Learn Natural Language Processing With Python

Learn Natural Language Processing and Neural Networks with Python and PyTorch

What you'll learn

Computational Graphs

PyTorch Basics

Corpora, Tokens, and Types

N-grams

Simplest Neural Network

Activation Functions

Supervised Training

Feed-Forward Networks

The Multilayer Perceptron

Model Evaluation and Prediction

Convolutional Neural Networks

Batch Normalization (BatchNorm)

Network-in-Network Connections

The CBOWClassifier Model

Sequence Modeling

Recurrent Neural Networks

Intermediate Sequence Modeling

Vanilla RNNs (or Elman RNNs)

Advanced Sequence Modeling

Requirements

Just passion for learning!

Description

Welcome to the exciting world of Natural Language Processing (NLP) and Neural Networks! In this comprehensive course, you will embark on a journey to master the fundamentals of NLP and neural networks using the powerful combination of Python programming language and PyTorch framework. Whether you are a beginner or an experienced programmer, this course will equip you with the essential skills and knowledge to leverage the potential of NLP and neural networks for various applications.Natural Language Processing (NLP) has emerged as a critical field within artificial intelligence, enabling computers to understand, interpret, and generate human language. Through a series of hands-on exercises and projects, you will delve into the core concepts of NLP, including text preprocessing, sentiment analysis, named entity recognition, part-of-speech tagging, and more. You will learn how to manipulate and analyze textual data using Python libraries such as NLTK (Natural Language Toolkit) and spaCy, gaining insights into the underlying structure of language.Neural networks have revolutionized the field of machine learning, offering powerful tools for solving complex tasks. In this course, you will explore the foundations of neural networks, including perceptrons, feedforward networks, backpropagation, activation functions, and optimization algorithms. You will then delve into advanced neural network architectures such as recurrent neural networks (RNNs), long short-term memory networks (LSTMs), and transformers, which are specifically designed to handle sequential data like text.PyTorch has emerged as one of the leading deep learning frameworks, known for its flexibility, efficiency, and ease of use. Throughout this course, you will harness the capabilities of PyTorch to implement NLP models and neural networks from scratch. You will learn how to define network architectures, train models on large datasets, and evaluate their performance using various metrics. By the end of the course, you will have the confidence and proficiency to build cutting-edge NLP applications and neural network models using PyTorch.Key Topics Covered:1. Introduction to Natural Language Processing (NLP)2. Text Preprocessing Techniques3. Sentiment Analysis and Text Classification4. Named Entity Recognition (NER) and Part-of-Speech (POS) Tagging5. Word Embeddings and Semantic Similarity6. Introduction to Neural Networks7. Perceptrons and Feedforward Networks8. Backpropagation and Gradient Descent9. Activation Functions and Optimization Algorithms10. Recurrent Neural Networks (RNNs) and Long Short-Term Memory Networks (LSTMs)11. Transformers for NLP Tasks12. Introduction to PyTorch and its Ecosystem13. Building NLP Models with PyTorch14. Implementing Neural Networks with PyTorch15. Training and Evaluating Deep Learning ModelsPrerequisites:This course is designed for individuals with a basic understanding of Python programming language and familiarity with machine learning concepts. While prior experience with deep learning or NLP is not required, a strong foundation in Python programming will be beneficial. Participants should also have a curiosity for exploring the intersection of language, artificial intelligence, and neural networks.By the end of this course, you will be equipped with the skills and knowledge to tackle real-world NLP challenges and leverage the power of neural networks for a wide range of applications. Whether you aspire to pursue a career in data science, natural language processing, or artificial intelligence, this course will provide you with a solid foundation to achieve your goals. Join us on this exciting journey and unlock the potential of NLP and neural networks with Python and PyTorch!

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Supervised Learning

Lecture 3 One-Hot Representation

Lecture 4 Term-Frequency (TF)

Lecture 5 TF-IDF

Lecture 6 Target Encoding and Computations

Lecture 7 Creating Tensors

Lecture 8 Tensor Size and Types

Lecture 9 Tensor Operations

Lecture 10 Joining, Slicing and Indexing

Lecture 11 Computational Graphs and Tensors

Section 2: Neural Network

Lecture 12 Perceptron The Simplest Neural Network

Lecture 13 Perceptron The Simplest Neural Network - 2

Lecture 14 Sigmoid

Lecture 15 Tanh

Lecture 16 ReLU

Lecture 17 Softmax

Lecture 18 Mean Squared Error Loss

Lecture 19 Categorical Cross-Entropy Loss

Lecture 20 Binary Cross-Entropy Loss

Lecture 21 Toy Data Construction

Lecture 22 Model Choosing and Loss Function

Lecture 23 Optimizer Choosing

People who want to explore Data Science,People who want to explore Natural Language Processing,People who want to explore Artificial Intelligence,People who want to explore Neural Networks