In this session (Day 4 of our Deep Learning course), we move from theory to practice by implementing a Convolutional Neural Network (CNN) using TensorFlow and Keras.
This class focuses on how CNNs are actually trained and how to improve their performance in real-world scenarios.
📌 Topics covered:
* CNN implementation step-by-step
* Training neural networks
* Backpropagation (intuitive understanding)
* Optimizers (SGD vs Adam)
* Learning rate tuning
* Dropout and regularization
* Debugging training issues
* Improving model performance
💻 Tools used:
* Python
* TensorFlow / Keras
📊 Dataset:
* MNIST handwritten digits
🎯 By the end of this session, you will be able to:
* Build a CNN from scratch
* Train and evaluate your model
* Fix common deep learning problems
* Improve accuracy using optimization techniques
🔔 This is part of a full Deep Learning course series.
👉 Stay tuned for upcoming sessions on:
* Transfer Learning
* Real-world AI projects
* Advanced architectures
#DeepLearning #CNN #TensorFlow #MachineLearning #AI #Keras #DataScience
