Deep Learning Quick Reference
This book is a practical guide to applying deep neural networks including MLPs, CNNs, LSTMs, and more in Keras and TensorFlow. Packed with useful hacks to solve real-world challenges along with the supported math and theory around each topic, this book will be a quick reference for training and optimize your deep neural networks.
Offered by
Difficulty Level
Intermediate
Completion Time
9h4m
Language
English
About Book
Who Is This Book For?
If you are a Data Scientist or a Machine Learning expert, then this book is a very useful read in training your advanced machine learning and deep learning models. You can also refer this book if you are stuck in-between the neural network modeling and need immediate assistance in getting accomplishing the task smoothly. Some prior knowledge of Python and tight hold on the basics of machine learning is required.
Deep Learning Quick Reference
- About Book
- Who Is This Book For?
- Book Content
Book content
chapters • 9h4m total length
The Building Blocks of Deep Learning
Using Deep Learning To Solve Regression Problems
Monitoring Network Training Using Tensor Board
Using Deep Learning To Solve Binary Classification Problems
Using Keras To Solve MultiClass Classification Problems
HyperParameter Optimization
Training a CNN From Scratch
Transfer Learning with Pretrained CNNs
Training an RNN from scratch
Training LSTMs with Word Embeddings From Scratch
Training Seq2Seq Models
Using Deep Reinforcement Learning
Deep Convolutional Generative Adversarial Networks
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