CNN, RNN & Transformers

Let’s first see what are the most popular deep learning models.

Deep Learning Models

Deep learning models are a subset of machine learning algorithms that utilize artificial neural networks to analyze complex patterns in data. Inspired by the human brain’s neural structure, these models comprise multiple layers of interconnected nodes (neurons) that process and transform inputs into meaningful representations. Deep learning has revolutionized various domains, including computer vision, natural language processing, speech recognition, and recommender systems, due to its ability to learn hierarchical representations, capture non-linear relationships, and generalize well to unseen data.

Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs)

The emergence of CNNs and RNNs marked significant milestones in deep learning’s evolution. CNNs, introduced in the 1980s, excel at image and signal processing tasks, leveraging convolutional and pooling layers to extract local features and downsample inputs. RNNs, developed in the 1990s, are designed for sequential data processing, using recurrent connections to capture temporal dependencies. These architectures have achieved state-of-the-art results in various applications, including image classification, object detection, language modeling, and speech recognition. However, they have limitations, such as CNNs’ inability to handle sequential data and RNNs’ struggle with long-term dependencies.

Transformers: The Paradigm Shift

The introduction of Transformers in 2017 marked a paradigm shift in deep learning, particularly in natural language processing. Transformers replaced traditional RNNs and CNNs with self-attention mechanisms, eliminating the need for recurrent connections and convolutional layers. This design enables parallelization, capturing long-range dependencies, and efficiently handling sequential data. Transformers have achieved remarkable success in machine translation, language modeling, question answering, and text generation, setting new benchmarks and becoming the de facto standard for many NLP tasks. Their impact extends beyond NLP, influencing computer vision, speech recognition, and other domains, and continues to shape the future of deep learning research.

CNN

Convolutional Neural Networks (CNNs)

Architecture Components:

Convolutional Layers:

Filters/Kernels: Small, learnable feature detectors scanning the input image.

Convolution Operation: Sliding the filter across the image, performing dot products to generate feature maps.

Activation Function: Introduces non-linearity (e.g., ReLU).

Pooling Layers:

Downsampling: Reduces feature map spatial dimensions.

Max Pooling: Retains maximum value in each window.

Flatten Layer:

Flattening: Reshapes feature maps into 1D vectors.

Fully Connected Layers:

Dense Layers: Processes flattened features for classification.

Key Concepts:

Local Connectivity: Neurons only connect to nearby neurons.

Weight Sharing: Same filter weights applied across the image.

Spatial Hierarchy: Features extracted at multiple scales.

RNN

Recurrent Neural Networks (RNNs)

Architecture Components:

Recurrent Layers:

Hidden State: Captures information from previous time steps.

Recurrent Connections: Feedback loops allow information flow.

Activation Functions: Introduces non-linearity (e.g., tanh).

Input Gate: Controls information flow from input to hidden state.

Output Gate: Generates predictions based on hidden state.

Cell State: Long-term memory storage.

Key Concepts:

Sequential Processing: Inputs are processed one at a time.

Temporal Dependencies: Captures relationships between time steps.

Backpropagation Through Time (BPTT): Training RNNs.

Variants:

Simple RNNs: Basic architecture.

LSTM (Long Short-Term Memory): Addresses vanishing gradients.

GRU (Gated Recurrent Unit): Simplified LSTM.

Transformers

Transformers

Architecture Components:

Self-Attention Mechanism:

Query (Q), Key (K), Value (V) Vectors: Linear transformations.

Attention Weights: Compute the similarity between Q and K.

Weighted Sum: Calculates context vector.

Multi-Head Attention: Parallel Attention Mechanisms: Different representation subspaces.

Encoder:

Input Embeddings: Token embeddings.

Positional Encoding: Adds sequence order information.

Layer Normalization: Normalizes activations.

Feed-Forward Networks: Processes attention output.

Decoder:

Masked Self-Attention: Prevents future token influence.

Key Concepts:

Parallelization: Eliminates sequential processing.

Self-Attention: Captures token relationships.

Positional Encoding: Preserves sequence order information.

Variants:

Encoder-Decoder Transformer: Basic architecture.

BERT: Modified Transformer for language modeling.

Here’s a detailed comparison of CNN, RNN, and Transformer models, including their context, architecture, strengths, weaknesses, and examples:

Convolutional Neural Networks (CNNs)

Context: Primarily used for image classification, object detection, and image segmentation tasks.

Architecture:

Convolutional layers: Extract local features using filters

Pooling layers: Downsample feature maps

Fully connected layers: Classify features

Strengths:

Excellent for image-related tasks

Robust to small transformations (rotation, scaling)

Weaknesses:

Not suitable for sequential data (e.g., text, audio)

Limited ability to capture long-range dependencies

Example: Image classification using CNN

Input: 224x224x3 image

Output: Class label (e.g., dog, cat)

Recurrent Neural Networks (RNNs)

Context: Suitable for sequential data, such as natural language processing, speech recognition, and time series forecasting.

Architecture:

Recurrent layers: Process sequences one step at a time

Hidden state: Captures information from previous steps

Output layer: Generates predictions

Strengths:

Excels at sequential data processing

Can capture long-range dependencies

Weaknesses:

Vanishing gradients (difficulty learning long-term dependencies)

Computationally expensive

Example: Language modeling using RNN

Input: Sequence of words (“The quick brown…”)

Output: Next word prediction

Transformers

Context: Revolutionized natural language processing tasks, such as language translation, question answering, and text generation.

Architecture:

Self-attention mechanism: Weights importance of input elements

Encoder: Processes input sequence

Decoder: Generates output sequence

Strengths:

Excellent for sequential data processing

Parallelizable, reducing computational cost

Captures long-range dependencies effectively

Weaknesses:

Computationally expensive for very long sequences

Requires large amounts of training data

Example: Machine translation using Transformer

Input: English sentence (“Hello, how are you?”)

Output: Translated sentence (e.g., Spanish: “Hola, ?cómo estás?”)

These architectures have transformed the field of deep learning, with Transformers being particularly influential in NLP tasks.

Here are some key takeaways:

CNNs are ideal for image-related tasks.

RNNs are suitable for sequential data but struggle with long-term dependencies.

Transformers excel at sequential data processing and are the go-to choice for many NLP tasks.