Deep Learning Fundamentals

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Questions and Answers

What is the primary role of humans in the deep learning process?

Framing the problem and acquiring data

What is the main advantage of deep learning over traditional machine learning?

Ability to learn from raw data

What is the primary reason why hand engineering features is not a good idea?

All of the above

What is the current state of deep learning in the field of AI?

It is one of the main approaches Signup and view all the answers

What is the relationship between deep learning and traditional machine learning?

Deep learning is a subset of machine learning Signup and view all the answers

What is the outcome of deep learning in many applications?

Establishing the state of the art by large margins Signup and view all the answers

What is the primary function of the dendrite in a biological neuron?

To receive signals from other neurons Signup and view all the answers

What is the minimum number of neurons required to form an interconnected network that works in parallel?

Hundreds Signup and view all the answers

What is the output of a neuron in a biological computational model?

A binary value of 0 or 1 Signup and view all the answers

What is the purpose of the synapse in a biological neuron?

To receive signals from other neurons Signup and view all the answers

What is the primary advantage of modeling a single neuron based on the observations of biological neurons?

It enables the creation of a threshold function using a logic operator Signup and view all the answers

What is the estimated number of neurons in the human brain?

Billions Signup and view all the answers

What happens to the neuron when it receives an inhibitory input, regardless of other inputs?

It does not fire at all Signup and view all the answers

What is the main limitation of the McCulloch-Pitts Artificial Neuron model?

It cannot capture the complexity of biological neurons Signup and view all the answers

What is the output of the AND function in the context of MCP neurons, given all incoming inputs are 1?

1, because the neuron fires Signup and view all the answers

What is the mathematical condition for the neuron to fire in the MCP model, assuming no inhibitory inputs?

The sum of the inputs is greater than or equal to the threshold Signup and view all the answers

Who proposed the McCulloch-Pitts Artificial Neuron model in 1943?

Warren MuCulloch and Walter Pitts Signup and view all the answers

What is the term used to describe the MCP model in alternative contexts?

Linear threshold gate Signup and view all the answers

What is the primary characteristic of the McCulloch-Pitts neuron model?

It can process continuous values Signup and view all the answers

What was the perceptron model, proposed by Rosenblatt, capable of doing?

Classifying data into two parts Signup and view all the answers

What is the significance of the perceptron model in the development of ANN models?

It is considered a significant first step towards development of ANN models Signup and view all the answers

What is the concept of numerical weights in the perceptron model?

The weights are learned during learning Signup and view all the answers

Who is credited with the invention of the perceptron model?

Frank Rosenblatt Signup and view all the answers

What was the expectation of the Navy from the perceptron model?

To develop an electronic computer that can walk, talk, see, write, reproduce itself and be conscious of its existence Signup and view all the answers

What is the primary advantage of ReLU over sigmoid/tanh functions in terms of convergence?

It accelerates convergence Signup and view all the answers

What makes ReLU units fragile during training?

Their ability to 'die' when the weights put them on the wrong side Signup and view all the answers

What happens when the current weights put the ReLU on the left flat side?

The weight is not updated at all Signup and view all the answers

What is the main difference between ReLU and tanh/sigmoid neurons in terms of operations?

tanh/sigmoid neurons involve more expensive operations Signup and view all the answers

Why does the weight not get updated when the ReLU is on the wrong side?

Because the gradient is zero Signup and view all the answers

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Study Notes

Machine Learning and Deep Learning

Machine learning involves framing a problem, acquiring and organizing data, designing a space of possible solutions, selecting a learning algorithm and its parameters, applying the algorithm to the data, and validating the resulting solution.
Deep learning is a type of machine learning that involves teaching computers to learn a task directly from raw data.

Importance of Deep Learning

Extracting features manually is not a good idea, as it is time-consuming, confusing, and often inapplicable.
Deep learning can solve the challenge of manually extracting features by learning the underlying features directly from data.
Deep learning has become a main approach to AI, successfully applied to various pattern recognition, prediction, and analysis problems, and has established state-of-the-art results in many areas.

Neural Networks

Neural networks are inspired by biological neurons, with a structure consisting of dendrites, synapses, soma, and axon.
Computational models of neurons are based on observations of how biological neurons work, including the concept of interconnected networks of hundreds to thousands of neurons working in parallel.
Single neuron output can be binary (0 or 1), and can be modeled using a threshold function with binary inputs.

The McCulloch-Pitts Artificial Neuron

The McCulloch-Pitts artificial neuron is the first computational model of a neuron, proposed by Warren McCulloch and Walter Pitts in 1943.
This model is also known as a linear threshold gate or threshold logic unit, and is a highly simplified representation of a neuron.
The McCulloch-Pitts neuron can be mathematically formalized using a binary output function.

Challenges of MCP Neurons

Boolean operations, such as AND and OR functions, are challenging to implement using MCP neurons.
MCP neurons also have limitations in processing continuous values, measuring importance, and having a learning procedure.

The Perceptron

The Perceptron is a more general computational model than the McCulloch-Pitts neuron, proposed by Frank Rosenblatt in 1958.
The Perceptron can classify data into two parts and is known as a Linear Binary Classifier.
The concept of numerical weights is introduced in the Perceptron, and these weights are learned during the learning process.

Training of Neural Networks

Gradient descent is a method used for training neural networks.
The error surface is a critical concept in training neural networks, and is used to visualize the relationship between the model's parameters and the error.
In practice, the error surface is complex and has many local minima, making training a neural network challenging.