| 1.1Data Manipulation using Pandas.ipynb | 45.5 KB | |
| 1.2 Probability Distribution Using NumPy .ipynb | 39.1 KB | |
| 1.3 Data Visualization using Matplotlib.ipynb | 204 KB | |
| 1.4 Visualization using Seaborne.ipynb | 358.6 KB | |
| 1.5 Lab 1 Data Manipulation and Visualization using Python.ipynb | 2.6 KB | |
| 2.1 Auto Dashboarding using Python.ipynb | 8.4 KB | |
| 3 RNN Fundamentals for Gen AI.ipynb | 53.8 KB | |
| 3 Understanding Recurrent Neural Networks (RNNs) and its example in Sequence Generation.ipynb | 1.3 MB | |
| 4 Convolutional Neural Networks (CNNs).ipynb | 190 KB | |
| Day 2 Synthetic Data Generation using Python.ipynb | 180.6 KB | |
| Day 6 Basic Generative Adversarial Network (GAN) implemented using Python. .ipynb | 344 KB | |
| Day 6 GAN Fundamentals and Unsupervised Training.ipynb | 1.7 MB | |
| Day 7 Transformers.ipynb | 502.6 KB | |
| Lab 2 Implement RNN.ipynb | 22.6 KB | |
| demo_dataset.csv | 19.3 KB | |
| dummy | 1 bytes | |
