Fundamentals of Machine Learning and AI

Question 1

AI

Answer 1

AI is a broad field that encompasses the development of intelligent systems capable of performing tasks that typically require human intelligence, such as perception, reasoning, learning, problem-solving, and decision-making. AI serves as an umbrella term for various techniques and approaches, including machine learning, deep learning, and generative AI, among others.

Question 2

Machine Learning

Answer 2

ML is a type of AI for understanding and building methods that make it possible for machines to learn. These methods use data to improve computer performance on a set of tasks.

Question 3

Deep Learning

Answer 3

Deep learning uses the concept of neurons and synapses similar to how our brain is wired. An example of a deep learning application is Amazon Rekognition, which can analyze millions of images and streaming and stored videos within seconds.

Question 4

Gen AI

Answer 4

Generative AI is a subset of deep learning because it can adapt models built using deep learning, but without retraining or fine tuning.

Generative AI systems are capable of generating new data based on the patterns and structures learned from training data.

Question 5

Labeled Data

Answer 5

Labeled data is a dataset where each instance or example is accompanied by a label or target variable that represents the desired output or classification. These labels are typically provided by human experts or obtained through a reliable process.

Question 6

Unlabled Data

Answer 6

Unlabeled data is a dataset where the instances or examples do not have any associated labels or target variables. The data consists only of input features, without any corresponding output or classification.

Question 7

ML learning process is traditionally divided into three broad categories:

Answer 7

supervised learning, unsupervised learning, and reinforcement learning.

Question 8

supervised learning,

Answer 8

the algorithms are trained on labeled data. The goal is to learn a mapping function that can predict the output for new, unseen input data.

Question 9

Unsupervised learning

Answer 9

refers to algorithms that learn from unlabeled data. The goal is to discover inherent patterns, structures, or relationships within the input data.

Question 10

reinforcement learning,

Answer 10

the machine is given only a performance score as guidance and semi-supervised learning, where only a portion of training data is labeled. Feedback is provided in the form of rewards or penalties for its actions, and the machine learns from this feedback to improve its decision-making over time

Question 11

Inferencing

Answer 11

After the model has been trained, it is time to begin the process of using the information that a model has learned to make predictions or decisions

Question 12

Batch INferencing

Answer 12

Batch inferencing is when the computer takes a large amount of data, such as images or text, and analyzes it all at once to provide a set of results. This type of inferencing is often used for tasks like data analysis, where the speed of the decision-making process is not as crucial as the accuracy of the results.

Question 13

Real-time inferencing

Answer 13

Real-time inferencing is when the computer has to make decisions quickly, in response to new information as it comes in. This is important for applications where immediate decision-making is critical, such as in chatbots or self-driving cars. The computer has to process the incoming data and make a decision almost instantaneously, without taking the time to analyze a large dataset.

Question 14

Deep Learning

Answer 14

deep learning is inspired by the structure and function of the brain. It involves the use of artificial neural networks, which are computational models that are designed to mimic the way the human brain processes information.

Question 15

Neural Networks

Answer 15

neural networks have lots of tiny units called nodes that are connected together. These nodes are organized into layers. The layers include an input layer, one or more hidden layers, and an output layer.

Question 16

Computer VIsion

Answer 16

Deep learning has revolutionized computer vision by providing powerful techniques for tasks such as image classification, object detection, and image segmentation.

Question 17

NLP

Answer 17

Deep learning has made significant strides in NLP, making possible tasks such as text classification, sentiment analysis, machine translation, and language generation.

Question 18

Self-supervised learning

Answer 18

Although traditional ML models rely on supervised, unsupervised, or reinforcement learning patterns, FMs are typically pre-trained through self-supervised learning. With self-supervised learning, labeled examples are not required. Self-supervised learning makes use of the structure within the data to autogenerate labels.

Question 19

FM initial pre-training stage,

Answer 19

the FM’s algorithm can learn the meaning, context, and relationship of the words in the datasets.

Question 20

continuous pre-training

Answer 20

After the initial pre-training, the model can be further pre-trained on additional data. This is known as continuous pre-training. The goal is to expand the model’s knowledge base and improve its ability to understand and generalize across different domains or tasks.

Question 21

FM Optimization

Answer 21

Pre-trained language models can be optimized through techniques like prompt engineering, retrieval-augmented generation (RAG), and fine-tuning on task-specific data.

Question 22

Embeddings

Answer 22

Embeddings are numerical representations of tokens, where each token is assigned a vector (a list of numbers) that captures its meaning and relationships with other tokens.

Question 23

For example, the embedding vector for the token “cat” might be close to the vectors for “feline” and “kitten” in the embedding space, indicating that they are semantically related.

Question 24

Forward diffusion

Answer 24

Using forward diffusion, the system gradually introduces a small amount of noise to an input image until only the noise is left over.

Question 25

Reverse difusion

Answer 25

In the subsequent reverse diffusion step, the noisy image is gradually introduced to denoising until a new image is generated.

Question 26

Diffusion models learn through a two-step process of

Answer 26

forward diffusion and reverse diffusion.

Question 27

Generative adversarial networks (GANs)
–

Answer 27

GANs are a type of generative model that involves two neural networks competing against each other in a zero-sum game framework. The two networks are generator and discriminator.

During training, the generator tries to generate data that can fool the discriminator into thinking it’s real, while the discriminator tries to correctly classify the real and generated data. This adversarial process continues until the generator produces data that is indistinguishable from the real data.

Question 28

Variational autoencoders (VAEs)
–

Answer 28

VAEs are a type of generative model that combines ideas from autoencoders (a type of neural network) and variational inference (a technique from Bayesian statistics). In a VAE, the model consists of two parts:

Encoder: This neural network takes the input data (for example, an image) and maps it to a lower-dimensional latent space, which captures the essential features of the data.

Decoder: This neural network takes the latent representation from the encoder and generates a reconstruction of the original input data.

The key aspect of VAEs is that the latent space is encouraged to follow a specific probability distribution (usually a Gaussian distribution), which allows for generating new data by sampling from this latent space and passing the samples through the decoder.

Question 29

Fine tuning

Answer 29

Fine-tuning is a supervised learning process that involves taking a pre-trained model and adding specific, smaller datasets. Adding these narrower datasets modifies the weights of the data to better align with the task.

Question 30

There are two ways to fine-tune a model

Answer 30

Instruction fine-tuning uses examples of how the model should respond to a specific instruction. Prompt tuning is a type of instruction fine-tuning.

*
Reinforcement learning from human feedback (RLHF) provides human feedback data, resulting in a model that is better aligned with human preferences.

Question 31

Difference between fine-utning and RAG

Answer 31

However, rather than having to fine-tune an FM with a small set of labeled examples, RAG retrieves a small set of relevant documents and uses that to provide context to answer the user prompt. RAG will not change the weights of the foundation model, whereas fine-tuning will change model weights.

Question 32

Amazon Kendra

Answer 32

Amazon Kendra is an intelligent search service powered by ML. Amazon Kendra reimagines enterprise search for your websites and applications. Your employees and customers can conveniently find the content that they are looking for, even when it’s scattered across multiple locations and content repositories within your organization.