Questions and Answers
What is a key advantage of a Recurrent Neural Network (RNN)?
It enables parallel and sequential computation.
What is one of the steps involved in the working of Deep Learning Neural Networks?
Checking the feasibility of Deep Learning for the problem
In the context of Deep Learning, what role do languages like R, Python, and Java play?
They feed images directly into algorithms without manual extraction.
Why is feature engineering reduced when using deep learning neural networks?
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What is a disadvantage of using Deep Learning Neural Networks?
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Why are Recurrent Neural Networks (RNNs) compared to the human brain?
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What step is crucial before choosing a Deep Learning Algorithm?
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What is a standout quality of Deep Learning Neural Networks in terms of problem-solving?
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What differentiates Deep Learning algorithms from traditional methods in terms of data input?
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How do Deep Learning Neural Networks handle input images?
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Study Notes
Deep Learning Neural Networks
- Computationally expensive to train and lacks a strong theoretical foundation.
Applications
- Automatic Text Generation: Learns a corpus of text and generates new text word-by-word or character-by-character, capable of learning spelling, punctuation, sentence formation, and style.
- Healthcare: Helps diagnose and treat various diseases.
- Automatic Machine Translation: Transforms words, sentences, or phrases in one language to another language.
- Image Recognition: Recognizes and identifies people and objects in images, understands content and context, and is used in gaming, retail, tourism, etc.
- Predicting Earthquakes: Teaches a computer to perform viscoelastic computations for predicting earthquakes.
- Other popular applications: Self-driving cars, language translation, natural language processing, etc.
Difference between Machine Learning and Deep Learning
- If you have lots of data and high hardware capabilities, use deep learning; otherwise, choose a machine learning model.
- Fig. 6.21 provides a flowchart to help decide between ML and DL.
Deep Feedforward Networks
- Also called feedforward neural networks or multilayer perceptrons (MLPs).
- The goal is to approximate some function f∗, mapping an input x to a category y.
- Feedforward networks are essential to machine learning practitioners and form the basis of many commercial applications.
Recurrent Neural Network
- Allows for parallel and sequential computation.
- Similar to the human brain, remembering important things about the input.
- Enables precise results.
Working of Deep Learning
- Identify the problem and check the feasibility of using deep learning.
- Identify and prepare relevant data.
- Choose the appropriate deep learning algorithm.
- Train the dataset using the algorithm.
- Perform final testing on the dataset.
Deep Learning Model
- Takes images as input and feeds them directly to algorithms without manual feature extraction.
- Images pass through different layers of the artificial neural network to predict the final output.
Languages Used
- R, Python, Matlab, CPP, Java, Julia, Lisp, JavaScript, etc.
Advantages and Disadvantages
- Advantages: Best in-class performance, reduces need for feature engineering, eliminates unnecessary costs, identifies defects easily.
- Disadvantages: Requires a large amount of data.
