What is Deep Learning?
Deep Learning (DL) is a specialized subset of Machine Learning based on artificial neural networks. The "deep" in deep learning refers to the use of multiple layers in the network. These algorithms attempt to simulate the behavior of the human brain to "learn" from large amounts of data.
Machine Learning vs. Deep Learning
While both are branches of AI, their approach to data processing differs significantly:
- Machine Learning: Usually requires manual feature extraction. A human expert must tell the algorithm what data points (features) to look at to make a decision.
- Deep Learning: Performs automatic feature extraction. You feed the raw data (like pixels of an image) directly into the network, and the hidden layers automatically learn which features are important.
The math behind deep learning has existed since the 1980s. It only became a dominant force recently due to two factors: the explosion of Big Data (to train the models) and the advancement of GPUs (to perform the massive matrix calculations quickly).
Real World Applications
Deep Learning drives the most cutting-edge technologies we use today:
- Computer Vision: Facial recognition, medical image analysis (detecting tumors), and object detection.
- Natural Language Processing (NLP): ChatGPT, language translation, and sentiment analysis.
- Speech Recognition: Virtual assistants like Siri and Alexa.
A Quick Look at Keras/TensorFlow
Building a deep neural network is remarkably accessible with modern frameworks. Here is a conceptual peek at creating a simple multi-layer network:
# 1. Import TensorFlow/Keras import tensorflow as tf from tensorflow.keras.models import Sequential from tensorflow.keras.layers import Dense # 2. Initialize a Sequential model (a linear stack of layers) model = Sequential() # 3. Add 'Deep' Layers to the network model.add(Dense(128, activation='relu', input_shape=(784,))) # Hidden Layer 1 model.add(Dense(64, activation='relu')) # Hidden Layer 2 model.add(Dense(10, activation='softmax')) # Output Layer # 4. Compile the model ready for training model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
Research the difference between TensorFlow and PyTorch. What are the pros and cons of each framework, and which one is generally preferred in academia versus industry?
Artificial Neural Networks (ANNs)
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The Perceptron
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Activation Functions
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Forward Propagation
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Loss Functions
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Gradient Descent & Backpropagation
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Optimizers (Adam, RMSprop)
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Handling Overfitting (Dropout)
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Batch Normalization
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Introduction to CNNs
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Convolution Operations
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Pooling Layers
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Famous CNN Architectures (ResNet)
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Transfer Learning
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Sequence Data & RNNs
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The Vanishing Gradient Problem
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Long Short-Term Memory (LSTM)
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Gated Recurrent Units (GRUs)
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Sequence-to-Sequence Models
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Attention Mechanisms
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The Transformer Architecture
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Autoencoders
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Variational Autoencoders (VAEs)
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Generative Adversarial Networks (GANs)
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Deep Q-Networks (RL Basics)
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Introduction to PyTorch
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Introduction to TensorFlow & Keras
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Model Deployment Strategies
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Ethics in Deep Learning
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