Let's try to enchance the performance of the previous model we have by incorporating new techniques you have learned.

If you want to refresh this dataset in your memory, review the EDA from the previous challenge.

<a href="https://colab.research.google.com/drive/12kyg3Lbp5HevGQexZf34QVane13ppI85?usp=sharing" target="_blank" style="color: #ff8a00; font-weight: 600; text-decoration: none; transition: 0.3s ease-in-out;"
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onmouseout="this.style.color='#d27204'">Exploratory Data Analysis</a>

<a href="https://colab.research.google.com/drive/1repLKgCuUD4udrELF9acVr9GWi94bkoX?usp=sharing" target="_blank" style="color: #ff8a00; font-weight: 600; text-decoration: none; transition: 0.3s ease-in-out;"
onmouseover="this.style.color='#ff0000'"
onmouseout="this.style.color='#d27204'">Solution</a>

Dive into the world of neural network development with an immersive program that blends foundational knowledge with advanced techniques. The course emphasizes hands-on learning, providing learners with the opportunity to apply these techniques. Additionally, you will face numerous data preprocessing tasks and real-world examples of neural network training, so stay prepared and motivated, as it will present a significant challenge for you.

This segment introduces the fundamentals of Keras through hands-on experiences with the Spotify dataset, covering everything from layer configuration and model creation to compilation, data preprocessing, and training.

The regularization module, utilizing the Mushrooms dataset, addresses the critical issues of overfitting and underfitting, explaining the concept of regularization and its importance in model training.

Exploring cutting-edge methodologies, this section delves into the Diamonds dataset to uncover the intricacies of optimizers, learning rate adjustments, TensorFlow Datasets, data generators, and advanced modeling techniques like non-sequential models, transfer learning, and multitask learning in a comprehensive overview.

Neural Networks with TensorFlow

Challenge