Johns Hopkins University via Coursera
The course "Mastering Neural Networks and Model Regularization" dives deep into the fundamentals and advanced techniques of neural networks, from understanding perceptron-based models to implementing cutting-edge convolutional neural networks (CNNs). This course offers hands-on experience with real-world datasets, such as MNIST, and focuses on practical applications using the PyTorch framework. Learners will explore key regularization techniques like L1, L2, and drop-out to reduce model overfitting, as well as decision tree pruning.
What makes this course unique is its emphasis on building neural networks from scratch, allowing learners to grasp the intricate details of model design and training. Additionally, the course covers computational graphs, activation and loss functions, and how to efficiently utilize GPUs for faster computation. Learners will also delve into CNNs for image and audio processing, gaining insights into cutting-edge applications in these fields.
By completing this course, learners will develop advanced skills in neural network design, model regularization, and the use of PyTorch for deep learning tasks—empowering them to tackle complex machine learning challenges with confidence.
This course provides a comprehensive introduction to neural networks, focusing on the perceptron model, regularization techniques, and practical implementation using PyTorch. Students will build and evaluate neural networks, including convolutional architectures for image processing and audio signa…
In this module, you will learn about the fundamental concepts in neural networks, covering the perceptron model, model parameters, and the back-propagation algorithm. You'll also learn to implement a neural network from scratch and apply it to classify MNIST images, evaluating performance against s…
In this module, you'll delve into techniques to enhance machine learning model performance and generalization. You'll grasp the necessity of regularization to mitigate overfitting, compare L1 and L2 regularization methods, understand decision tree pruning, explore dropout regularization in neural n…
In this module, you'll cover essential concepts and practical skills in deep learning using PyTorch. You'll also learn computational graphs in supervised learning, create and manipulate tensors in PyTorch, compare activation and loss functions, learn implementation steps and library functions for n…
In this module, you'll focus on advanced applications of convolutional neural networks (CNNs) using PyTorch. You'll also learn to implement CNN filters, compare different CNN architectures, develop models for image processing tasks in PyTorch, and explore techniques for modeling audio time signals…
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Johns Hopkins University is in Tier 1: world-leading universities and India's top institutes of our institution ranking (93/100).
This course provides an introduction to mathematical modeling of computational problems. It covers the common algorithms, algorithmic paradigms, and data structures used to solve these problems. The course emphasizes the relationship between algorithms and programming, and introduces basic performa…
Led by Andrew Ng, this course provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (cluster…
Instructor: Prof. Erik Demaine, Dr. Jason Ku, Prof. Justin Solomon View the complete course: https://ocw.mit.edu/6-006S20 YouTube Playlist: https://www.youtube.com/playlist?list=PLUl4u3cNGP63EdVPNLG3ToM6LaEUuStEY This course is an introduction to mathematical modeling of computational problems, as…
This course teaches techniques for the design and analysis of efficient algorithms, emphasizing methods useful in practice. Topics covered include: sorting; search trees, heaps, and hashing; divide-and-conquer; dynamic programming; amortized analysis; graph algorithms; shortest paths; network flow;…
This course explores the world of Transformers and Large Language Models (LLMs). You will learn the evolution of NLP methods, the core components of the Transformer architecture, along with how they relate to LLMs as well as techniques to enhance model performance for real-world applications. Throu…
Dive into the intriguing world of AI Transformers with this curated YouTube playlist. Featuring expert insights, educational content from leading organizations, and engaging discussions, this playlist keeps you informed about the latest breakthroughs in Transformer technology.
