A Greater Foundation for Machine Learning Engineering- Reader’s Digest

Machine Learning Book

February 26, 2021 – In this post, Gloria Pullman performs the book review at Reader’s Digest Magazine on A Greater Foundation for Machine Learning Engineering – The Hallmarks of the Great Beyond in PyTorch, R, TensorFlow, and Python authored by Dr. Ganapathi Pulipaka. The comprehensive review provides an overview of what to expect in the book.

Dr. Ganapathi Pulipaka is a data science leader, author, and premier speaker with expertise in machine learning, cloud computing, aerospace, and IoT Edge computing.

Gloria Pullman from the book review publishing house got her hands on the upcoming paperback and completed her review, that is yet to be released from the publisher. Though the book’s price is undetermined at this stage, Gloria Pullman performed a more in-depth review of this book. This research scholarly illustrated book has more than 250 illustrations, which are covered in great detail. The simple models of supervised machine learning with Gaussian Naïve Bayes, Naïve Bayes, decision trees, classification rule learners, linear regression, logistic regression, local polynomial regression, regression trees, model trees, K-nearest neighbors, and support vector machines lay a more excellent foundation for statistics.

The author of the book Dr. Ganapathi Pulipaka, a top influencer of machine learning in America, has created this as a reference book for universities. This book contains an incredible foundation for machine learning and engineering beyond a compact manual. The author goes to extraordinary lengths to make academic machine learning and deep learning literature comprehensible to create a new body of knowledge. The book aims at readership from university students, enterprises, data science beginners, and deep learning engineers at scale for high-performance computing environments.

A Greater Foundation of Machine Learning Engineering covers a broad range of classical linear algebra and calculus with program implementations in PyTorch, TensorFlow, R, and Python with in-depth coverage. The author does not hesitate to go into math equations for each algorithm at length that usually many foundational machine learning books lack leveraging JupyterLab environment.  Newcomers can leverage the book from University or people from all walks of data science or software lives to the advanced practitioners of machine learning and deep learning.  Though the book title suggests machine learning, there are several implementations of deep learning algorithms, including deep reinforcement learning. 

The book’s mission is to hep build a strong foundation for machine learning and deep learning engineers with all the algorithms, processors to train and deploy into production for enterprise-wide machine learning implementations. This book also introduces all the concepts of natural language processing required for machine learning algorithms in Python. The book covers Bayesian statistics without assuming high-level mathematics or statistics experience from the readers. It delivers the core concepts and implementations required with R code with open datasets. The book also covers unsupervised machine learning algorithms with association rules and k-means clustering, metal-learning algorithms, bagging, boosting, random forests, and ensemble methods.

The book delves into the origins of deep learning in a scholarly way covering neural networks, restricted Boltzmann machines, deep belief networks, autoencoders, deep Boltzmann machines, LSTM, and natural language processing techniques with deep learning algorithms and math equations. It leverages the NLTK library of Python with PyTorch, Python, and TensorFlow’s installation steps, then demonstrates how to build neural networks with TensorFlow. Deploying machine learning algorithms require a blend of cloud computing platform, SQL databases, and NoSQL databases. Any data scientist with a statistics background that looks to transition into a machine learning engineer role requires an in-depth understanding of machine learning project implementations on Amazon, Google, or Microsoft Azure cloud computing platforms. The book provides real-world client projects for understanding the complete implementation of machine learning algorithms.

In the book’s foreword, the author explains the AGI, ‘the digital computing for machine learning will shift to “neuromorphic” brain-like in-memory computing as the future of the machine learning paradigm. Memristive crossbar architectures will be the linchpin for the future of deep learning as powerful in-memory computing engines for artificial neural networks.’ The author then explains the algorithms of swarm intelligence and in-memory computing. He further explains the bio-inspired deep computing with machine learning algorithms such as genetic algorithms, ant colony optimization algorithms, particle swarm optimization algorithm, artificial bee colony algorithm, bacterial foraging optimization algorithm, leaping frog algorithm, cuckoo search algorithm, firefly algorithm, bat algorithm, and flower pollination algorithms with mathematical equations and few real-world client projects.

The book sheds light on real-world research projects with supercomputers covering spiking neural networks, transistors, memristors, and neuromorphic computing. The author lights the torch on mathematical frontiers of plasma turbulence for nuclear fusion on the high-performance supercomputer Summit with convolutional neural networks and recurrent neural networks, providing insights into creating commercial-scale fusion power.

The book also covers advanced supercomputing projects to discover quantum materials and magnetic materials with neural networks. The reader can see the difference between the writing nowadays by farm-site first-time Ph.D. authors or programmers with development experience vs. a Postdoc in AI scholarly-authored book with in-depth research. The book further delves on SpiNNaker spiking networks with ARM hardware for MNIST digit recognition sets with supercomputer architecture compiler techniques to map the deep learning code to high-performance computing applications for massively parallel processing. With his experience working on hardware, research insights, deep learning, and supercomputing applications experience, the author also describes the critical hardware and infrastructure for designing, training, and implementing deep learning applications with petascale architecture with ARM hardware. The book’s unique portion covers the entire stack of deep learning frameworks ranging from all the vendors with a significant emphasis on commercial applications to building high-performance computing applications.

The book further delves into complex topics such as linear solvers for making parallel algebraic multigrid methods, vector calculus, parallel computing, MPI for data science and HPC, Spark Ecosystem for distributed learning HPC frameworks for solving some of the most complicated puzzles in supercomputing applications. Another factor that the book cover is the in-depth and extensive coverage of data engineering tools for machine learning implementation such as Hadoop HDFS, MapReduce, Apache Spark, Apache Sqoop, Apache Oozie, Apache Storm, Apache Flink, Hadoop YARN for building healthcare applications with some of the in-memory fabric real-word projects with a demonstration of Hadoop installation with author’s project implementation experiences. This book can go as a recommendation to Universities as a textbook at a Ph.D. level. It is so thorough on cloud-computing architectures, machine learning engineering, deep learning engineering, data engineering, streaming analytics engines, and cloud computing modeling.

The brightest spot in the book then comes with reinforcement learning. The book covers every algorithm of reinforcement learning with math equations and architectures. The book creates Springer Nature or ScienceDirect journal league of literature on a broader range of policy gradients, policy optimization methods, A2C/A3C, Proximal policy optimization algorithms, Trust region policy optimization algorithms, deep deterministic policy gradients, TD3 Algorithm, SAC algorithm, Q-Learning, and Deep-Q-Learning. It also covers topics such as  C-51 – A distributional reinforcement learning categorical algorithm, QR-Deep Q Learning, HER, World models, I2A, MBMF, MBVE, Temporal difference, Dynamic programming, Monte Carlo methods, Discretization, Tile coding, REINFORCE, and Cross-entropy algorithm with some of the implementation of these algorithms in PyTorch in a cookbook style.  

This book is a marvel that does not leave any application of machine learning and deep learning algorithm. It sets a more excellent foundation for newcomers and expands the horizons for experienced deep learning practitioners.  It is almost inevitable that there will be a series of more advanced algorithms follow-up books from the author in some shape or form after setting such a perfect foundation for machine learning engineering.



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