Coding Deep Learning Optimizers From Scratch Academic Homepage

🔗 Notebook Link: Coding Deep learning optimizers from scratch This project explores the optimization algorithms that are critical for training deep learning models. It includes: Non-Convex Optimization from both mathematical and practical perspective: SGD, SGDMomentum, AdaGrad, RMSprop, and Adam in Python This article will provide the short mathematical expressions of common non-convex optimizers and their Python implementations from scratch. Understanding the math behind these optimization algorithms will enlighten your perspective when training complex machine learning models.

The structure of this article will be as follows. First, I will talk about the particular optimization algorithm; then, I will give the mathematical formula and provide the Python code. All algorithms are implemented by pure NumPy. Here are the non-convex optimization algorithms that we will discuss Let’s start with the simplest one, Stochastic Gradient Descent. SGD is an iterative, non-convex, and first-order optimization algorithm over the differentiable error surfaces.

It is a stochastic estimation of gradient descent, in which the training data is randomized. It is a computationally-stable and mathematically well-established optimization algorithm. The intuition behind SGD is that we take the partial derivative of the objective function with respect to the parameter we can to optimize, which yields its gradient, which shows the increasing direction of... Hence, we take the negative of that gradient, to step forward where the loss is not increasing. To ensure stable and less-oscillatory optimization, we introduce the learning rate parameter ŋ then multiply the gradient with ŋ. Finally, the obtained value is subtracted from the parameter that we can optimize in an iterative fashion.

Here is the SGD update formula and Python Code. In the context of SGD, instead of computing the exact derivate of our loss function, we’re approximating it on small batches in an iterative fashion. Hence, it is not certain that the model learns in a direction where the loss is minimized. To propose more stable, direction-aware, and fast learning, we introduce SGDMomentum that determines the next update as a linear combination of the gradient and the previous update. Hence, it takes into account the previous updates also. There was an error while loading.

Please reload this page. There was an error while loading. Please reload this page. Implementing a deep learning framework from scratch in Python is a complex task that requires a deep understanding of computer science, mathematics, and software engineering. However, building a framework from scratch allows for customization, optimization, and a deeper understanding of the underlying algorithms and techniques. In this tutorial, we will guide you through the process of implementing a basic deep learning framework in Python, covering the core concepts, implementation guide, code examples, best practices, testing and debugging, and optimization.

By the end of this tutorial, you will have a solid understanding of the following: Before starting this tutorial, you should have a good understanding of: Implementing a deep learning framework from scratch in Python requires a deep understanding of computer science, mathematics, and software engineering. By following this tutorial, you have learned the core concepts, implementation guide, code examples, best practices, testing and debugging, and optimization techniques. Remember to always follow best practices and optimize your code for performance and security. Happy coding!

Note: This tutorial is a comprehensive guide to implementing a deep learning framework in Python. However, it is not a substitute for formal education and training in deep learning and computer science. Deep learning optimization algorithms, like Gradient Descent, SGD, and Adam, are essential for training neural networks by minimizing loss functions. Despite their importance, they often feel like black boxes. This guide simplifies these algorithms, offering clear explanations and practical insights Gradient descent is one of the most popular algorithms to perform optimization and the de facto method to optimize neural networks.

Every state-of-the-art deep learning library contains implementations of various algorithms to improve on vanilla gradient descent. These algorithms, however, are often used as black-box optimizers, as practical explanations are hard to come by. This article aims at providing the reader with intuitions with regard to the behaviour of different algorithms for optimizing gradient descent. Taking a step back: Gradient descent is a way to minimize an objective function $J(\theta)$ parameterized by a model's parameters $\theta \in \mathbb{R}^d$ by updating the parameters in the opposite direction of the gradient... to the parameters. The learning rate $\eta$ determines the size of the steps we take to reach a (local) minimum.

In other words, we follow the direction of the slope of the surface created by the objective function downhill until we reach a valley. This article discuss various methods to improve on this "blind" stepwise approach to following the slope. Feel free to review previous articles if you need to brush up on computing partial derivatives, the gradient, gradient descent, regularization, and automatic differentiation (part1 and part 2). It is needless to say how relevant machine learning frameworks are for research and industry. Due to their extensibility and flexibility, it is rare to find a project that does not use Google TensorFlow or Meta PyTorch nowadays. It may seem counter-intuitive to spend time coding machine learning algorithms from scratch without any base framework.

However, it is not. Coding the algorithms ourselves provides a clear and solid understanding of how the algorithms work and what the models are really doing. In this series, we will learn how to code the must-to-know deep learning algorithms such as convolutions, backpropagation, activation functions, optimizers, deep neural networks, and so on, using only plain and modern C++. We will begin our journey in this story by learning modern C++ language features and relevant programming details to code deep learning and machine learning models. What I cannot create, I do not understand. — Richard Feynman

This project implements an OCR pipeline using TrOCR (Transformer OCR), a state-of-the-art model for recognizing text in images. The workflow includes dataset preparation, model fine-tuning, and evaluation using Hugging Face’s transformers library. The pipeline is optimized for low-resolution images and supports augmentation techniques to enhance training. It achieves high accuracy in extracting structured information from images, such as weights, volumes, and other product details. This project implements an OCR-based entity extraction pipeline to process product images, extract text, and map it to structured data fields. It leverages PaddleOCR for text recognition and BERT for fine-tuned entity mapping.

The pipeline handles large-scale datasets, performs preprocessing, and applies machine learning techniques for accurate predictions of entity values such as weight, dimensions, and voltage. This project demonstrates the concept of adversarial attacks on neural networks using GoogLeNet. By introducing small, imperceptible perturbations to input images, the model’s predictions are intentionally altered, showcasing vulnerabilities in deep learning models. The pipeline includes loading pretrained models, performing adversarial attacks, and visualizing the impact of perturbations on predictions. This project provides a comprehensive exploration of optimization algorithms used in deep learning, focusing on their implementation both from scratch and using Keras. It covers popular optimizers such as Gradient Descent, Momentum, Adam, RMSProp, and others, explaining their mathematical foundations, challenges, and practical applications.

The notebook also demonstrates how these optimizers adjust model parameters to minimize loss functions, using examples with synthetic data and neural networks. This Jupyter Notebook provides a comprehensive implementation of machine learning algorithms from scratch using only NumPy and Pandas. It covers a wide range of algorithms, starting from basic regression models like Linear Regression to advanced techniques such as Gradient Descent, K-Means Clustering, Decision Trees, and Random Forests. The notebook emphasizes understanding the mathematical intuition behind each algorithm and coding them step-by-step without relying on external libraries like Sklearn This is a collection of simple PyTorch implementations of neural networks and related algorithms. These implementations are documented with explanations,

The website renders these as side-by-side formatted notes. We believe these would help you understand these algorithms better. We are actively maintaining this repo and adding new implementations almost weekly. for updates. Solving games with incomplete information such as poker with CFR.

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