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This Repository Contains Deep Learning Based Jupyter Notebooks From Beginner to Advance | All are structured in Folders and according to their types | And the datasets used in these notebooks you can find then in my Dataset Repository.
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This score does not audit code, security, maintainers, documentation quality, or suitability. Verify the repository and its current documentation before adoption.
From raw data to trained neural networks — every algorithm, every single neural network properly explained, every experiment, documented.
🏢 Maintained and updated by : Bhavya Kansal | 🌐 visit at : bhavyakansal.dev | 📍 Patiala, Punjab, India
Selected from shared topics, language and repository description—not editorial ratings.
ayushdabra /
This repository contains the Jupyter Notebook for the UNet-VGG16 CNN Model trained on the Lunar Landscape Images Dataset.
59/100 healthTouradBaba /
This repository contains programming assignments for the Deep Learning Specialization by deeplearning.AI. It includes Jupyter Notebooks for exercises in neural networks, hyperparameter tuning, convolutional networks, and sequence models.
56/100 healthThis is not just a notebook dump — it is a structured, continuously updated Deep Learning knowledge base maintained and updated by Bhavya Kansal. As an AI/ML Engineer and Developer, I built this repository to provide Beginner to Advanced level and Structural Understanding of Deep Learning.
Every notebook in this repository:
This repository is actively maintained and updated regularly with new neural network architectures, projects, and experiments as the learning journey progresses.
| Audience | How This Helps |
|---|---|
| 🎓 Beginners | Who want to start Deep Learning with clean, documented code |
| 🔬 Students | Reference implementations for assignments and understanding |
| 💼 Practitioners | Quick refresher notebooks for standard architectures |
| 🧑💻 Developers | Baseline TensorFlow, ANN, RNN, CNN patterns to build production models from scratch |
| Tool | Purpose |
|---|---|
 | Core programming language |
 | Deep learning & neural networks |
 | High-level neural network API |
 | Numerical computing & tensors |
 | Data preprocessing & analysis |
 | Data visualization |
 | Statistical visualization |
 | ML utilities & preprocessing |
 | Experimentation & prototyping |
All notebooks are self-contained and can be opened directly on GitHub or run locally. Click any notebook name to open it.
The foundation of every DL pipeline — cleaning, transforming, and preparing raw data.
| # | Notebook | Concepts Covered |
|---|---|---|
| 1 | Data Preprocessing | Missing values, data cleaning, pipelines |
| 2 | Feature Scaling | StandardScaler, MinMaxScaler, normalization |
| 3 | Encoding | Label encoding, One-Hot encoding for neural networks |
| 4 | Outlier Detection | IQR, Z-score, visualizing outliers |
Building blocks of deep learning — from perceptrons to multi-layer networks.
| # | Notebook | Concepts Covered |
|---|---|---|
| 1 | Artifical Neural Network (ANN) | Forward propagation, backpropagation, activations |
| 2 | Activation Functions | ReLU, Sigmoid, Tanh, Softmax explained |
| 3 | Loss Functions | MSE, Cross-Entropy, choosing right loss |
| 4 | Optimizers | SGD, Adam, RMSprop, learning rates |
For sequences and time-series data.
| # | Notebook | Concepts Covered |
|---|---|---|
| 1 | RNN Basics | Vanishing gradients, unfolding, BPTT |
| 2 | LSTM Networks | Long Short-Term Memory, cell state, gates |
| 3 | GRU Networks | Gated Recurrent Units, simplified LSTM |
| 4 | Sequence to Sequence | Encoder-Decoder, attention mechanisms |
For image recognition and computer vision tasks.
| # | Notebook | Concepts Covered |
|---|---|---|
| 1 | CNN Fundamentals | Convolution, pooling, stride, padding |
| 2 | Popular Architectures | LeNet, AlexNet, VGG, ResNet concepts |
| 3 | Image Classification | CIFAR-10, MNIST with CNNs |
| 4 | Transfer Learning | Fine-tuning pre-trained models |
Because building a model is only half the job.
| # | Notebook | Concepts Covered |
|---|---|---|
| 1 | Confusion Matrix | TP/FP/FN/TN, precision, recall, F1 |
| 2 | Model Validation | K-Fold, Cross-validation strategies |
| 3 | Regularization | Dropout, L1/L2, Early Stopping |
| 4 | Hyperparameter Tuning | GridSearchCV, RandomizedSearchCV |
Make sure you have Python 3.x installed. Then install the required libraries:
pip install numpy pandas matplotlib seaborn scikit-learn tensorflow jupyter
# 1. Clone this repository
git clone https://github.com/BhavyaKansal20/Deep-Learning.git
# 2. Navigate into the folder
cd Deep-Learning
# 3. Launch Jupyter Notebook
jupyter notebook
Then open any .ipynb file from the Jupyter interface in your browser.
Click the badge below or open any notebook on GitHub and change the URL domain from github.com to colab.research.google.com/github:
This repository is actively growing. Upcoming additions:
⭐ Star the repo to get notified when new notebooks are added!
Datasets used in these notebooks are maintained in a separate dedicated repository to keep this repo clean and lightweight.
🔗 Dataset Repository: Datasets
Some notebooks use built-in datasets (CIFAR-10, MNIST, etc.) which require no external download.
Contributions, improvements, and suggestions are warmly welcome!
How to contribute:
git checkout -b feature/your-topicgit commit -m "Add: Transformer notebook"git push origin feature/your-topicPlease read the CONTRIBUTING.md and CODE_OF_CONDUCT.md before submitting.
MIT License
Copyright (c) 2026 Bhavya Kansal
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.
See the full LICENSE file.
All notebooks and code in this repository are intended strictly for educational and learning purposes. The implementations are for conceptual clarity and skill development, not production deployment without thorough validation.
Datasets used across these notebooks may be sourced from:
Refer to individual notebooks for specific dataset sources and their respective licenses. All will be checked from this repository: Datasets
For responsible disclosure of any security concerns, please refer to the SECURITY.md file.
Bhavya Kansal | AI/ML Developer | Researcher & Collaborator | जय श्री राम 🙏❤️
📍 Patiala, Punjab, India
It keeps this project alive and motivates more content to be added.
© 2026 Bhavya Kansal · All Rights Reserved
*Built with ❤️ & 🧠 in
vishnubalaji /
This repository contains all the Jupyter notebooks that are dealing with Deep Learning using PyTorch
41/100 healthAbdelrhman-Ellithy /
Plant Disease Classification project! This repository contains a Jupyter Notebook that leverages Convolutional Neural Networks (CNNs) to classify plant diseases using image datasets.
52/100 healthUjjwal-270 /
This repository contains a Jupyter Notebook for classifying potato leaf diseases using deep learning techniques.
30/100 healthEvansNjogu /
This repository contains a Jupyter Notebook with the implementation of a Sudoku Solver using Deep Learning. The goal is to solve a Sudoku puzzle through image recognition of the puzzle and using a Convolutional Neural Network (CNN) to predict the numbers for each cell.
37/100 health