References
by Krishna Choppella, Dr. Uday Kamath, Boštjan Kaluža, Jennifer L. Reese, Richard M
Machine Learning: End-to-End guide for Java developers
- Machine Learning: End-to-End guide for Java developers
- Table of Contents
- Machine Learning: End-to-End guide for Java developers
- Credits
- Preface
- 1. Module 1
- 1. Getting Started with Data Science
- Problems solved using data science
- Understanding the data science problem - solving approach
- Acquiring data for an application
- The importance and process of cleaning data
- Visualizing data to enhance understanding
- The use of statistical methods in data science
- Machine learning applied to data science
- Using neural networks in data science
- Deep learning approaches
- Performing text analysis
- Visual and audio analysis
- Improving application performance using parallel techniques
- Assembling the pieces
- Summary
- 2. Data Acquisition
- 3. Data Cleaning
- 4. Data Visualization
- 5. Statistical Data Analysis Techniques
- 6. Machine Learning
- 7. Neural Networks
- 8. Deep Learning
- 9. Text Analysis
- 10. Visual and Audio Analysis
- 11. Mathematical and Parallel Techniques for Data Analysis
- 12. Bringing It All Together
- 1. Getting Started with Data Science
- 2. Module 2
- 1. Applied Machine Learning Quick Start
- 2. Java Libraries and Platforms for Machine Learning
- 3. Basic Algorithms – Classification, Regression, and Clustering
- 4. Customer Relationship Prediction with Ensembles
- 5. Affinity Analysis
- 6. Recommendation Engine with Apache Mahout
- 7. Fraud and Anomaly Detection
- 8. Image Recognition with Deeplearning4j
- 9. Activity Recognition with Mobile Phone Sensors
- 10. Text Mining with Mallet – Topic Modeling and Spam Detection
- 11. What is Next?
- A. References
- 3. Module 3
- 1. Machine Learning Review
- Machine learning – history and definition
- What is not machine learning?
- Machine learning – concepts and terminology
- Machine learning – types and subtypes
- Datasets used in machine learning
- Machine learning applications
- Practical issues in machine learning
- Machine learning – roles and process
- Machine learning – tools and datasets
- Summary
- 2. Practical Approach to Real-World Supervised Learning
- Formal description and notation
- Data transformation and preprocessing
- Feature relevance analysis and dimensionality reduction
- Model building
- Model assessment, evaluation, and comparisons
- Case Study – Horse Colic Classification
- Summary
- References
- 3. Unsupervised Machine Learning Techniques
- Issues in common with supervised learning
- Issues specific to unsupervised learning
- Feature analysis and dimensionality reduction
- Clustering
- Outlier or anomaly detection
- Real-world case study
- Summary
- References
- 4. Semi-Supervised and Active Learning
- Semi-supervised learning
- Active learning
- Case study in active learning
- Summary
- References
- 5. Real-Time Stream Machine Learning
- Assumptions and mathematical notations
- Basic stream processing and computational techniques
- Concept drift and drift detection
- Incremental supervised learning
- Incremental unsupervised learning using clustering
- Modeling techniques
- Partition based
- Hierarchical based and micro clustering
- Density based
- Grid based
- Validation and evaluation techniques
- Key issues in stream cluster evaluation
- Evaluation measures
- Cluster Mapping Measures (CMM)
- V-Measure
- Other external measures
- Unsupervised learning using outlier detection
- Case study in stream learning
- Summary
- References
- 6. Probabilistic Graph Modeling
- Probability revisited
- Graph concepts
- Bayesian networks
- Representation
- Inference
- Learning
- Learning parameters
- Learning structures
- Measures to evaluate structures
- Methods for learning structures
- Constraint-based techniques
- Search and score-based techniques
- 7. Deep Learning
- Multi-layer feed-forward neural network
- Inputs, neurons, activation function, and mathematical notation
- Multi-layered neural network
- Structure and mathematical notations
- Activation functions in NN
- Training neural network
- Empirical risk minimization
- Parameter initialization
- Loss function
- Gradients
- Feed forward and backpropagation
- How does it work?
- Regularization
- L2 regularization
- L1 regularization
- Limitations of neural networks
- Deep learning
- Building blocks for deep learning
- Rectified linear activation function
- Restricted Boltzmann Machines
- Autoencoders
- Unsupervised pre-training and supervised fine-tuning
- Deep feed-forward NN
- Deep Autoencoders
- Deep Belief Networks
- Deep learning with dropouts
- Sparse coding
- Convolutional Neural Network
- CNN Layers
- Recurrent Neural Networks
- Building blocks for deep learning
- Case study
- Summary
- References
- 8. Text Mining and Natural Language Processing
- NLP, subfields, and tasks
- Text categorization
- Part-of-speech tagging (POS tagging)
- Text clustering
- Information extraction and named entity recognition
- Sentiment analysis and opinion mining
- Coreference resolution
- Word sense disambiguation
- Machine translation
- Semantic reasoning and inferencing
- Text summarization
- Automating question and answers
- Issues with mining unstructured data
- Text processing components and transformations
- Topics in text mining
- Tools and usage
- Summary
- References
- NLP, subfields, and tasks
- 9. Big Data Machine Learning – The Final Frontier
- What are the characteristics of Big Data?
- Big Data Machine Learning
- Batch Big Data Machine Learning
- Case study
- Business problem
- Machine Learning mapping
- Data collection
- Data sampling and transformation
- Spark MLlib as Big Data Machine Learning platform
- A. Linear Algebra
- B. Probability
- D. Bibliography
- Index
- Empirical risk minimization
- Multi-layer feed-forward neural network
- Modeling techniques
- 1. Machine Learning Review
- Behnke, S. (2001). Learning iterative image reconstruction in the neural abstraction pyramid. International Journal of Computational Intelligence and Applications, 1(4), 427–438.
- Behnke, S. (2002). Learning face localization using hierarchical recurrent networks. In Proceedings of the 12th international conference on artificial neural networks (pp. 1319–1324).
- Behnke, S. (2003). Discovering hierarchical speech features using convolutional non-negative matrix factorization. In Proceedings of the international joint conference on neural networks, vol. 4 (pp. 2758–2763).
- Behnke, S. (2003). LNCS, Lecture notes in computer science: Vol. 2766. Hierarchical neural networks for image interpretation. Springer. Behnke, S. (2005). Face localization and tracking in the neural abstraction pyramid. Neural Computing and Applications, 14(2), 97–103.
- Casey, M. P. (1996). The dynamics of discrete-time computation, with application to recurrent neural networks and finite state machine extraction. Neural Computation, 8(6), 1135–1178.
- Rumelhart, D. E., Hinton, G. E., and Williams, R. J. (1986). Learning internal representations by error propagation. In Rumelhart, D. E. and McClelland, J. L., editors, Parallel Distributed Processing, volume 1, pages 318–362. MIT Press.
- Goller, C.; Küchler, A (1996). ""Learning task-dependent distributed representations by backpropagation through structure"". Neural Networks, IEEE. doi:10.1109/ICNN.1996.548916
- Hochreiter, Sepp. The vanishing gradient problem during learning recurrent neural nets and problem solutions. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 6(02): 107–116, 1998.
- G. E. Hinton, S. Osindero, and Y. The (2006). "A fast learning algorithm for deep belief nets," Neural Comput., vol. 18, pp. 1527–1554.
- G. E. Hinton (2002). "Training products of experts by minimizing contrastive divergence," Neural Comput., vol. 14, pp. 1771–1800.
- G. E. Hinton and R. R. Salakhutdinov (2006). "Reducing the dimensionality of data with neural networks," Science, vol. 313, no. 5786, pp. 504–507.
- Hinton, G. E., & Zemel, R. S. (1994). Autoencoders, minimum description length, and Helmholtz free energy. Advances in Neural Information Processing Systems, 6, 3–10.
- Y. Bengio, P. Lamblin, D. Popovici, and H. Larochelle. (2007). "Greedy layer-wise training of deep networks," in Advances in Neural Information Processing Systems 19 (NIPS'06) pp. 153–160.
- H. Larochelle, D. Erhan, A. Courville, J. Bergstra, and Y. Bengio (2007). "An empirical evaluation of deep architectures on problems with many factors of variation," in Proc. 24th Int. Conf. Machine Learning (ICML'07) pp. 473–480.
- P. Vincent, H. Larochelle, Y. Bengio, and P.-A. Manzagol (2008), "Extracting and composing robust features with denoising autoencoders," in Proc. 25th Int. Conf. Machine Learning (ICML'08), pp. 1096–1103.
- F.-J. Huang and Y. LeCun (2006). "Large-scale learning with SVM and convolutional nets for generic object categorization," in Proc. Computer Vision and Pattern Recognition Conf. (CVPR'06).
- F. A. Gers, N. N. Schraudolph, and J. Schmidhuber (2003). Learning precise timing with LSTM recurrent networks. The Journal of Machine Learning Research.
- Kyunghyun Cho et. al (2014). Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation. https://arxiv.org/pdf/1406.1078.pdf.
- https://brohrer.github.io/how_convolutional_neural_networks_work.html
- Henry W. Lin, Max Tegmark, David Rolnick (2016). Why does deep and cheap learning work so well? https://arxiv.org/abs/1608.08225
- Mike Schuster and Kuldip K. Paliwal (1997). Bidirectional Recurrent Neural Networks, Trans. on Signal Processing.
- H Lee, A Battle, R Raina, AY Ng (2007). Efficient sparse coding algorithms, In Advances in Neural Information Processing Systems
- Bengio Y. (2009). Learning deep architectures for AI, Foundations and Trends in Machine Learning 1(2) pages 1-127.
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