Mastering Recurrent Neural Networks & LSTM

Mastering Recurrent Neural Networks & LSTM

Table of Contents:

1. Introduction
2. Overview of Recurrent Neural Networks (RNN) 2.1. RNN Architecture 2.2. Applications of RNN
3. Working of Recurrent Neural Networks 3.1. RNN API 3.2. Implementation of RNN 3.3. Training RNN 3.3.1. Endowing Semantics on Vectors 3.3.2. Working with Character Level Language Models 3.4. Generating Sequences with RNN 3.4.1. Generating Poetry 3.4.2. Generating Mathematics 3.4.3. Generating Code
4. Interpretable Cells in RNNs 4.1. Discovering Interpretability in RNNs 4.2. Examples of Interpretable Cells
5. Image Captioning with RNN 5.1. Introduction to Image Captioning 5.2. Architecture of Image Captioning Model 5.3. Training and Evaluation 5.4. Extending the Image Captioning Model
6. Querying Images with RNN 6.1. Reverse Image Querying 6.2. Searching for Text Snippets in Images
7. Conclusion
8. Further Learning Resources

Introduction

Recurrence Neural Networks (RNN) have revolutionized various fields, including language modeling, image captioning, and sequence predictions. In this article, we will explore the concept of RNN, its architecture, and its applications in different domains. We will also Delve into the working of RNN, including its API, implementation, training process, and the generation of sequences using RNNs. Furthermore, we will discuss the interpretability of RNNs and how certain cells in the network can be interpreted. Additionally, we will cover the topic of image captioning with RNNs and the ability to query images using RNNs. Lastly, we will conclude the article with further resources for those interested in learning more about RNNs.

Overview of Recurrent Neural Networks (RNN)

Recurrent Neural Networks (RNN) are powerful models that offer flexibility in designing network architectures. Unlike traditional neural networks, RNNs can operate on sequences of inputs and outputs. This makes them suitable for tasks such as image captioning, sentiment classification, machine translation, and video classification. RNNs have a simple API consisting of a single step function that updates its state Based on the input vector. The recurrent neural network has a state that is updated every time a new vector is observed. The parameters of the RNN determine its behavior and can be tuned using training data.

Working of Recurrent Neural Networks

The working of Recurrent Neural Networks involves the step function that takes an input vector, updates the state vector, and predicts the output vector. Training RNNs involves endowing semantics on vectors and working with character-level language models. By tuning the parameters of the RNN using training data, the network learns to generate sequences with a specific behavior. RNNs can generate poetry, mathematics, and even code by sampling from probability distributions at each time step. One can feed large datasets into an RNN to learn the statistical Patterns of the data.

Interpretable Cells in RNNs

Certain cells in RNNs can be interpreted to understand the patterns the network has learned. For example, a cell in the RNN might correspond to detecting quotes or keeping track of line lengths. By studying the activity of Hidden state neurons, insights can be gained about the RNN's understanding of the input data.

Image Captioning with RNN

RNNs can be used for image captioning tasks by combining them with Convolutional Neural Networks (CNN). The CNN extracts features from images, which are then fed into the RNN to generate Captions. The joint model can detect and describe objects within an image and generate Relevant captions. Training such models requires large datasets with image-sentence pairs, allowing the RNN to learn the associations between visual and textual information.

Querying Images with RNN

RNNs can also be used to query images based on text snippets. By providing a query in the form of a text snippet, the RNN can search through a collection of images and identify regions that are compatible with the given query. This allows for efficient searching and retrieval of relevant images based on specific textual criteria.

Conclusion

Recurrent Neural Networks (RNN) have proven to be powerful models for various tasks, including language modeling, image captioning, and sequence generation. They offer flexibility in designing network architectures and can learn complex patterns from training data. By combining RNNs with other neural network models, such as Convolutional Neural Networks (CNN), new possibilities arise for tasks like image captioning and image querying. As RNNs Continue to evolve, their applications in different domains are likely to expand further.

Further Learning Resources

• CS231n Class at Stanford University
• Lecture videos, slides, notes, and assignments available for learning more about RNNs