Neural Networks - Where Do I Start
Explore neural network architectures, from basic to cutting-edge, and learn how to apply them to real-world problems. Gain insights on getting started with neural networks in your projects.
Deep Learning with CNTK and F#
Explore deep learning with CNTK and F# in .NET, covering neural networks, model training, and practical applications in computer vision and language processing.
Writing a Neural Net from Scratch
Learn to build a neural network for image recognition from scratch in C#, covering key concepts like gradient descent and backpropagation without complex math or external libraries.
Neural Networks - A Primer
Explore neural networks' foundations, intuition, and applications in machine learning. Learn about their historical inspiration, mathematical concepts, and practical tools for developing AI solutions.
Artificial Intelligence - Our Journey Towards Singularity
Explore AI's journey towards singularity, covering machine learning, deep learning, image recognition, generative AI, and natural language processing. Gain insights into current progress and future challenges.
Neural Networks - A Primer
Explore neural networks' fundamentals, from biological inspiration to practical applications. Learn key concepts, mathematical foundations, and tools for developing machine learning solutions using neural networks.
A Practical Guide to Deep Learning
Practical introduction to computer vision and deep learning, covering neural network programming, training, and refinement techniques for future projects.
Recent Solutions Using Deep Learning Models - The How and Why
Explore recent deep learning applications across industries, examining how neural networks solve complex problems and generate predictions from massive datasets.
Writing a Neural Net from Scratch
Learn to code a neural network for image recognition in C# from scratch, covering gradient descent, activation functions, and backpropagation without complex math or external libraries.
ML BuzzWords Demystified
Demystifies latest ML trends like Reinforcement Learning, AutoML, and ML bias. Provides clear insights into complex concepts, ensuring you're up-to-date with the field's advancements.
Machine Learning - The Bare Math Behind Libraries
Explore the mathematical foundations of machine learning, from supervised to unsupervised learning techniques, to gain a deeper understanding of neural networks and their underlying processes.
A Practical Guide to Deep Learning
Practical guide to computer vision and deep learning, covering neural network programming, training, and refinement techniques for real-world projects.
Deep Learning with CNTK and F#
Explore deep learning with CNTK and F#, covering neural networks, model training, and practical applications in .NET. Learn core concepts and see live demonstrations using real-world datasets.
A Friendly Introduction to Recurrent Neural Networks
Explore how computers predict and generate sequences using Recurrent Neural Networks. Learn about vectors, neural networks, and practical applications in cooking, weather, and more.
EfficientNet Paper Walkthrough
Comprehensive walkthrough of the EfficientNet paper, exploring model scaling, proposed methods, and results. Offers insights into innovative approaches for improving neural network efficiency.
Building Our First Simple GAN
Learn to build a simple Generative Adversarial Network (GAN) using fully connected layers and train it on the MNIST dataset. Explore the architecture, setup, and training process for this fundamental deep learning model.
TensorFlow Tutorial - Neural Networks with Sequential and Functional API
Learn to build neural networks in TensorFlow 2.0 using Keras Sequential and Functional API. Covers model creation, layer feature extraction, and model summaries with hands-on MNIST dataset examples.
TensorFlow Tutorial - Tensor Basics
Comprehensive guide to essential tensor operations in TensorFlow 2.0, covering initialization, casting, math operations, indexing, and reshaping. Builds foundation for neural network development.
Pytorch Neural Network Example
Step-by-step guide to building a simple neural network using PyTorch, covering network creation, data handling, loss calculation, and accuracy evaluation.
How to Learn NLP for Beginners
Comprehensive guide to self-learning NLP, covering foundational ML concepts, neural networks, modern NLP techniques, and practical projects. Includes curated resources and course recommendations for beginners.
Machine Learning Fundamentals - Lecture 1.3
Explore machine learning concepts and applications in AI, focusing on knowledge representation and reasoning techniques.