Welcome to the Deep Learning Odyssey repository! This curated collection of Jupyter Notebook files provides a hands-on journey through the fascinating world of deep learning. Whether you're a beginner taking your first steps or an experienced practitioner seeking to expand your knowledge, you'll find valuable insights and practical examples here.
This repository aims to be your comprehensive resource for learning and applying deep learning techniques. Each notebook focuses on a specific concept, algorithm, or application, offering a clear and concise exploration through code and explanations.
- Wide Range of Topics: Dive into diverse deep learning topics, from fundamental concepts like Artificial Neural Networks (ANNs) to advanced architectures like Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and more.
- Hands-on Examples: Each topic is accompanied by a Jupyter Notebook file containing practical implementations. This allows you to directly experiment with the code, modify parameters, and observe the results.
- Practical Applications: Discover how deep learning is transforming various fields. Explore notebooks that apply these techniques to real-world problems like image recognition, natural language processing, churn prediction, and more.
- Educational Resource: Utilize this repository for self-study, classroom instruction, or as a foundation for your research endeavors in deep learning.
The code examples in this repository leverage the power of the following technologies:
- Python: The primary programming language for its ease of use and rich ecosystem of data science libraries.
- TensorFlow: A leading open-source machine learning framework known for its flexibility and scalability.
- Keras: A user-friendly high-level API built on top of TensorFlow, simplifying the process of building and training deep learning models.
- Jupyter Notebook: An interactive environment that combines code, visualizations, and explanations, making it ideal for learning and experimentation.
Explore the notebooks below, each dedicated to a specific aspect of deep learning:
- 1.M1-CreditCardChurn.ipynb: Predict credit card churn using deep learning models.
- 2.Mnist_ANN.ipynb: Implement an ANN to classify handwritten digits from the MNIST dataset.
- 3.admission-prediction-using-ann.ipynb: Predict admission chances using an ANN.
- 4.Backpropagation-scratch-Regression.ipynb: Understand backpropagation, a core algorithm in deep learning, by implementing it from scratch for regression tasks.
- 5.backpropagation_classification.ipynb: Explore backpropagation in the context of classification problems.
- 6.vanishing_gradient_problem.ipynb: Grasp the challenges posed by the vanishing gradient problem in deep neural networks.
- 7_early_stopping_keras.ipynb: Implement early stopping in Keras to prevent overfitting and improve model generalization.
- 8_feature_scaling.ipynb: Learn the importance of feature scaling in preparing data for deep learning models.
- 9_dropout_Regression.ipynb: Apply dropout regularization to regression tasks.
- Bidirectional_LSTM.ipynb: Work with Bidirectional LSTMs, a powerful type of recurrent neural network.
- CIFAR10_CNN.ipynb: Build a CNN to classify images from the CIFAR-10 dataset.
- CNN-transfer_learning.ipynb: Leverage the power of transfer learning using pre-trained CNNs.
- DeepLearning-NPTEL.pdf: A resource for deeper understanding of Deep Learning concepts.
- Deep_learning_nptel.ipynb: A companion notebook for the Deep Learning NPTEL resource.
- DogBreed.ipynb: Explore deep learning for dog breed classification.
- DogvsCat.ipynb: Build a model to distinguish between images of dogs and cats.
- Fashion_MNIST.ipynb: Work with the Fashion MNIST dataset for image classification.
- GRU_Sentiment_Analysis.ipynb: Perform sentiment analysis using GRUs, another type of recurrent neural network.
- LSTM_Fake_News_Classification.ipynb: Build a model to detect fake news using LSTMs.
- LSTM_Next_Word_Prediction.ipynb: Implement next-word prediction using LSTMs for natural language processing.
- Non_LinearNN.ipynb: Explore non-linear neural networks and their capabilities.
- SimpleRNN_IntegerEncoding_Embedding.ipynb: Understand how to use integer encoding and embedding layers in RNNs.
- yolov3_object_detection.ipynb: Implement object detection using the YOLOv3 algorithm.
This repository is designed to guide you through a comprehensive deep learning journey. In addition to the notebooks listed above, the content covers a wider spectrum of concepts, including:
- Convolutional Neural Networks (CNNs)
- Recurrent Neural Networks (RNNs)
- Transfer Learning
- Generative Adversarial Networks (GANs)
- Natural Language Processing (NLP)
- Reinforcement Learning
For a deeper dive into the world of deep learning, check out this valuable resource:
- Campus-X: This YouTube channel provides extensive content on machine learning and deep learning, offering tutorials, explanations, and practical examples.
This repository is licensed under the MIT License. You have the freedom to use, modify, and distribute the code for both commercial and non-commercial purposes. Please refer to the LICENSE file for detailed information.
Feel free to reach out if you have any questions, suggestions, or feedback:
- Name: Sourav Kumar
- Email: Souravkumar8432@gmail.com
- LinkedIn: linkedin.com/in/singhsourav0/
We welcome contributions and collaborations to enrich this repository further!