Probably Overthinking It: How to Use Data to Answer Questions, Avoid Statistical Traps, and Make Better Decisions Allen B. Downey
Lige nu: Prøv 1 måned til kun 9 kr.!
Start tilbuddetLyt når som helst, hvor som helst
Nyd den ubegrænsede adgang til tusindvis af spændende e- og lydbøger - helt gratis
- Lyt og læs så meget du har lyst til
- Opdag et kæmpe bibliotek fyldt med fortællinger
- Eksklusive titler + Mofibo Originals
- Opsig når som helst
Mastering NLP from Foundations to LLMs: Apply advanced rule-based techniques to LLMs and solve real-world business problems using Python
- Sprog
- Engelsk
- Format
- Kategori
Fakta
Do you want to master Natural Language Processing (NLP) but don’t know where to begin? This book will give you the right head start. Written by leaders in machine learning and NLP, Mastering NLP from Foundations to LLMs provides an in-depth introduction to techniques. Starting with the mathematical foundations of machine learning (ML), you’ll gradually progress to advanced NLP applications such as large language models (LLMs) and AI applications. You’ll get to grips with linear algebra, optimization, probability, and statistics, which are essential for understanding and implementing machine learning and NLP algorithms. You’ll also explore general machine learning techniques and find out how they relate to NLP. Next, you’ll learn how to preprocess text data, explore methods for cleaning and preparing text for analysis, and understand how to do text classification. You’ll get all of this and more along with complete Python code samples.
By the end of the book, the advanced topics of LLMs’ theory, design, and applications will be discussed along with the future trends in NLP, which will feature expert opinions. You’ll also get to strengthen your practical skills by working on sample real-world NLP business problems and solutions.
© 2024 Packt Publishing (E-bog): 9781804616383
Release date
E-bog: 26. april 2024
Andre kan også lide...
- Python Deep Learning Cookbook: Over 75 practical recipes on neural network modeling, reinforcement learning, and transfer learning using Python Indra den Bakker
- Hands-On Neuroevolution with Python: Build high-performing artificial neural network architectures using neuroevolution-based algorithms Iaroslav Omelianenko
- Data Science from Scratch: The #1 Data Science Guide for Everything A Data Scientist Needs to Know: Python, Linear Algebra, Statistics, Coding, Applications, Neural Networks, and Decision Trees Steven Cooper
- Deep Reinforcement Learning Hands-On: Apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more Maxim Lapan
- Machine Learning with scikit-learn Quick Start Guide: Classification, regression, and clustering techniques in Python Kevin Jolly
- Practical Data Science with Python: Learn tools and techniques from hands-on examples to extract insights from data Nathan George
- TensorFlow Deep Learning Projects: 10 real-world projects on computer vision, machine translation, chatbots, and reinforcement learning Luca Massaron
- Hands-On Reinforcement Learning for Games: Implementing self-learning agents in games using artificial intelligence techniques Micheal Lanham
- Natural Language Understanding with Python: Combine natural language technology, deep learning, and large language models to create human-like comprehension Deborah A. Dahl
Brugbare links
Sprog og region
Dansk
Danmark
