- What is AI
- Narrow AI
- Strong AI
- Nueral Network
- Deep Learning
- AI Perceptrons
- AI Historical Context
- AI Types
- Problem Solving Using AI
- AI Use Cases In Real Life
- AI Advantages & Disadvantages
- AI Tools
- Cognitive AI
- Symbolic AI
- AI Ethics
- Responsible AI
- AI Multi Model Networks
- AI Genetic Algorithms
- AI Multi Agent Systems
- AI Prompt Programming
- AI Taining Your Embeddings
- AI CCN Architecture
- AI Pre Trained Networks
Deep Learning.
Deep Learning is a subset of machine learning (ML) that focuses on training neural networks with multiple layers to process complex data. It is the key technology behind AI applications like image recognition, speech processing, and natural language understanding.
Deep learning uses artificial neural networks (ANNs) with multiple hidden layers to learn patterns from large amounts of data. It is called “deep” because of the many layers in the network.
Key Differences Between ML and Deep Learning
Neural Network Structure
A deep learning model consists of multiple layers of neurons:
1️⃣ Input Layer Receives raw data (e.g., images, text, numbers).
2️⃣ Hidden Layers (One or More) Perform feature extraction using weights and activation functions. The deeper the network, the more complex patterns it can learn.
3️⃣ Output Layer Produces the final prediction (e.g., classification labels).
Mathematical Representation
Each neuron performs:
y=ActivationFunction(WX+B)
where:
W = Weights X = Input B = Bias Activation functions introduce non-linearity (e.g., ReLU, Sigmoid, Tanh).
Key Differences Between ML and Deep Learning
| Feature | Machine Learning | Deep Learning |
| Feature Extraction | Manual (requires domain expertise) | Automatic (learns from data) |
| Performance on Big Data | Limited | Scales well with large datasets |
| Computational Power | Moderate | Requires GPUs for high performance |
| Examples | Decision Trees, SVM, Random Forest | CNN, RNN, Transformers |
Neural Network Structure
A deep learning model consists of multiple layers of neurons:
1️⃣ Input Layer Receives raw data (e.g., images, text, numbers).
2️⃣ Hidden Layers (One or More) Perform feature extraction using weights and activation functions. The deeper the network, the more complex patterns it can learn.
3️⃣ Output Layer Produces the final prediction (e.g., classification labels).
Mathematical Representation
Each neuron performs:
y=ActivationFunction(WX+B)
where:
W = Weights X = Input B = Bias Activation functions introduce non-linearity (e.g., ReLU, Sigmoid, Tanh).
Training a Deep Learning Model
Training a deep neural network involves multiple steps:
1️⃣ Forward Propagation
Input data passes through the layers. Predictions are made based on current weights.
2️⃣ Loss Function Calculation
Measures the difference between predicted and actual values. Examples: Mean Squared Error (MSE), Cross-Entropy Loss.
3️⃣ Backpropagation & Optimization
Adjusts weights using Gradient Descent to minimize the loss. Optimizers like Adam, SGD, RMSprop help speed up learning.
4️⃣ Epochs & Batch Training
Training is repeated over multiple iterations (epochs). Data is processed in small chunks (batches) for efficiency.
Popular Deep Learning Architectures
🔹 Feedforward Neural Networks (FNN)
Basic neural network where data flows in one direction.
🔹 Convolutional Neural Networks (CNN)
Used for image processing, object detection, and facial recognition.
🔹 Recurrent Neural Networks (RNN)
Best for sequential data (e.g., speech, text, time-series).
🔹 Long Short-Term Memory (LSTM)
A special type of RNN that handles long-term dependencies.
🔹 Transformers (e.g., BERT, GPT)
Advanced NLP models for text understanding and generation.
Applications of Deep Learning
📸 Computer Vision (Face recognition, self-driving cars)
🎙 Speech Recognition (Siri, Google Assistant)
📝 Natural Language Processing (NLP) (ChatGPT, Google Translate)
🔬 Healthcare (Disease detection, medical imaging)
🏦 Finance (Fraud detection, stock prediction)
Training a deep neural network involves multiple steps:
1️⃣ Forward Propagation
Input data passes through the layers. Predictions are made based on current weights.
2️⃣ Loss Function Calculation
Measures the difference between predicted and actual values. Examples: Mean Squared Error (MSE), Cross-Entropy Loss.
3️⃣ Backpropagation & Optimization
Adjusts weights using Gradient Descent to minimize the loss. Optimizers like Adam, SGD, RMSprop help speed up learning.
4️⃣ Epochs & Batch Training
Training is repeated over multiple iterations (epochs). Data is processed in small chunks (batches) for efficiency.
Popular Deep Learning Architectures
🔹 Feedforward Neural Networks (FNN)
Basic neural network where data flows in one direction.
🔹 Convolutional Neural Networks (CNN)
Used for image processing, object detection, and facial recognition.
🔹 Recurrent Neural Networks (RNN)
Best for sequential data (e.g., speech, text, time-series).
🔹 Long Short-Term Memory (LSTM)
A special type of RNN that handles long-term dependencies.
🔹 Transformers (e.g., BERT, GPT)
Advanced NLP models for text understanding and generation.
Applications of Deep Learning
📸 Computer Vision (Face recognition, self-driving cars)
🎙 Speech Recognition (Siri, Google Assistant)
📝 Natural Language Processing (NLP) (ChatGPT, Google Translate)
🔬 Healthcare (Disease detection, medical imaging)
🏦 Finance (Fraud detection, stock prediction)