Notebooks from tensforflow for DL added

README.md

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+## Datasets and Deep Learning Projects
+
+In my deep learning journey, I have explored various datasets and implemented deep learning models to solve real-world problems. Below is an overview of some notable deep learning projects, showcasing their objectives, datasets, and outcomes.
+
+---
+
+### **Deep Learning Projects**
+
+#### 1. **CIFAR=1- Image Classification with Convolutional Neural Networks (CNNs)**
+- **Description**: Built and trained a CNN model for classifying images from the CIFAR-10 dataset. The project involved data augmentation, hyperparameter tuning, and visualizing model predictions.
+- **Dataset**: [CIFAR-10 Dataset](https://www.cs.toronto.edu/~kriz/cifar.html)
+- **Key Outcomes**: Improved classification accuracy through dropout and batch normalization techniques.
+- **Repository**: [CIFAR-10 Image Classification](https://github.com/vmahawar/cifar-10-image-classification)
+
+---
+
+#### 2. **Sentiment Analysis Using Recurrent Neural Networks (RNNs)**
+- **Description**: Performed sentiment analysis on the IMDB dataset using LSTM-based RNNs. The project focused on text preprocessing, sequence padding, and embedding layers.
+- **Dataset**: [IMDB Movie Reviews Dataset](https://ai.stanford.edu/~amaas/data/sentiment/)
+- **Key Outcomes**: Achieved high accuracy in detecting positive or negative sentiments with effective text vectorization.
+- **Repository**: [IMDB Sentiment Analysis](https://github.com/vmahawar/imdb-sentiment-analysis)
+
+---
+
+#### 3. **Handwritten Digit Recognition with Neural Networks**
+- **Description**: Developed a neural network model to classify handwritten digits using the MNIST dataset. The project focused on using dense and convolutional layers for feature extraction.
+- **Dataset**: [MNIST Dataset](http://yann.lecun.com/exdb/mnist/)
+- **Key Outcomes**: Achieved over 98% accuracy using an optimized neural network architecture.
+- **Repository**: [MNIST Digit Recognition](https://github.com/vmahawar/mnist-digit-recognition)
+
+---
+
+#### 4. **Time-Series Forecasting with LSTMs**
+- **Description**: Designed and trained an LSTM-based model for time-series forecasting using stock market data. The project included sequence generation, data normalization, and model evaluation.
+- **Dataset**: Custom Stock Market Dataset
+- **Key Outcomes**: Predicted future trends with reduced mean squared error by applying LSTM layers and dropout regularization.
+- **Repository**: [Stock Market Forecasting](https://github.com/vmahawar/stock-market-forecasting)
+
+---
+
+### My Dataset Collection Repository
+
+For a broader range of datasets I’ve explored in my machine learning and deep learning projects, visit my **[Dataset Collection Repository](https://github.com/vmahawar/data-science-datasets-collection)**. This repository consolidates popular datasets for experimentation and learning.
+
+---
+
+## 📜 License
+
+All projects in this repository are licensed under the **MIT License** for educational and non-commercial use.
+
+---
+
+## 🌐 Connect with Me
+
+Feel free to connect, collaborate, or provide feedback:
+
+- **LinkedIn**: [Vijay Mahawar](https://www.linkedin.com/in/vijay-mahawar)
+- **GitHub**: [vmahawar](https://github.com/vmahawar)