- eBook:Applied Deep Learning: A Case-Based Approach to Understanding Deep Neural Networks
- Author:Umberto Michelucci
- Edition:1st ed. edition
- Format:PDF, ePUB
The next section talks about more complicated neural network architectures with several layers and neurons and explores the problem of random initialization of weights. An entire chapter is dedicated to a complete overview of neural network error analysis, giving examples of solving problems originating from variance, bias, overfitting, and datasets coming from different distributions.
Applied Deep Learning also discusses how to implement logistic regression completely from scratch without using any Python library except NumPy, to let you appreciate how libraries such as TensorFlow allow quick and efficient experiments. Case studies for each method are included to put into practice all theoretical information. You’ll discover tips and tricks for writing optimized Python code (for example vectorizing loops with NumPy).
What You Will Learn
- Implement advanced techniques in the right way in Python and TensorFlow
- Debug and optimize advanced methods (such as dropout and regularization)
- Carry out error analysis (to realize if one has a bias problem, a variance problem, a data offset problem, and so on)
- Set up a machine learning project focused on deep learning on a complex dataset
Readers with a medium understanding of machine learning, linear algebra, calculus, and basic Python programming.
Chapter 2: Single Neuron
Chapter 3: Feedforward Neural Networks
Chapter 4: Training Neural Networks
Chapter 5: Regularization
Chapter 6: Metric Analysis
Chapter 7: Hyperparameter Tuning
Chapter 8: Convolutional and Recurrent Neural Networks
Chapter 9: A Research Project
Chapter 10: Logistic Regression from Scratch