Welcome to the 'Deep Neural Networks' repository! This repository is your comprehensive guide to building and working with deep neural networks (DNNs) in Python. Explore a wide range of topics, from generating and preprocessing data to training and inference, as well as advanced techniques such as embedding layers, convolutional neural networks (CNNs), long short-term memory networks (LSTMs), and text generation.
- Generate Our Data: Learn how to generate synthetic data for various tasks, including classification, regression, and sequence prediction. Understand the importance of data generation for training deep neural networks.
- Loading and Preprocessing Techniques: Explore techniques for loading and preprocessing data, including handling missing values, scaling features, encoding categorical variables, and splitting datasets into training and testing sets.
- Training and Inference: Dive into the process of training deep neural networks using popular frameworks such as TensorFlow and PyTorch. Understand the concepts of forward and backward propagation, gradient descent optimization, and model evaluation.
- Embedding Layer: Discover how to use embedding layers in neural network architectures to represent categorical variables as dense vectors. Explore techniques for learning embeddings from scratch or using pre-trained embeddings.
- Convolutional Neural Networks (CNNs): Learn about CNNs, a class of deep neural networks commonly used for image recognition and computer vision tasks. Understand the architecture of CNNs, including convolutional, pooling, and fully connected layers.
- Long Short-Term Memory Networks (LSTMs): Delve into LSTMs, a type of recurrent neural network (RNN) designed to capture long-term dependencies in sequential data. Explore applications of LSTMs in natural language processing, time series analysis, and more.
- Poem Text Generation: Experience the fascinating task of text generation using deep neural networks. Learn how to train a model to generate poetry or other types of text based on a given input.
Whether you're new to deep learning or seeking to expand your knowledge of advanced techniques, this repository provides valuable content, examples, and resources to support your journey.
Medical Image Analysis With CNN
Sentiment Analysis On Social Media Posts With LSTM
Optical Character Recognition with CNN
Contributions are welcome! If you have suggestions, improvements, or additional content to contribute, feel free to open issues, submit pull requests, or provide feedback. Your contributions help make this repository a valuable resource for the community.
This repository is maintained by Elsayed Elmandoh, an AI Engineer passionate. You can connect with Elsayed on LinkedIn or follow him on Twitter for updates and discussions related to deep neural networks and artificial intelligence.
Happy coding and deep learning!