1 what is deep learning what can deep learning do 2 compared...

Name: 1 what is deep learning what can deep learning do 2 compared to traditional machine learning what is the most prominent difference of deep learning 3 list and briefly explain different learn 07948
Uploaded: 2023-08-11T20:32:19-08:00
Duration: 6 min 3 s
Channel: Varun Indurthi
Description: 1 what is deep learning what can deep learning do 2 compared to traditional machine learning what is the most prominent difference of deep learning 3 list and briefly explain different learn 07948

1. What is deep learning? What can deep learning do? 2. Compared to traditional machine learning, what is the most prominent difference of deep learning? 3. List and briefly explain different learning methods in AI. 4. What is representation learning, and how does it relate to deep learning?

Transcript

00:01 Let us answer the following deep learning questions.

00:04 The first question says which of the following statements correctly represents the concept of a neuron? so a neuron by design has multiple inputs but a single output only because the answer is b.

00:22 The explanation is a neuron in a neural network typically has multiple inputs each associated with a weight and computes a weighted sum of these inputs.

00:34 This sum is then passed through an activation function to produce a single output.

00:41 Now moving on to the second question, once a data set's dimensionality has been reduced is it possible to reverse the operation? if so, how? once a data set's dimensionality has been reduced it is usually possible to perfectly reverse.

00:57 So for the first point it is possible and it is possible to do it perfectly.

01:04 So dimensionality reduction techniques like pca involve transforming the data to a lower dimensional space and during this process some information is lost.

01:18 The loss of information makes it challenging to fully reverse the transformation and recover the original high dimensional data.

01:27 So it is possible but not perfectly possible because of the loss of the information.

01:36 Now the second part says can pca be used to reduce the dimensionality of a highly nonlinear data set? so pca is usually effective for linear data transformations and might not work well for highly nonlinear data sets.

01:53 So it is more suitable for capturing variance in the data along the principal axis which might not align with the underlying structure of a highly nonlinear data set.

02:04 For nonlinear data set other techniques like t -sne or auto encoders might be more appropriate.

02:13 Let us go with the chaining question after this.

02:18 So to summarize, not possible for nonlinear data.

02:29 Pca is not possible for nonlinear data.

02:31 Now does it make sense to chain two different dimensionality reduction algorithms? chaining two different dimensionality reduction algorithms can make sense in certain cases.

02:43 However, it is important to consider the potential interaction between the algorithms and whether they are aligned with the goals of analysis.

02:54 So it is possible but situational...

Question

1. What is deep learning? What can deep learning do? 2. Compared to traditional machine learning, what is the most prominent difference of deep learning? 3. List and briefly explain different learning methods in AI. 4. What is representation learning, and how does it relate to deep learning?

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