adjust your stance or aim (updating the weights), and practice to improve your next shot (reducing error). This feedback loop is essential for growth and improvement in both sports and neural networks.
Gradient Descent Variants
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β’ Batch Gradient Descent
β’ Stochastic Gradient Descent (SGD)
β’ Mini-batch Gradient Descent
β’ Optimizers:
o Adam
o RMSProp
o Adagrad
Detailed Explanation
Gradient Descent is a technique used to optimize neural networks during training. There are several variants to speed up the process and improve results. In Batch Gradient Descent, the model uses the entire dataset to compute the gradient, which can slow down training for large datasets. Stochastic Gradient Descent (SGD) calculates the gradient using one sample at a time, making it faster but noisier. Mini-batch Gradient Descent strikes a balance; it uses small batches to stabilize learning while maintaining speed. Additionally, various optimizers like Adam, RMSProp, and Adagrad help fine-tune the learning rate, adapting it during training for better convergence.
Examples & Analogies
Use the analogy of a hiker finding their way up a mountain. Batch Gradient Descent is like looking at the whole landscape to choose your path, which may take a lot of time. SGD is like taking chaotic, rapid steps based on how the terrain feels underfoot. Mini-batch Gradient Descent combines these methods, taking steady steps while frequently checking the surroundings. Different optimizers, like a guide with advanced tools, help you find the fastest route to the summit!
Challenges in Training
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β’ Vanishing/Exploding Gradients
β’ Overfitting
β’ Computational Complexity
Detailed Explanation
When training deep networks, several challenges can arise. Vanishing and exploding gradients refer to situations where the gradients become too small or too large, making learning inefficient. Overfitting happens when the model learns the training data too well, including its noise, resulting in poor performance on unseen data. Lastly, the complexity of deep networks can lead to long training times and the need for extensive resources, which can be a barrier to effective training.
Examples & Analogies
Training a deep network can be like preparing for an exam. If you only memorize facts without truly understanding concepts (overfitting), you may do well on practice tests but fail in real-life applications. Vanishing gradients are like studying only a few areas too deeply, while exploding gradients might make you rush and skip important concepts, resulting in confusion. Balancing your study and preparation (training) time while managing resources is essential for effective learning.
Regularization Techniques
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β’ Dropout β Randomly disables neurons during training.
β’ L1/L2 Regularization β Penalizes large weights.
β’ Early Stopping β Halts training when validation error increases.
Detailed Explanation
Regularization techniques are strategies used to prevent overfitting in deep learning models. Dropout is a method where a random selection of neurons is turned off during training, preventing the model from relying too heavily on specific features. L1 and L2 Regularization add penalties for large weights, encouraging simpler models. Early stopping involves monitoring the validation loss and stopping training when it begins to increase, which can prevent the model from becoming too tailored to the training data.
Examples & Analogies
Think of these techniques like preparing for a performance. Dropout is like practicing solo without relying on a partner, which helps you develop your own skills. L1/L2 Regularization ensures you donβt learn to rely too much on any one part of your performance, maintaining versatility. Early stopping is akin to recognizing when rehearsals are becoming stale and stepping back at your peak performance moment.
Types of Deep Learning Architectures
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β’ Convolutional Neural Networks (CNNs) - Designed for image and spatial data.
o Convolutional layers
o Pooling layers
o Applications: Image classification, object detection
β’ Recurrent Neural Networks (RNNs) - Designed for sequential data.
o LSTM (Long Short-Term Memory)
o GRU (Gated Recurrent Unit)
o Applications: Time series forecasting, language modeling
β’ Autoencoders - Used for unsupervised learning and dimensionality reduction.
o Encoder and Decoder
o Applications: Anomaly detection, denoising
β’ Generative Adversarial Networks (GANs)
o Generator vs Discriminator
o Application: Image synthesis, data augmentation
Detailed Explanation
Different types of architectures in deep learning are optimized for various tasks. Convolutional Neural Networks (CNNs) are typically used for processing images, employing convolutional and pooling layers to extract features. Recurrent Neural Networks (RNNs) handle sequential data, like time series or text, with architectures like LSTM and GRU to capture dependencies over time. Autoencoders focus on unsupervised learning and reducing data dimensions through encoding and decoding processes. GANs consist of two neural networksβthe generator and the discriminatorβworking against each other to create realistic new data, such as images.
Examples & Analogies
Think of these architectures like different tools for specific tasks. A CNN is like a specialized camera lens designed for sharp images (image tasks). RNNs are like a narrator in a story, weaving together events (sequential data). Autoencoders resemble a sculptor who chisels away unnecessary parts to reveal the essence (dimensionality reduction). GANs are like two competitive artists, one creating artworks while the other critiques them, pushing for ever more realistic art.
Transfer Learning
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β’ Uses pre-trained models (e.g., ResNet, BERT).
β’ Saves time and computational resources.
β’ Fine-tuning adapts the model to new tasks with smaller datasets.
Detailed Explanation
Transfer learning is a technique that takes advantage of previously trained models on similar tasks to accelerate the learning process for new, often related tasks. By leveraging pre-trained models like ResNet for images or BERT for natural language, one can save significant development time and computational resources. After using these models, further fine-tuning on smaller datasets specific to the new task can provide good performance without needing to build a model from scratch.
Examples & Analogies
This is akin to learning how to play the piano. If you already play guitar, many of your skills (like understanding of music theory) transfer over, making learning the piano easier and faster. Similarly, by applying skills from one model to another, you donβt start from square one.
Deep Learning Frameworks
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Popular Libraries:
Framework | Language | Features
--- | --- | ---
TensorFlow | Python | Scalable, good for production
PyTorch | Python | Dynamic computation graphs, research-friendly
Keras | Python | High-level API (runs on TF backend)
MXNet | Python/R | Distributed training, hybrid frontend
Detailed Explanation
There are various deep learning frameworks available, each catering to different needs. TensorFlow is widely used for production applications and is scalable for large datasets. PyTorch is favored in research for its flexibility and ease of debugging with dynamic computation graphs. Keras offers a user-friendly high-level API built on top of TensorFlow, simplifying model design. MXNet supports distributed training and can be used with either Python or R, making it versatile for multiple developers. Choosing the right framework depends on the project requirements and personal preferences.
Examples & Analogies
Think of frameworks as different types of kitchens for cooking. TensorFlow is a fully equipped professional kitchenβgreat for large-scale production. PyTorch is more like a flexible pop-up kitchen where you can quickly change menus and try new recipes. Keras is like a pre-prepared meal kit that makes following recipes easier. MXNet is adaptable, allowing you to cook with multiple teams across diverse cuisine styles.
Evaluation Metrics for Deep Learning Models
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Classification Metrics:
β’ Accuracy
β’ Precision, Recall, F1-score
β’ ROC-AUC
Regression Metrics:
β’ MSE, RMSE
β’ MAE
β’ RΒ² Score
Detailed Explanation
To assess the performance of deep learning models, different evaluation metrics are used based on the type of task. For classification tasks, metrics like accuracy, precision, recall, F1-score, and ROC-AUC are critical for understanding model performance. In regression, metrics like Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and RΒ² Score provide insight into how well the model predicts continuous outcomes. Selecting the right metric is essential for accurately evaluating model effectiveness.
Examples & Analogies
Evaluating a model is like grading a student. Accuracy is like the overall score, but precision and recall are akin to checking how well they did in specific subjects. Just as some students might excel in certain areas while struggling in others, models can perform variably, making it essential to use a balanced set of metrics for a complete understanding.
Real-World Applications
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| Domain | Application | Example |
|---|---|---|
| Healthcare | Medical imaging, drug discovery | |
| Finance | Fraud detection, algorithmic trading | |
| Retail & E-commerce | Customer segmentation, recommendation | |
| Transportation | Self-driving cars | |
| NLP | Chatbots, sentiment analysis |
Detailed Explanation
Deep learning has numerous real-world applications across various domains. In healthcare, it aids in medical imaging and drug discovery, improving diagnostic accuracy. In finance, it supports fraud detection and algorithmic trading strategies to make smarter financial decisions. The retail world utilizes deep learning for customer segmentation and personalized recommendations based on shopping behaviors. In transportation, deep learning powers technologies for self-driving cars. Lastly, in natural language processing (NLP), it enhances the functionalities of chatbots and sentiment analysis, allowing better human-computer interaction.
Examples & Analogies
Consider deep learning applications as specialized tools in a toolbox. Just as youβd use different tools for different tasksβlike a hammer for nails and a screwdriver for screwsβdeep learning techniques can be employed to tackle specific challenges effectively across various fields.
Ethical Considerations in Deep Learning
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β’ Bias in Training Data
β’ Model Explainability
β’ Privacy Concerns
β’ Energy Consumption and Carbon Footprint
Detailed Explanation
Alongside the advancements in deep learning, ethical considerations have gained prominence. Bias in training data can lead to unfair outcomes and reinforce stereotypes. Model explainability is crucial, as stakeholders need to understand how decisions are made, especially in high-stakes scenarios. Privacy concerns arise from collecting and using personal data, necessitating protection standards. Additionally, energy consumption and the carbon footprint of training large models have become critical topics as we try to balance progress with environmental sustainability.
Examples & Analogies
These ethical issues are analogous to ethical gardening. Just like a gardener must ensure the plants grow fairly and sustainably without harming the environment or neighboring ecosystems, engineers must ensure models are developed and deployed responsibly, considering fairness, transparency, privacy, and ecological impact.
Definitions & Key Concepts
Learn essential terms and foundational ideas that form the basis of the topic.
Key Concepts
-
Artificial Neural Networks: Computational models using layered structures to simulate human brain functions.
-
Activation Functions: Mathematical functions introducing non-linearities critical for learning complex patterns.
-
Backpropagation: The method of efficiently training neural networks through iterative weight adjustments based on errors.
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Overfitting: A challenge faced in machine learning where a model learns the noise in the training data instead of the intended outputs.
Examples & Real-Life Applications
See how the concepts apply in real-world scenarios to understand their practical implications.
Examples
-
A CNN (Convolutional Neural Network) can classify images of animals by learning spatial hierarchies through its layers.
-
RNNs (Recurrent Neural Networks) like LSTMs are used in natural language processing for tasks such as machine translation and sentiment analysis.
Memory Aids
Use mnemonics, acronyms, or visual cues to help remember key information more easily.
π΅ Rhymes Time
-
In a network of layers, learning so sweet, with weights and biases, true intelligence we greet.
π Fascinating Stories
-
Imagine a student, a layer of neurons, each learning independently but sharing their insights, collectively solving complex math problems. That's how networks function!
π§ Other Memory Gems
-
Remember the acronym I-H-O for Input, Hidden, and Output layers of ANN.
π― Super Acronyms
For activation functions, think S-T-R
- Sigmoid
- Tanh
- ReLU.
Flash Cards
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Glossary of Terms
Review the Definitions for terms.
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Term: Artificial Neural Network (ANN)
Definition:
A computational model inspired by the human brain consisting of interconnected nodes (neurons) in layers.
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Term: Neuronal Activation Functions
Definition:
Mathematical equations that determine if a neuron should be activated, introducing non-linearity in the network.
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Term: Backpropagation
Definition:
An algorithm used to train neural networks by calculating gradients of the loss function and updating weights.
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Term: Overfitting
Definition:
A modeling error which occurs when a machine learning model captures noise along with the underlying data pattern.