AI-by-Hand-exercises-Tom-Yeh

Excel exercises done following posts from Professor Tom Yeh (IG: @ProfTomYeh)

1_LSTM (Long short-term memory)

Artificial recurrent neural network architecture used in deep learning

https://x.com/ProfTomYeh/status/1806285518126317877

Exercise: LSTM_Tom Yeh.xlsx

2_Superposition

Superposition enables learning of many tasks within a single neural network. The method relies on learning different tasks in nearly orthogonal spaces, which mitigates model forgetting

https://x.com/ProfTomYeh/status/1802839647057523158

Exercise: Superpositon_Tom Yeh.xlsx

3_Vector database

"At its core, a vector database is a purpose-built system designed for the storage and retrieval of vector data. In this context, a vector refers to an ordered set of numerical values that could represent anything from spatial coordinates to feature attributes, such as the case for machine learning and data science use cases where vectors are often used to represent the features of objects. The vector database can efficiently store and retrieve these feature vectors."

https://x.com/ProfTomYeh/status/1795076707386360227

Exercise: Vector database_Tom Yeh.xlsx

4_U-Net

U-Net is a convolutional neural network that was developed for biomedical image segmentation at the Computer Science Department of the University of Freiburg.[1] The network is based on a fully convolutional neural network[2] whose architecture was modified and extended to work with fewer training images and to yield more precise segmentation. Segmentation of a 512 × 512 image takes less than a second on a modern (2015) GPU using the U-Net architecture.[1]

The U-Net architecture has also been employed in diffusion models for iterative image denoising.[3] This technology underlies many modern image generation models, such as DALL-E, Midjourney, and Stable Diffusion.

https://x.com/ProfTomYeh/status/1786064644982948106

Exercise:

5_GAN (Generative adversarial network)

A generative adversarial network (GAN) is a class of machine learning frameworks and a prominent framework for approaching generative AI.[1][2] The concept was initially developed by Ian Goodfellow and his colleagues in June 2014.[3] In a GAN, two neural networks contest with each other in the form of a zero-sum game, where one agent's gain is another agent's loss.

Given a training set, this technique learns to generate new data with the same statistics as the training set. For example, a GAN trained on photographs can generate new photographs that look at least superficially authentic to human observers, having many realistic characteristics. Though originally proposed as a form of generative model for unsupervised learning, GANs have also proved useful for semi-supervised learning,[4] fully supervised learning,[5] and reinforcement learning.[6]

The core idea of a GAN is based on the "indirect" training through the discriminator, another neural network that can tell how "realistic" the input seems, which itself is also being updated dynamically.[7] This means that the generator is not trained to minimize the distance to a specific image, but rather to fool the discriminator. This enables the model to learn in an unsupervised manner.

GANs are similar to mimicry in evolutionary biology, with an evolutionary arms race between both networks.

https://x.com/ProfTomYeh/status/1786124446303989880

Exercise:

6_Backpropagation

In machine learning, backpropagation is a gradient estimation method used to train neural network models. The gradient estimate is used by the optimization algorithm to compute the network parameter updates.

It is an efficient application of the chain rule to neural networks.[1] It is also known as the reverse mode of automatic differentiation or reverse accumulation, due to Seppo Linnainmaa (1970).[2][3][4][5][6][7][8] The term "back-propagating error correction" was introduced in 1962 by Frank Rosenblatt,[9][1] but he did not know how to implement this, even though Henry J. Kelley had a continuous precursor of backpropagation[10] already in 1960 in the context of control theory.[1]

Backpropagation computes the gradient of a loss function with respect to the weights of the network for a single input–output example, and does so efficiently, computing the gradient one layer at a time, iterating backward from the last layer to avoid redundant calculations of intermediate terms in the chain rule; this can be derived through dynamic programming.[10][11][12] Gradient descent, or variants such as stochastic gradient descent,[13] are commonly used.

https://x.com/ProfTomYeh/status/1787874286813733108

Exercise: Backpropagation_Tom Yeh.xlsx

Exercise: Backpropagation_Tom Yeh - 2.xlsx

7_RRN (Recurrent Neural Network)

A recurrent neural network (RNN) is one of the two broad types of artificial neural network, characterized by direction of the flow of information between its layers. In contrast to the uni-directional feedforward neural network, it is a bi-directional artificial neural network, meaning that it allows the output from some nodes to affect subsequent input to the same nodes. Their ability to use internal state (memory) to process arbitrary sequences of inputs[1][2][3] makes them applicable to tasks such as unsegmented, connected handwriting recognition[4] or speech recognition.[5][6]

The term "recurrent neural network" is used to refer to the class of networks with an infinite impulse response, whereas "convolutional neural network" refers to the class of finite impulse response. Both classes of networks exhibit temporal dynamic behavior.[7] A finite impulse recurrent network is a directed acyclic graph that can be unrolled and replaced with a strictly feedforward neural network, while an infinite impulse recurrent network is a directed cyclic graph that cannot be unrolled.

Additional stored states and the storage under direct control by the network can be added to both infinite-impulse and finite-impulse networks. Another network or graph can also replace the storage if that incorporates time delays or has feedback loops. Such controlled states are referred to as gated states or gated memory and are part of long short-term memory networks (LSTMs) and gated recurrent units. This is also called Feedback Neural Network (FNN). Recurrent neural networks are theoretically Turing complete and can run arbitrary programs to process arbitrary sequences of inputs.[8]

https://x.com/ProfTomYeh/status/1787906449227554917

Exercise:

8_Autoencoder

An autoencoder is a type of artificial neural network used to learn efficient codings of unlabeled data (unsupervised learning).[1][2] An autoencoder learns two functions: an encoding function that transforms the input data, and a decoding function that recreates the input data from the encoded representation. The autoencoder learns an efficient representation (encoding) for a set of data, typically for dimensionality reduction.

Variants exist, aiming to force the learned representations to assume useful properties.[3] Examples are regularized autoencoders (Sparse, Denoising and Contractive), which are effective in learning representations for subsequent classification tasks,[4] and Variational autoencoders, with applications as generative models.[5] Autoencoders are applied to many problems, including facial recognition,[6] feature detection,[7] anomaly detection and acquiring the meaning of words.[8][9] Autoencoders are also generative models which can randomly generate new data that is similar to the input data (training data).[7]

https://x.com/ProfTomYeh/status/1788238035080900904

Exercise:

9_Variational Auto Encoder

In machine learning, a variational autoencoder (VAE) is an artificial neural network architecture introduced by Diederik P. Kingma and Max Welling.[1] It is part of the families of probabilistic graphical models and variational Bayesian methods.[2]

In addition to being seen as an autoencoder neural network architecture, variational autoencoders can also be studied within the mathematical formulation of variational Bayesian methods, connecting a neural encoder network to its decoder through a probabilistic latent space (for example, as a multivariate Gaussian distribution) that corresponds to the parameters of a variational distribution.

Thus, the encoder maps each point (such as an image) from a large complex dataset into a distribution within the latent space, rather than to a single point in that space. The decoder has the opposite function, which is to map from the latent space to the input space, again according to a distribution (although in practice, noise is rarely added during the decoding stage). By mapping a point to a distribution instead of a single point, the network can avoid overfitting the training data. Both networks are typically trained together with the usage of the reparameterization trick, although the variance of the noise model can be learned separately.[citation needed]

Although this type of model was initially designed for unsupervised learning,[3][4] its effectiveness has been proven for semi-supervised learning[5][6] and supervised learning.[7]

https://x.com/ProfTomYeh/status/1788299547355021719

Exercise:

10_Transformer

A transformer is a deep learning architecture developed by Google and based on the multi-head attention mechanism, proposed in a 2017 paper "Attention Is All You Need".[1] Text is converted to numerical representations called tokens, and each token is converted into a vector via looking up from a word embedding table.[1] At each layer, each token is then contextualized within the scope of the context window with other (unmasked) tokens via a parallel multi-head attention mechanism allowing the signal for key tokens to be amplified and less important tokens to be diminished. The transformer paper, published in 2017, is based on the softmax-based attention mechanism proposed by Bahdanau et. al. in 2014 for machine translation,[2][3] and the Fast Weight Controller, similar to a transformer, proposed in 1992.[4][5][6]

Transformers have the advantage of having no recurrent units, and therefore require less training time than earlier recurrent neural architectures such as long short-term memory (LSTM).[7] Later variations have been widely adopted for training large language models (LLM) on large (language) datasets, such as the Wikipedia corpus and Common Crawl.[8]

This architecture is now used not only in natural language processing and computer vision,[citation needed] but also in audio,[9] multi-modal processing and robotics.[10] It has also led to the development of pre-trained systems, such as generative pre-trained transformers (GPTs)[11] and BERT[12] (Bidirectional Encoder Representations from Transformers).

https://x.com/ProfTomYeh/status/1794702829103309291

Exercise: Transformers_Tom Yeh.xlsx

11_SORA

SORA is OpenAI's text-to-video generative AI model. That means you write a text prompt, and it creates a video that matches the description of the prompt.

SORA is a diffusion model that starts with a video that resembles static noise. Over many steps, the output gradually transforms by removing the noise. By providing the model with the foresight of multiple frames concurrently, OpenAI has resolved the complex issue of maintaining subject consistency, even when it momentarily disappears from view.

It is named for the Japanese word “sky,” which the company said was to show its "limitless creative potential.

https://x.com/ProfTomYeh/status/1795449683285848509

Exercise:

12_RLHF (Reinforcement learning from human feedback)

In machine learning, reinforcement learning from human feedback (RLHF) is a technique to align an intelligent agent to human preferences. In classical reinforcement learning, the goal of such an agent is to learn a function that guides its behavior called a policy. This function learns to maximize the reward it receives from a separate reward function based on its task performance.[1] However, it is difficult to define explicitly a reward function that approximates human preferences. Therefore, RLHF seeks to train a "reward model" directly from human feedback.[2] The reward model is first trained in a supervised fashion—independently from the policy being optimized—to predict if a response to a given prompt is good (high reward) or bad (low reward) based on ranking data collected from human annotators. This model is then used as a reward function to improve an agent's policy through an optimization algorithm like proximal policy optimization.[3]

RLHF has applications in various domains in machine learning, including natural language processing tasks such as text summarization and conversational agents, computer vision tasks like text-to-image models, and the development of video game bots. While RLHF is an effective method of training models to act better in accordance with human preferences, it also faces challenges due to the way the human preference data is collected. Though RLHF does not require massive amounts of data to improve performance, sourcing high-quality preference data is still an expensive process. Furthermore, if the data is not carefully collected from a representative sample, the resulting model may exhibit unwanted biases.

https://x.com/ProfTomYeh/status/1795803665845899511

Exercise:

13_Self Attention

The machine learning-based attention method simulates how human attention works by assigning varying levels of importance to different words in a sentence. It assigns importance to each word by calculating "soft" weights for the word's numerical representation, known as its embedding, within a specific section of the sentence called the context window to determine its importance

Self-attention, also known as scaled dot-product attention, is a fundamental concept in the field of NLP and deep learning. It plays a pivotal role in tasks such as machine translation, text summarization, and sentiment analysis. Self-attention enables models to weigh the importance of different parts of an input sequence when making predictions or capturing dependencies between words

https://x.com/ProfTomYeh/status/1797249951325434315

Exercise: Self-attention_Tom Yeh.xlsx

Exercise 2: Self Attention.xlsx

14_Discrete Fourier Transform

The discrete Fourier transform, or DFT, is the primary tool of digital signal processing. The math involved is extremely complex, involving a summation over a complex number term e^(-iwt). This exercise from Prof. Tom Yeh demonstrate this complexity.

https://x.com/ProfTomYeh/status/1805694219031662799

Exersise: DFT_Tom Yeh.xlsx

15_Feed-forward and Back-propagation

Two fundamental algorithms, Feed-forward and Back-propagation, that enable the working of a Neural Network.

A feedforward network consists of three types of layers:

1. Input Layer: This layer receives the raw input data, which could be features extracted from an image, text, or any other form of data.
2. Hidden Layers: These intermediate layers process the input data using weights and activation functions. Hidden layers allow the network to learn complex patterns and representations.
3. Output Layer: The final layer produces the network’s output, which could be predictions for classification tasks or continuous values for regression tasks

The backpropagation algorithm involves two main phases:

1. Forward Pass: During the forward pass, input data is propagated through the network, and the network produces a prediction. Each layer’s outputs are computed by applying weights and activation functions.
2. Backward Pass: In the backward pass, the network calculates the gradients of the loss function with respect to the network’s weights and biases. These gradients indicate the direction and magnitude of adjustments needed to minimize the loss. The network then updates its weights and biases using optimization algorithms like gradient descent

Demystifying Feed-forward and Back-propagation using MS Excel. Gaurav Gupta. Towards Data Sicence. 12 Feb 2019 Backpropagation vs. Feedforward Networks. Syed Wahad. TechKluster.

https://towardsdatascience.com/demystifying-feed-forward-and-back-propagation-using-ms-excel-30f5aeefcfc7

Exercise: FF & Back propagation.xlsx

16_Batch Normalization

Batch normalization is a common practice to improve training and achieve faster convergence. "Batch normalization is a technique for training very deep neural networks that standardizes the inputs to a layer for each mini-batch. This has the effect of stabilizing the learning process and dramatically reducing the number of training epochs required to train deep networks." A Gentle Introduction to Batch Normalization for Deep Neural Networks. Jason Brownlee. 4 Dec 2019. Deep Learning Performance

https://x.com/ProfTomYeh/status/1830941593269870715

Exercise: Batch normalization_Tom Yeh.xlsx

17_Dropout

“Dropout” in machine learning refers to the process of randomly ignoring certain nodes in a layer during training. Dropout is used as a regularization technique — it prevents overfitting by ensuring that no units are codependent. Dropout is a simple yet effective way of reducing overfitting and improving generalization

Dropout is definitely not the only option to combat overfitting. Common regularization techniques include:

1. Early stopping: stop training automatically when a specific performance measure (eg. Validation loss, accuracy) stops improving
2. Weight decay: incentivize the network to use smaller weights by adding a penalty to the loss function (this ensures that the norms of the weights are relatively evenly distributed amongst all the weights in the networks, which prevents just a few weights from heavily influencing network output)
3. Noise: allow some random fluctuations in the data through augmentation (which makes the network robust to a larger distribution of inputs and hence improves generalization)
4. Model combination: average the outputs of separately trained neural networks (requires a lot of computational power, data, and time) Despite the plethora of alternatives, dropout remains an extremely popular protective measure against overfitting because of its efficiency and effectiveness A Simple Introduction to Dropout Regularization (With Code!). Nisha McNealis. 23 Apr 2020. Analytics Vidhya

https://x.com/ProfTomYeh/status/1836044959020900392

Exercise: Dropout_Tom Yeh.xlsx