Guide to deploying a low-cost NVIDIA GPU VM on Azure using MSDN credits for PyTorch and TensorFlow machine learning development.
A tutorial on deploying a GPU-accelerated TensorFlow Jupyter Notebook on Google Kubernetes Engine (GKE) Autopilot.
A tutorial on using Sentence Transformers models with TensorFlow and Keras to create text embeddings for semantic search and similarity tasks.
Explores techniques for flattening and unflattening nested data structures in TensorFlow, JAX, and PyTorch for efficient deep learning model development.
A guide to setting up and using the Google Coral USB TPU Accelerator for faster machine learning inference on Windows 10.
The article argues that the choice of machine learning library (like PyTorch or TensorFlow) is less critical than building robust data and production pipelines.
A comparative analysis of the underlying architecture and design principles of TensorFlow and PyTorch machine learning frameworks.
Announcing the 3rd edition of Python Machine Learning, updated for TensorFlow 2.0 and featuring a new chapter on Generative Adversarial Networks (GANs).
Author announces the 3rd edition of Python Machine Learning, featuring TensorFlow 2.0 updates and a new chapter on Generative Adversarial Networks.
A guide to using Python decorators for automatic TensorFlow named scopes, improving code organization and TensorBoard visualization.
Introducing TFFS, a FUSE-based filesystem to interactively explore TensorFlow graphs and tensors using familiar Unix commands.
A detailed technical tutorial on implementing a Variational Autoencoder (VAE) with TensorFlow, including code and conditioning on digit types.
Step-by-step guide to reproducing the 'World Models' AI experiments, including prerequisites, software setup, and instructions for running pre-trained models.
A hands-on tutorial on implementing deep reinforcement learning models using TensorFlow and the OpenAI Gym environment.
Explains the Gated Multimodal Unit (GMU), a deep learning architecture for intelligently fusing data from different sources like images and text.
A comparison of PyTorch and TensorFlow deep learning frameworks, focusing on programmability, flexibility, and ease of use for different project scales.
A tutorial on building a Recurrent Neural Network (RNN) with LSTM cells in TensorFlow to predict S&P 500 stock prices.
A humorous take on solving the classic Fizz Buzz coding interview problem using an unnecessarily complex TensorFlow neural network.
Explores why modern neural networks succeed where older ones failed, emphasizing the critical role of massive computational power and data size.
