How Convolutional Neural Networks Work
Explore the inner workings of Convolutional Neural Networks, from basic concepts to advanced techniques, in this comprehensive guided tour of deep learning architecture.
LLaMA- Open and Efficient Foundation Language Models - Paper Explained
Detailed explanation of Meta's LLaMA language models, covering training data, architecture, implementation, and performance. Explores how smaller models can outperform larger ones with optimized training.
Data Augmentation for Image-Based Reinforcement Learning
Explores data augmentation techniques for image-based reinforcement learning, presenting a model-free algorithm and a self-supervised framework for visual continuous control tasks, achieving state-of-the-art results.
Training Machines to Learn the Way Humans Do - An Alternative to Backpropagation
Explore an alternative to backpropagation in machine learning, focusing on biologically-inspired local learning methods and their applications in neural networks and the cerebellum.
Reproducible, Reusable, and Robust Reinforcement Learning - Joelle Pineau
Explore reproducibility, reusability, and robustness in reinforcement learning. Delve into policy gradient methods, hyperparameter interplay, fair comparisons, and real-world applications of RL algorithms.
Yes, Generative Models Are The New Sparsity
Explore how generative models revolutionize data compression and signal processing, offering new perspectives on sparsity and optimization in machine learning.
MCMC Training of Bayesian Neural Networks
Explore Bayesian Neural Networks, MCMC methods, and their applications in machine learning with Prof. Radford Neal. Gain insights into flexible modeling and practical implementations.
Auto-Train a Time-Series Forecast Model with AML & ADB
Learn to auto-train time-series forecast models using Azure Machine Learning and Azure Databricks for large-scale enterprise solutions in supply chain, healthcare, insurance, and finance.
Building Makemore - MLP
Implement a multilayer perceptron character-level language model, covering machine learning basics like model training, hyperparameter tuning, evaluation, and addressing under/overfitting.
Exploring the Origins and Prevalence of Texture Bias in Convolutional Neural Networks
Explore texture bias in CNNs: origins, prevalence, and implications. Examine dataset influence, training objectives, and model architectures to understand and mitigate this phenomenon in computer vision.
Introduction to Neural Networks in Python - Tensorflow-Keras
Learn neural network basics, architecture, hyperparameters, and implementation in Python using TensorFlow/Keras. Includes hands-on coding examples for various classification tasks, from simple to complex datasets.
Methods of Optimization in Machine Learning
Explore optimization techniques for machine learning models, including gradient descent and stochastic methods. Learn to adjust hyperparameters and minimize cost functions effectively.
Intro to Deep Learning - ML Tech Talks
Comprehensive introduction to deep learning concepts, neural network architectures, and practical applications, featuring TensorFlow examples, code demos, and insights into representation learning and model interpretability.
Inside GPT - Large Language Models Demystified
Demystifying GPT algorithms: from fundamentals to advanced applications. Explore NLP concepts, model training, ChatGPT capabilities, and practical implementation strategies for leveraging AI in your solutions.
How to Fine-Tune Your Own Language Model - OpenPipe CEO Kyle Corbitt
Explore fine-tuning language models with OpenPipe's CEO. Learn about model selection, data curation, hyperparameter optimization, and validation processes for efficient AI development and deployment.
Custom RAG Pipelines and LLM Fine-Tuning - A Gradient Tutorial
Explore custom RAG pipelines and LLM fine-tuning using Gradient. Learn to deploy fine-tuned models, mimic Yoda's speech style, and efficiently fine-tune 13B parameter open-source models for unique applications.