Podcast
Questions and Answers
What is the primary task in the ImageNet Challenge?
What is the primary task in the ImageNet Challenge?
How many object categories are present in the ImageNet Challenge?
How many object categories are present in the ImageNet Challenge?
What is an artificial neural network?
What is an artificial neural network?
What is the purpose of an activation function in a neural network?
What is the purpose of an activation function in a neural network?
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What is an artificial neuron?
What is an artificial neuron?
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What is the output of an artificial neuron?
What is the output of an artificial neuron?
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What is the main goal of Machine Learning algorithms?
What is the main goal of Machine Learning algorithms?
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What is the performance measure in a Text Classification problem?
What is the performance measure in a Text Classification problem?
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Who is credited with introducing the term 'Machine Learning'?
Who is credited with introducing the term 'Machine Learning'?
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What is the task in a chess learning problem?
What is the task in a chess learning problem?
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What is the training experience in a Text Classification problem?
What is the training experience in a Text Classification problem?
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What is the common goal of both Machine Learning and Neural Networks?
What is the common goal of both Machine Learning and Neural Networks?
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What is the primary goal of the performance measure P?
What is the primary goal of the performance measure P?
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What is the primary difference between traditional programming and machine learning?
What is the primary difference between traditional programming and machine learning?
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What is the primary advantage of deep learning methods over shallow learning methods?
What is the primary advantage of deep learning methods over shallow learning methods?
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What is the primary characteristic of the trainable classifier in shallow learning?
What is the primary characteristic of the trainable classifier in shallow learning?
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What is the primary reason why deep learning methods have become dominant in recent years?
What is the primary reason why deep learning methods have become dominant in recent years?
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What is the primary characteristic of the features extracted in deep learning?
What is the primary characteristic of the features extracted in deep learning?
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What does the bias unit in an artificial neuron do?
What does the bias unit in an artificial neuron do?
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What is the primary characteristic of a neural network's architecture?
What is the primary characteristic of a neural network's architecture?
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What is the result of using a linear activation function in a multilayer neural network?
What is the result of using a linear activation function in a multilayer neural network?
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What is the purpose of the training algorithm in a neural network?
What is the purpose of the training algorithm in a neural network?
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What is the characteristic of a binary step function?
What is the characteristic of a binary step function?
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What are hyperparameters in a neural network?
What are hyperparameters in a neural network?
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What is the main issue with the sigmoid activation function?
What is the main issue with the sigmoid activation function?
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What is the range of the output of the sigmoid activation function?
What is the range of the output of the sigmoid activation function?
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What is the advantage of the ReLU activation function?
What is the advantage of the ReLU activation function?
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What is the problem with the ReLU activation function when inputs approach zero or are negative?
What is the problem with the ReLU activation function when inputs approach zero or are negative?
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What is the range of the output of the hyperbolic tangent (tanh) activation function?
What is the range of the output of the hyperbolic tangent (tanh) activation function?
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What is a characteristic of the sigmoid and hyperbolic tangent (tanh) activation functions?
What is a characteristic of the sigmoid and hyperbolic tangent (tanh) activation functions?
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Study Notes
Performance Measure and Training Experience
- Performance measure P: average distance travelled before an error (as judged by human overseer)
- Training experience E: a sequence of images and steering commands recorded while observing a human driver
Machine Learning vs Traditional Programming
- Traditional programming: writing a program to produce the output based on given data
- Machine learning: training a computer to produce the program that solves the problem based on given data and desired output
AI, ML, and DL
- AI: Artificial Intelligence
- ML: Machine Learning
- DL: Deep Learning
Shallow Learning
- Feature extraction is a manual process that requires domain knowledge of the data
- Trainable classifier is often generic (e.g. SVM)
- Features are key to recent progress in recognition
Deep Learning
- Learn underlying features directly from data
- Multiple layers of nonlinear processing units
- Each layer extracts features from the output of previous layer
- Supervised or unsupervised learning of feature hierarchies representations in each layer
Why Now?
- Neural Networks date back decades, but recent dominance is due to advancements in ImageNet Challenge
- ImageNet Large Scale Visual Recognition Challenge (ILSVRC) is a classification task that involves producing a list of object categories present in an image
Neural Network Basics
- Artificial neural network: an information-processing system that mimics the way the human brain operates
- Components: neurons, connection links, weights, and activation functions
Artificial Neuron
- An artificial neuron is the basic building block that constructs complicated neural networks
- It makes a particular computation based on other units it's connected to
Importance of Activation Functions
- Introduce non-linearities into the network
- Activation functions: sigmoid, hyperbolic tangent (tanh), rectified linear (ReLU)
Activation Functions
- Sigmoid activation function:
- Squashes the neuron's pre-activation between 0 and 1
- Always positive
- Bounded
- Strictly increasing
- Disadvantages: vanishing gradient problem, computationally expensive
- Hyperbolic tangent (tanh) activation function:
- Squashes the neuron's pre-activation between -1 and 1
- Can be positive or negative
- Bounded
- Strictly increasing
- Rectified linear (ReLU) activation function:
- Bounded below by 0 (always non-negative)
- Not upper bounded
- Strictly increasing
- Computationally efficient
- Disadvantage: the dying ReLU problem
Neural Network Characterization
- A neural network is characterized by its architecture, activation function, and method of determining the weights on the connections (training algorithm)
- Hyperparameters include learning rate, epochs, and batch size
Studying That Suits You
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Description
Explore the brief history of Neural Networks, Machine Learning, and its algorithms. Learn how computers can detect patterns in data and predict future outcomes without being explicitly programmed.
