https://github.com/kaustubh-indulkar/be-it-dl-assignments
This repository contains implementations of various deep learning models and techniques using popular frameworks like TensorFlow, Keras, Theano, and PyTorch. It covers fundamental concepts such as feedforward neural networks, image classification, anomaly detection with autoencoders, and natural language processing with the Continuous Bag of Words
https://github.com/kaustubh-indulkar/be-it-dl-assignments
sppu-2019-pattern sppu-be-practical
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This repository contains implementations of various deep learning models and techniques using popular frameworks like TensorFlow, Keras, Theano, and PyTorch. It covers fundamental concepts such as feedforward neural networks, image classification, anomaly detection with autoencoders, and natural language processing with the Continuous Bag of Words
- Host: GitHub
- URL: https://github.com/kaustubh-indulkar/be-it-dl-assignments
- Owner: Kaustubh-Indulkar
- Created: 2025-02-15T05:58:26.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2025-02-15T06:04:42.000Z (4 months ago)
- Last Synced: 2025-02-15T07:19:01.020Z (4 months ago)
- Topics: sppu-2019-pattern, sppu-be-practical
- Homepage:
- Size: 935 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# BE-IT-DL-ASSIGNMENTS
# Deep Learning Implementations with TensorFlow, Keras, Theano, and PyTorch
This repository contains implementations of various deep learning models and techniques using popular frameworks like TensorFlow, Keras, Theano, and PyTorch. It covers fundamental concepts such as feedforward neural networks, image classification, anomaly detection with autoencoders, and natural language processing with the Continuous Bag of Words (CBOW) model. Transfer learning for object detection is also included.
## Table of Contents
1. [Deep Learning Package Comparison](#deep-learning-package-comparison)
2. [Feedforward Neural Networks](#feedforward-neural-networks)
3. [Image Classification Model](#image-classification-model)
4. [Anomaly Detection with Autoencoders](#anomaly-detection-with-autoencoders)
5. [Continuous Bag of Words (CBOW) Model](#continuous-bag-of-words-cbow-model)
6. [Object Detection with Transfer Learning](#object-detection-with-transfer-learning)
## Deep Learning Package Comparison
This section documents the distinct features and functionalities of TensorFlow, Keras, Theano, and PyTorch. It compares their strengths, weaknesses, and suitability for different deep learning tasks.
## Feedforward Neural Networks
This section demonstrates the implementation of feedforward neural networks using Keras and TensorFlow. The MNIST or CIFAR10 dataset is used for training and testing.
* **Packages:** Keras, TensorFlow
* **Dataset:** MNIST/CIFAR10
* **Steps:**
* Import necessary packages
* Load and preprocess data
* Define network architecture
* Train the model using SGD
* Evaluate the network
* Plot training loss and accuracy
## Image Classification Model
This section details the implementation of an image classification model, divided into four stages:
* **Stages:**
* Loading and preprocessing image data
* Defining the model's architecture
* Training the model
* Estimating model performance
## Anomaly Detection with Autoencoders
This section demonstrates anomaly detection using autoencoders.
* **Libraries:** [List required libraries, e.g., TensorFlow/Keras, scikit-learn]
* **Steps:**
* Import required libraries
* Load/access dataset
* Encoder for latent representation
* Decoder for reconstruction
* Compile model (optimizer, loss, metrics)
## Continuous Bag of Words (CBOW) Model
This section implements the Continuous Bag of Words (CBOW) model for natural language processing.
* **Stages:**
* Data preparation
* Training data generation
* Model training
* Output generation
## Object Detection with Transfer Learning
This section demonstrates object detection using transfer learning with pre-trained CNN architectures.