Book description
Solve different problems in modelling deep neural networks using Python, Tensorflow, and Keras with this practical guide
About This Book
- Practical recipes on training different neural network models and tuning them for optimal performance
- Use Python frameworks like TensorFlow, Caffe, Keras, Theano for Natural Language Processing, Computer Vision, and more
- A hands-on guide covering the common as well as the not so common problems in deep learning using Python
Who This Book Is For
This book is intended for machine learning professionals who are looking to use deep learning algorithms to create real-world applications using Python. Thorough understanding of the machine learning concepts and Python libraries such as NumPy, SciPy and scikit-learn is expected. Additionally, basic knowledge in linear algebra and calculus is desired.
What You Will Learn
- Implement different neural network models in Python
- Select the best Python framework for deep learning such as PyTorch, Tensorflow, MXNet and Keras
- Apply tips and tricks related to neural networks internals, to boost learning performances
- Consolidate machine learning principles and apply them in the deep learning field
- Reuse and adapt Python code snippets to everyday problems
- Evaluate the cost/benefits and performance implication of each discussed solution
In Detail
Deep Learning is revolutionizing a wide range of industries. For many applications, deep learning has proven to outperform humans by making faster and more accurate predictions. This book provides a top-down and bottom-up approach to demonstrate deep learning solutions to real-world problems in different areas. These applications include Computer Vision, Natural Language Processing, Time Series, and Robotics.
The Python Deep Learning Cookbook presents technical solutions to the issues presented, along with a detailed explanation of the solutions. Furthermore, a discussion on corresponding pros and cons of implementing the proposed solution using one of the popular frameworks like TensorFlow, PyTorch, Keras and CNTK is provided. The book includes recipes that are related to the basic concepts of neural networks. All techniques s, as well as classical networks topologies. The main purpose of this book is to provide Python programmers a detailed list of recipes to apply deep learning to common and not-so-common scenarios.
Style and approach
Unique blend of independent recipes arranged in the most logical manner
Table of contents
- Preface
-
Programming Environments, GPU Computing, Cloud Solutions, and Deep Learning Frameworks
- Introduction
- Setting up a deep learning environment
- Launching an instance on Amazon Web Services (AWS)
- Launching an instance on Google Cloud Platform (GCP)
- Installing CUDA and cuDNN
- Installing Anaconda and libraries
- Connecting with Jupyter Notebooks on a server
- Building state-of-the-art, production-ready models with TensorFlow
- Intuitively building networks with Keras
- Using PyTorch’s dynamic computation graphs for RNNs
- Implementing high-performance models with CNTK
- Building efficient models with MXNet
- Defining networks using simple and efficient code with Gluon
-
Feed-Forward Neural Networks
- Introduction
- Understanding the perceptron
- Implementing a single-layer neural network
- Building a multi-layer neural network
- Getting started with activation functions
- Experiment with hidden layers and hidden units
- Implementing an autoencoder
- Tuning the loss function
- Experimenting with different optimizers
- Improving generalization with regularization
- Adding dropout to prevent overfitting
- Convolutional Neural Networks
- Recurrent Neural Networks
- Reinforcement Learning
- Generative Adversarial Networks
- Computer Vision
- Natural Language Processing
- Speech Recognition and Video Analysis
- Time Series and Structured Data
- Game Playing Agents and Robotics
-
Hyperparameter Selection, Tuning, and Neural Network Learning
- Introduction
- Visualizing training with TensorBoard and Keras
- Working with batches and mini-batches
- Using grid search for parameter tuning
- Learning rates and learning rate schedulers
- Comparing optimizers
- Determining the depth of the network
- Adding dropouts to prevent overfitting
- Making a model more robust with data augmentation
- Network Internals
- Pretrained Models
Product information
- Title: Python Deep Learning Cookbook
- Author(s):
- Release date: October 2017
- Publisher(s): Packt Publishing
- ISBN: 9781787125193
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