Description
Introduction of Natural Language Processing(NLP):
“Natural Language Processing (NLP) with Python” is a comprehensive course designed to introduce participants to the field of NLP and how to implement various NLP techniques using Python. This course covers fundamental concepts and practical applications of NLP, such as text processing, sentiment analysis, and language modeling. Participants will gain hands-on experience with popular NLP libraries and tools in Python, including NLTK, spaCy, and transformers. By the end of the course, learners will have the skills to build and deploy NLP applications and understand the role of NLP in extracting meaningful insights from textual data.
Prerequisites of Natural Language Processing(NLP):
- Basic programming knowledge in Python.
- Familiarity with basic data manipulation and analysis.
- Understanding of fundamental machine learning concepts is helpful but not required.
 Table of Contents:
- Introduction to Deep Learning
1.1 What is Deep Learning?
1.2 Overview of Neural Networks
1.3 The Role of TensorFlow and Keras in Deep Learning(Ref: Caffe: Deep Learning Framework ) - Getting Started with TensorFlow and Keras
2.1 Setting Up the Environment
2.2 Introduction to TensorFlow: Key Concepts and Components
2.3 Overview of Keras: High-Level API for Building Models - Fundamentals of Neural Networks
3.1 Understanding Perceptrons and Multi-Layer Perceptrons
3.2 Activation Functions: Sigmoid, ReLU, Tanh
3.3 Loss Functions and Optimization Algorithms - Building and Training Neural Networks with Keras
4.1 Creating Models Using Sequential and Functional APIs
4.2 Training Models: Data Preparation, Batch Processing, Epochs
4.3 Evaluating Model Performance: Metrics and Validation - Deep Learning Architectures
5.1 Convolutional Neural Networks (CNNs) for Image Processing
5.2 Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) Networks for Sequential Data
5.3 Generative Adversarial Networks (GANs) Overview - Advanced Deep Learning Techniques
6.1 Transfer Learning and Pre-trained Models
6.2 Hyperparameter Tuning and Model Optimization
6.3 Regularization Techniques: Dropout, Batch Normalization - Natural Language Processing (NLP) with Deep Learning
7.1 Introduction to NLP Concepts
7.2 Building NLP Models: Text Classification and Sentiment Analysis
7.3 Using Pre-trained Language Models with TensorFlow - Deploying Deep Learning Models
8.1 Saving and Loading Models
8.2 Deploying Models to Production Environments
8.3 Model Serving with TensorFlow Serving - Hands-on Projects
9.1 Image Classification Project Using CNNs
9.2 Text Generation Using RNNs or LSTMs
9.3 Building a Real-time Object Detection System - Ethics and Best Practices in Deep Learning
10.1 Addressing Bias and Fairness in Models
10.2 Ensuring Privacy and Security
10.3 Best Practices for Responsible AI Development - Conclusion and Further Learning
11.1 Recap of Key Concepts and Techniques
11.2 Exploring Advanced Topics and Emerging Trends in Deep Learning
11.3 Resources for Continued Learning and Professional Development
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