Our Mission
The field of Machine Learning can be overwhelming, and you might feel like you're losing control. Instead of giving up, let me share a secret with you: it's easier than you think. All you need is a "Rosetta Stone" to fully accelerate your work with machine learning.
This book takes you on an alternative route, starting with the fundamental concepts from calculus, linear algebra, numerical methods, and optimization, leading up to the state-of-the-art algorithms that have emerged over the last couple of decades.
This is an ambitious promise, but my background in industry, research, and teaching has given me deep insights into the common challenges of developing efficient algorithms for prediction. This book will focus on the coding perspective—a "from-scratch" approach where the configuration includes setting up the environment properly, defining the dataset, and configuring training and validation. Moreover, this book will dedicate significant effort to eliminating version-related errors that could cause problems when maintaining the code for future development.
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Numerical Methods
Numerical methods are mathematical techniques used to approximate solutions to complex problems that cannot be solved analytically. These methods are essential in fields like engineering, physics, and computer science. One common approach is gradient-based methods, which are used to find the minimum or maximum of a function. In these methods, the gradient (or slope) of the function is calculated, and iterative steps are taken in the direction of the steepest descent (for minimization) or ascent (for maximization) to converge on an optimal solution. Gradient-based methods are widely used in optimization problems, including machine learning and neural networks.
Neural Networks
This chapter covers neural networks, exploring their structure and function. It explains how interconnected layers of neurons process data and learn patterns through training, forming the foundation of modern machine learning models.
Resnet
This chapter explores ResNet (Residual Networks), a deep learning architecture designed to overcome the vanishing gradient problem in neural networks. It introduces the concept of residual connections, which allow for more efficient training of very deep models by skipping layers and enabling better gradient flow.
Autograd
This chapter introduces Autograd, an automatic differentiation tool used in machine learning. It explains how AutoGrad computes gradients automatically during the training of neural networks, streamlining the optimization process by enabling efficient backpropagation and gradient-based learning.
CNN
This chapter delves into Convolutional Neural Networks (CNNs), a deep learning model designed for processing grid-like data, such as images. It explains how CNNs use convolutional layers to automatically detect patterns and features, making them highly effective for tasks like image recognition and classification.
Transformers
This chapter focuses on Transformers, a powerful model architecture used primarily for sequence-based tasks, such as natural language processing. It explains the self-attention mechanism that allows Transformers to capture relationships within data without relying on sequence order, enabling them to process text more efficiently and accurately.
Generative Adversarial
This chapter explores Generative Adversarial Networks (GANs), a model architecture consisting of two neural networks, a generator and a discriminator, that compete against each other. It explains how GANs generate realistic data, such as images or videos, by training the generator to create outputs indistinguishable from real data.
PCA-SVM Case
Study
This chapter explores the combined use of Principal Component Analysis (PCA) and Support Vector Machines (SVM) in a real-world case study. It demonstrates how PCA is applied to reduce the dimensionality of large datasets, simplifying the data while retaining key features. The reduced data is then classified using SVM, which identifies optimal decision boundaries for separating categories. Together, PCA and SVM provide an efficient and powerful approach to solving complex classification problems.
Fully Convolutional Neural Net
This chapter explores Fully Convolutional Networks (FCNs), a specialized deep learning architecture designed for tasks like image segmentation. Unlike traditional convolutional networks, FCNs replace fully connected layers with convolutional ones, allowing them to produce spatially dense predictions. FCNs are highly effective in pixel-level tasks, such as identifying objects within images and segmenting them accurately.
Examples book 1.
The chapter on fully connected neural networks marks the beginning of the exploration into advanced machine learning models.
The chapter on fully connected neural networks marks the beginning of the exploration into advanced machine learning models. It starts with a schematic overview of the forward layer and backpropagation processes. To ensure a comprehensive understanding, the book provides a complete code implementation, along with detailed explanations, demonstrating how concepts from linear algebra are applied in practice
Examples book 2.
The original Transformer model serves as the foundation for all generative large language models. While its applications may seem overwhelming, it is crucial to understand its design to drive future advancements.
This section will delve into the concept of positional encoding, exploring its role in handling the sequential nature of data in transformer models. We will also examine the modular design of the original Transformer model, highlighting how its components work together to process information efficiently. Finally, we will provide an in-depth look at the attention mechanism, the core of the Transformer architecture, and explain how it enables the model to focus on relevant parts of the input sequence.
Examples book 3.
"Style Transfer - VGG19: Style transfer, popularized by models like VGG19, helped bring AI to public attention. While it may seem limited to visual applications, its impact goes beyond, paving the way for more advanced models capable of generating innovative images for use in advertising, CGI in movies, and other creative industries."
VGG19 is one of the early convolutional neural networks used in style transfer, a technique that blends the artistic style of one image with the content of another. Style transfer caught public attention and contributed to the growing interest in AI-generated visual art, even though its direct applications might seem niche. However, its underlying technology has influenced the development of more complex generative models, used in various industries like entertainment and marketing.
The original Dog
A few decades ago, it would have been impossible to generate a painted picture of a beloved dog, styled to mimic the work of Vincent van Gogh. Today, this is achievable thanks to advancements in AI.
The Original Masterpiece
The style of a painting is used as input alongside the original photo.
The Starry Dog
Now, with AI, everything seems possible.
Positional Encoding
The mechanism behind positional encoding i s explained
The transformer block
The modular design of the transformer block is explained
The attention layer
The attention layer is explained through its key, query, and value components as fundamental building blocks.
Clear, Generic Code Examples for Application in Python and C/C++
The mathematical descriptions at the beginning of each chapter are seamlessly connected to code examples in a clear and easy-to-follow manner.
Structured Explanation of Code Segments Using Bullet Points and Descriptive Text
The accompanying Python code examples are explained step by step.
Complete Code Examples
The book includes complete Python code examples with provided data for both training and validation.