Comparing TensorFlow (Keras), PyTorch, & MLX – Day 46

Comparing Deep Learning on TensorFlow (Keras), PyTorch, and Apple’s MLX Deep learning frameworks such as TensorFlow (Keras), PyTorch, and Apple’s MLX offer powerful tools to build and train machine learning models. Despite solving similar problems, these frameworks have different philosophies, APIs, and optimizations under the hood. In this post, we will examine how the same model is implemented on each platform and why the differences in code arise, especially focusing on why MLX is more similar to PyTorch than TensorFlow. 1. Model in PyTorch PyTorch is known for giving developers granular control over model-building and training processes. The framework encourages...

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185

TensorFlow: Using TensorBoard, Callbacks, and Model Saving in Keras _. day 16

Mastering TensorFlow: Using TensorBoard, Callbacks, and Model Saving in Keras Mastering TensorFlow: Using TensorBoard, Callbacks, and Model Saving in Keras TensorFlow and Keras provide powerful tools for building, training, and evaluating deep learning models. In this blog post, we will explore three essential techniques: Using TensorBoard for visualization Utilizing callbacks to enhance model training Saving and restoring models Using TensorBoard for Visualization TensorBoard is an interactive visualization tool that helps you understand your model’s training dynamics. It allows you to view learning curves, compare metrics between multiple runs, and analyze training statistics. Installation !pip install -q -U tensorflow tensorboard-plugin-profile Setting...

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17

Sequential vs Functional Keras API Part 2 explanation _ Day 15

Keras API Example Let’s continue from day 14 which we explained the 3 Keras API types and compare them Understanding Sequential vs. Functional API in Keras with a Simple Example When building neural networks in Keras, there are two main ways to define models: the Sequential API and the Functional API. In this post, we’ll explore the differences between these two approaches using a simple mathematical example. Sequential API The Sequential API in Keras is a linear stack of layers. It’s easy to use but limited to single-input, single-output stacks of layers. Here’s a simple example to illustrate how it...

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29

sequential , functional and model subclassing API in Keras _ day 14

In our last blog on day 13, we explained what’s Keras and we showed a code example which was using the Sequential API but did not discuss its API type. Understanding Keras APIs and Their Use Cases In our previous blog post on Day 13, we introduced Keras and provided a code example using the Sequential API. In this post, we will delve into the different types of Keras APIs: Sequential, Functional, and Model Subclassing. We will explain each API, its inventor, appropriate use cases, and whether they can be used interchangeably. We will also analyze the code examples provided...

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72

What is Keras _ day 13

Understanding Keras and Its Role in Deep Learning What is Keras? Keras is an open-source software library that provides a Python interface for artificial neural networks. It serves as a high-level API, simplifying the process of building and training deep learning models. Developed by François Chollet, a researcher at Google, Keras was first released in March 2015. It is designed to enable fast experimentation with deep neural networks, which is crucial for research and development in machine learning and artificial intelligence (AI). Who Invented Keras and Why? François Chollet created Keras to democratize deep learning by making it accessible and...

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