A great guide to machine learning. It helped launch my third career!
Machine Learning with TensorFlow gives readers a solid foundation in machine-learning concepts plus hands-on experience coding TensorFlow with Python.
Part 1: My Machine Learning Rig
1. A machine-learning odyssey
1.1. Machine learning fundamentals
1.1.1. Parameters
1.1.2. Learning and inference
1.2. Data representation and features
1.3. Distance Metrics
1.4. Types of Learning
1.4.1. Supervised Learning
1.4.2. Unsupervised Learning
1.4.3. Reinforcement Learning
1.5. TensorFlow
1.6. Overview of future chapters
1.7. Summary
2. TensorFlow essentials
2.1. Ensuring TensorFlow works
2.2. Representing tensors
2.3. Creating operators
2.4. Executing operators with sessions
2.4.1. Understanding code as a graph
2.4.2. Session configurations
2.5. Writing code in Jupyter
2.6. Using variables
2.7. Saving and Loading Variables
2.8. Visualizing data using TensorBoard
2.8.1. Implementing a moving average
2.8.2. Visualizing the moving average
2.9. Summary
Part 2: Core Learning Algorithms
3. Linear regression and beyond
3.1. Formal notation
3.1.1. How do you know the regression algorithm is working?
3.2. Linear Regression
3.3. Polynomial Model
3.4. Regularization
3.5. Application of linear regression
3.6. Summary
4. A gentle introduction to classification
4.1. Formal Notation
4.2. Measuring Performance
4.2.1. Accuracy
4.2.2. Precision and Recall
4.2.3. Receiver operating characteristic curve
4.3. Using linear regression for classification
4.4. Using logistic regression
4.4.1. Solving one-dimensional logistic regression
4.4.2. Solving two-dimensional logistic regression
4.5. Multiclass classifier
4.5.1. One versus all
4.5.2. One versus one
4.5.3. Softmax regression
4.6. Application of classification
4.7. Summary
5. Automatically clustering data
5.1. Traversing files in TensorFlow
5.2. Extracting features from audio
5.3. K-means clustering
5.4. Audio segmentation
5.5. Clustering using a self-organizing map
5.6. Application of clustering
5.7. Summary
6. Hidden Markov models
6.1. Example of a not-so-interpretable model
6.2. Markov Model
6.3. Hidden Markov Model
6.4. Forward algorithm
6.5. Viterbi decode
6.6. Uses of Hidden Markov Models
6.6.1. Modeling a video
6.6.2. Modeling DNA
6.6.3. Modeling an image
6.7. Application of hidden Markov models
6.8. Summary
Part 3: The Neural Network Paradigm
7. A peek into autoencoders
7.1. Neural Networks
7.2. Autoencoder
7.3. Batch training
7.4. Working with images
7.5. Application of autoencoders
7.6. Summary
8. Reinforcement learning
8.1. Formal notions
8.1.1. Policy
8.1.2. Utility
8.2. Applying reinforcement learning
8.3. Implementation
8.4. Applications of reinforcement learning
8.5. Summary
9. Convolutional neural networks
9.1. Drawback of neural networks
9.2. Convolutional neural networks
9.3. Preparing the image
9.3.1. Generate filters
9.3.2. Convolve using filters
9.3.3. Max-pooling
9.4. Implementing a convolutional neural network in TensorFlow
9.4.1. Measuring performance
9.4.2. Training the classifier
9.5. Tips and tricks to improve performance
9.6. Application of convolutional neural networks
9.7. Summary
10. Recurrent neural networks
10.1. Contextual information
10.2. Introduction to recurrent neural networks
10.3. Implementing a recurrent neural network
10.4. A predictive model for timeseries data
10.5. Application of recurrent neural networks
10.6. Summary
11. Sequence-to-sequence models for chatbots
11.1. Building on Classification and RNNs
11.2. Seq-to-seq architecture
11.3. Vector representation of symbols
11.4. Putting it all together
11.5. Gathering dialogue data
11.6. Summary
12. Utility landscape
12.1. Preference model
12.2. Image embedding
12.3. Ranking images
12.4. Summary
12.5. What’s next?
Appendixes
Appendix A: Installation
A.1. Installing TensorFlow using Docker
A.1.1. Install Docker on Windows
A.1.2. Install Docker on Linux
A.1.3. Install Docker on OSX
A.1.4. How to user Docker
A.2. Installing Matplotlib
About the Technology
TensorFlow, Google's library for large-scale machine learning, simplifies often-complex computations by representing them as graphs and efficiently mapping parts of the graphs to machines in a cluster or to the processors of a single machine.
About the book
Machine Learning with TensorFlow gives readers a solid foundation in machine-learning concepts plus hands-on experience coding TensorFlow with Python. You'll learn the basics by working with classic prediction, classification, and clustering algorithms. Then, you'll move on to the money chapters: exploration of deep-learning concepts like autoencoders, recurrent neural networks, and reinforcement learning. Digest this book and you will be ready to use TensorFlow for machine-learning and deep-learning applications of your own.
What's inside
- Matching your tasks to the right machine-learning and deep-learning approaches
- Visualizing algorithms with TensorBoard
- Understanding and using neural networks
- customers also bought these items
- Practical Recommender Systems
- Kubernetes in Action
- The Quick Python Book, Third Edition
- Deep Learning with R
- Machine Learning Systems
- Grokking Deep Learning
FREE domestic shipping on three or more pBooks
The many examples provide excellent hands-on experience.
Helped me to jump-start working with TensorFlow.
Learn how to use TensorFlow to power your machine-learning projects with this fast-paced yet unintimidating book!