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GPT Instruction Fine-tuning With Keras

GPT Instruction Fine-tuning With Keras

Fine-tuning has become the new training because training large language models (LLMs) from scratch is computationally expensive. It also requires collecting and preparing large datasets which is also time intensive. These resources are only the purview of a few individuals and organizations. Fortunately, there are many open-source LLMs that one

How to Detect AI Generated Content With TensorFlow

How to Detect AI Generated Content With TensorFlow

With the plethora of open-source language models, it's incredibly difficult to determine if a piece of text is AI generated. However, with a good dataset, you can train a model in TensorFlow to detect if a large language model generated text. It's such an interesting problem

Convolutional Neural Networks in JAX: Ultimate Guide

Convolutional Neural Networks in JAX: Ultimate Guide

JAX is a high performance library that offers accelerated computing through XLA and Just In Time Compilation. It also has handy features that enable you to write one codebase that can be applied to batches of data and run on CPU, GPU, or TPU. However, one of its biggest selling

TensorFlow Tensors(What are Tensors: Understanding the Basics, Creating, and Working with Tensors)

TensorFlow Tensors(What are Tensors: Understanding the Basics, Creating, and Working with Tensors)

First off: If you are familiar with NumPy arrays, understanding TensorFlow Tensors will be as easy as first importing TensorFlow as below: import tensorflow as tf print(tf.__version__) # check version # 2.14.0 💡The examples in this article use TensorFlow v2.x, so concepts deprecated and or that were

Implementing Transformer decoder for text generation in Keras and TensorFlow

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Implementing Transformer decoder for text generation in Keras and TensorFlow

The recent wave of generative language models is the culmination of years of research starting with the seminal "Attention is All You Need" paper. The paper introduced the Transformer architecture that would later be used as the backbone for numerous language models. These text generation language models are

Text Classification With BERT and KerasNLP

Text Classification With BERT and KerasNLP

BERT is a popular Masked Language Model. Some words are hidden from the model and trained to predict them. The model is bidirectional, meaning it has access to the words to the left and right, making it a good choice for tasks such as text classification. Training BERT can quickly

Top 20 Pandas Functions You Aren't Using, Which You Should Be Using

This blog post will explore 20 powerful and unique Pandas functions that can significantly enhance your data analysis workflow. We will be using the famous Iris dataset as an example to demonstrate each function. The Iris dataset contains four features: Sepal Length, Sepal Width, Petal Length, and Petal Width, along

How to Build Large Language Model Applications with PaLM API and LangChain

How to Build Large Language Model Applications with PaLM API and LangChain

You can now use Generative AI Studio on Vertex AI to prompt, tune and deploy Google's foundational models, including PaLM 2, Imagen, Codey, and Chirp. You can easily design and fine-tune your prompt and copy the code required to deploy the solution. Leveraging a foundational model is a

How to Perform Image Augmentation With KerasCV

How to Perform Image Augmentation With KerasCV

Training computer vision models with little data can lead to poor model performance. This problem can be solved by generating new data samples from the existing images. For example, you can create new images by flipping and rotating the existing ones. Generating new image samples from existing ones is known

How to Build LLM Applications With LangChain and Openai

How to Build LLM Applications With LangChain and Openai

LangChain is an open-source tool for building large language model (LLM) applications. It supports a variety of open-source and closed models, making it easy to create these applications with one tool. Some of the modules in Langchain include: * Models for supported models and integrations * Prompts for making it easy to

How to Train Stable Diffusion With Keras

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How to Train Stable Diffusion With Keras

Image generation models are causing a sensation worldwide, particularly the powerful Stable Diffusion technique. With Stable Diffusion, you can generate images with your laptop, which was previously impossible. Here's how diffusion models work in plain English: 1. Generating images involves two processes. Diffusion adds noise gradually to the

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How to Generate Images with Variational Autoencoders(VAE) (Create VAE from scratch using Keras and TensorFlow)

An autoencoder takes an input image and creates a low-dimensional representation, i.e., a latent vector. This vector is then used to reconstruct the original image. Regular autoencoders get an image as input and output the same image. However, Variational AutoEncoders (VAE) generate new images with the same distribution as

Distributed training with TensorFlow: How to train Keras models on multiple GPUs

Training computer vision models requires a lot of time because of the size of the models and image data. Therefore, training these models can take prolonged periods of time, especially when training on a single GPU. You can reduce the training time by distributing the training across several GPUs. This

Technical Writing

Technical Writing: Ultimate Beginners Guide

I have created technical content for various companies over the last 5 years. Educating developers is how technology companies use to grow their communities. Developers hate being sold to, so this is the best way to get developers to use a company's product. The product should solve a

Writing for Data Scientists

Download Writing for Data Scientists sample Writing for Data Scientists Free Sample Writing for Data Scientists - Free Sample .pdf337 KBdownload-circle I earned $300 for my first paid data science and machine learning article. I get paid between $250 and $500 for each data science article I write. In this

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Create U-Net from scratch (Image segmentation with U-Net with Keras and TensorFlow)

In the Implementing Fully Convolutional Networks (FCNs) from scratch in Keras and TensorFlow article, you saw how to build an image segmentation model with FCNs. However, due to the model's limitations, it did not perform very well in the segmenting task. In this post, you will see how

Implementing Fully Convolutional Networks (FCNs) from scratch in Keras and TensorFlow (Build image segmentation model from scratch)

Implementing Fully Convolutional Networks (FCNs) from scratch in Keras and TensorFlow (Build image segmentation model from scratch)

In 2014, Jonathan Long, Evan Shelhamer, and Trevor Darrell proposed solving image segmentation problems using Fully Convolutional Neural Networks(FCNs). FCNs have no fully connected layers. Image segmentation involves making a prediction for each pixel in an image. FCNs can accept images of any size because they don't

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Entropy, information gain, and Gini impurity(Decision tree splitting criteria)

Decision trees are supervised machine-learning models used to solve classification and regression problems. They help to make decisions by breaking down a problem with a bunch of if-else-then-like evaluations that result in a tree-like structure. For quality and viable decisions to be made, a decision tree builds itself by splitting

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Decision Trees and Random Forests(Building and optimizing decision tree and random forest models)

In the modern world, so much data is present on the internet. Organizations need efficient and rigorous algorithms to handle these huge chunks of data, make practical analyses, and provide appropriate decisions relevant to maximizing their profits and market presence. There are such algorithms commonly used today for decision-making processes.

How to become a Kaggle Competitions Grandmaster

How to become a Kaggle Competitions Grandmaster

writtencast 001 - Shujun He In this inaugural interview of the writtencast, I am joined by Shujun He. Shujun is a P.hD. student at Texas A&M University and a Kaggle Competitions Grandmaster. To become a Kaggle Competitions Grandmaster you need 5 gold medals and at least one

Gradio tutorial (Build machine learning applications)

Gradio tutorial (Build machine learning applications)

You have built your optimally performing machine learning model. What next? This tutorial explores the use of Gradio in building machine learning applications. What is Gradio? Gradio is an open-source Python package that allows you to quickly create easy-to-use, customizable UI components for your ML model, any API, or even

Free data science course

Join my free data science and machine learning email course with Python. Each day I will share a new lesson and provide code examples and notebooks to help you in your data science journey. The course covers beginner to advanced concepts in data science and machine learning. I will provide

Object detection with Vision Transformer for Open-World Localization(OWL-ViT)

Object detection with Vision Transformer for Open-World Localization(OWL-ViT)

Convolutional neural networks have been the primary networks applied in objection detection. Recently, Transformers have gained popularity in natural language processing and computer vision. In this article, we explore the use of the OWL-ViT in object detection. Let’s get started. What is a Vision Transformer? Transformers have been widely

logistic regression

Logistic regression in Python with Scikit-learn

In linear regression, we tried to understand the relationship between one or more predictor variables and a continuous response variable. This article will explore logistic regression, where the response variable will be discrete or categorical. What is classification? Classification is a supervised machine learning problem of predicting which category or

Transfer learning guide(With examples for text and images in Keras and PyTorch)

Transfer learning guide(With examples for text and images in Keras and PyTorch)

Training computer vision (CV) or natural language processing (NLP) models can be expensive and requires large datasets. If labeling is done manually, the process will take a longer training time and requires expensive hardware. For instance, the Generative Pre-trained Transformer 2 (GPT-2), a benchmark-setting language model created by Open AI