Journey into Machine Learning with Sklearn and Tensorflow

Introduction to Neural Networks with TensorFlow

Start your exploration of neural networks with a beginner's course on TensorFlow, using the scikit-learn Digits Dataset. Learn neural network basics and deep learning by developing, training, and evaluating models with TensorFlow. Understand different neural network architectures and improve them, emphasizing the importance of data preparation in deep learning.

Lessons and practices

Visualize an Image and Verify TensorFlow Version

Exploring Binary Color Map for Digit Images

Exploring Image Representation with Color Maps

Displaying the Flattened Digits Dataset

Shuffling Digits Dataset for Better Training

Standardizing Data for Neural Networks

Data Preprocessing: Fit or Fit-Transform?

Data Preprocessing for Neural Networks

Defining a Neural Network Model Architecture

Fix the Neural Network Model Architecture

Adding Hidden Layers to Neural Networks

Implementing the Sigmoid Activation Function

Implementing the ReLU Activation Function

Neuron Output Calculation Fix

Defining Second Layer of Neurons

Exploring the Neural Network Architecture

Applying Optimal Settings for Neural Network Compilation

Correcting Neural Network Errors

Building a Classification Neural Network

Observe the Learning Trajectory of a Neural Network

Enhancing Neural Network Training with Epochs

Adding a Second Layer to the Neural Network

Modifying and Observing a Neural Network

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