Python Machine Learning | Page 2

Explore our comprehensive tutorials on Python Machine Learning. Master skills and stay updated with the latest tech insights.

Boost CNN Performance: Data Augmentation & Transfer Learning

Boost CNN Performance: Data Augmentation & Transfer Learning

Python Machine Learning In the last lesson, we built a Convolutional Neural Network (CNN) from scratch to classify images from the MNIST dataset. We learned how to design layers, compile the model, and train it to recognize handwritten digits. While building a CNN from scratch is a great way to understand its... Read more
Build a CNN for MNIST Image Classification Using Keras

Build a CNN for MNIST Image Classification Using Keras

Python Machine Learning In the last lesson, we explored the basics of Convolutional Neural Networks (CNNs), which are powerful tools for image processing. We learned how CNNs use filters to detect patterns like edges, textures, and shapes in images. We also discussed the roles of convolutional layers, pooling layers,... Read more
Introduction to Convolutional Neural Networks for Image Processing

Introduction to Convolutional Neural Networks for Image Processing

Python Machine Learning In the previous lesson, we explored Hyperparameter Tuning with Keras Tuner, where we learned how to optimize neural network models by adjusting parameters like learning rate, batch size, and the number of layers. This process helped us improve model performance and efficiency. Now, we'll dive... Read more
Hyperparameter Tuning with Keras Tuner: A Step-by-Step Guide

Hyperparameter Tuning with Keras Tuner: A Step-by-Step Guide

Python Machine Learning In the previous lesson, we explored techniques like dropout and batch normalization to improve model performance. These methods help prevent overfitting and stabilize training, but they are just one piece of the puzzle. To truly optimize your neural network, you need to fine-tune its... Read more
Improve Neural Network Performance with Keras: Dropout & Batch Norm

Improve Neural Network Performance with Keras: Dropout & Batch Norm

Python Machine Learning In the previous lesson, we explored how to split data into training, validation, and testing sets, which is crucial for evaluating model performance. Now, we'll dive into techniques that improve neural network performance and prevent overfitting. Overfitting happens when a model learns the... Read more
How to Split Data for Training, Validation, and Testing in Keras

How to Split Data for Training, Validation, and Testing in Keras

Python Machine Learning In the previous lesson, we built a simple neural network using Keras. We learned how to define layers, compile the model, and run it on a dataset. However, we didn't focus on how to evaluate the model's performance properly. This is where splitting data into training, validation, and testing... Read more
Build a Simple Neural Network with Keras: Step-by-Step Guide

Build a Simple Neural Network with Keras: Step-by-Step Guide

Python Machine Learning In the previous lesson, we explored backpropagation and optimization techniques like Gradient Descent and Adam. These methods help neural networks learn by adjusting weights to minimize errors. Now, we will apply these concepts practically by building a simple neural network using Keras, a... Read more
Master Backpropagation and Optimization in Deep Learning

Master Backpropagation and Optimization in Deep Learning

Python Machine Learning In the last lesson, we explored activation functions like ReLU, sigmoid, and softmax, which are key to adding non-linearity to neural networks. These functions help models learn complex patterns in data. Now, we'll dive into backpropagation and optimization, the core processes that make neural... Read more
Master Activation Functions: ReLU, Sigmoid, Softmax in Neural Networks

Master Activation Functions: ReLU, Sigmoid, Softmax in Neural Networks

Python Machine Learning In the last lesson, we explored the basics of neural networks, focusing on how layers of neurons work together to process data. We learned how weights and biases help these networks make decisions. Now, it's time to dive deeper into one of the most critical parts of neural networks: activation... Read more