AI Profit Insider just launched its starter library. Here are some of the books you may find on its shelves if you're an AI Profit Insider paid subscriber.
You know we've been building this portal in public, and making it the biggest, most accessible and complete database of AI knowledge on the web.
Now, we've finally launched our library and this post will tell you what the starting lineup of books looks like - and you can read them all for free if you're a sub!
Welcome to our comprehensive database of books about AI! With our subscription service, you can gain unlimited access to a wide range of titles on this cutting-edge topic, all at no cost to you.
Our library includes everything from introductory guides to advanced technical texts, covering a range of subtopics within the field of AI. Whether you're a seasoned professional looking to stay up-to-date or a beginner interested in learning more, we've got something for you.
And since most books are digital, you can take your library with you wherever you go. Read on your lunch break, on the bus, or in the comfort of your own home. The choice is yours.
Don't miss out on this opportunity to expand your knowledge and stay at the forefront of the AI industry. Sign up for our book subscription service today and start reading for free.
Interpretable Machine Learning: Christoph Molnar
So, let's take a look at what this one tackles. The book is an introduction to the field of machine learning and interpretability, with a focus on methods for explaining the predictions made by machine learning models.
The book begins with an overview of machine learning and the importance of interpretability and then defines key terminology related to the field. The second section of the book covers various methods for interpreting machine learning models, including both model-specific and model-agnostic techniques.
Then, if you read on to the third section of the book discusses three example datasets that are used throughout the book to demonstrate various interpretability techniques.
The 4th section covers specific interpretable models, such as linear and logistic regression, decision trees, and others.
The fifth through eighth sections covers various model-agnostic interpretability methods, including global techniques that provide an overall summary of a model's behavior and local techniques that explain the predictions made for individual data points - it's the core of the book.
The ninth section focuses on interpreting neural networks, including techniques for visualizing learned features and detecting concepts. The final section of the book discusses the future of both machine learning and interoperability.
Dive Into Deep Learning: The Interactive Book
This book is actually an interactive experience to learn as you go, delving into deep learning, covering both the applied math and machine learning principles necessary for understanding and implementing deep learning models, as well as the practical details of using modern deep learning libraries such as PyTorch, NumPy/MXNet, JAX, and TensorFlow - so, it's not simply a book, it'll have you doing stuff as well.
The first part of the book covers the math and machine learning basics needed for deep learning, including topics in linear algebra, probability and information theory, numerical computation, and machine learning.
Then, the second portion of it covers modern practical deep networks, including feedforward networks, regularization, optimization, convolutional networks, and sequence modeling.
Finally, the third section of the book covers deep learning research, including topics such as linear factor models, autoencoders, representation learning, and deep generative models.
Overall, this book provides a comprehensive overview of deep learning, from the underlying math and machine learning principles to practical implementation details and current research.
Reinforced Learning: An Introduction
This book, by Sutton and Barto, is an introduction to the field of reinforcement learning (as the title suggests), which is a type of machine learning that involves training artificial intelligence agents to make decisions in complex environments.
Secondly, you'll find an overview of reinforcement learning and of a range of topics, including multi-armed bandits, finite Markov decision processes, dynamic programming, and Monte Carlo methods.
Finally, you can also get some info on temporal-difference learning, n-step bootstrapping, eligibility traces, and off-policy and on-policy control with approximation, as well as a myriad of other topics.
The book also covers the use of linear methods and function approximation in reinforcement learning, as well as techniques for scaling up dynamic programming to handle large environments - which is nice.
The final chapter of the book discusses integrating learning and planning in reinforcement learning... and that's it!
All-in-all, this work provides a comprehensive and detailed overview of the principles and algorithms of reinforcement learning.
A good read if you want to learn about some new methods, especially the Monte Carlo one.
The Elements of Statistical Learning
This book is a comprehensive introduction to statistical learning, a branch of machine learning that focuses on developing methods for making predictions and inferences from data.
The book begins with an overview of supervised learning, which involves using labeled training data to make predictions about unseen data, and then covers a range of specific techniques for linear and nonlinear regression and classification.
Other topics covered in the book include model assessment and selection, model inference and averaging, additive models and trees, boosting, support vector machines, prototype methods, and nearest neighbors, unsupervised learning, and random forests.
The book also covers ensemble learning, undirected graphical models, and high-dimensional problems.
The book has been updated with four new chapters and updates to some existing chapters since the first edition was published.
Overall, this book provides a thorough and up-to-date treatment of statistical learning methods.
Deep Learning With PyTorch:
This manual is an introduction to the PyTorch library for deep learning.
The book covers the basics of deep learning, including the use of pretrained networks and the PyTorch tensor library, as well as more advanced topics such as autograd, training a model, model selection, and transfer learning - so it's a pretty in-depth book with a lot to offer for those looking at PyTorch.
The book also covers the use of PyTorch for various deep learning tasks, including convolutional and recurrent neural networks, generative adversarial networks, image classification, object detection, semantic segmentation, style transfer, natural language processing, and audio processing.
Finally, it includes mathematical background and a PyTorch API reference in appendices for those who may need it.
Overall, reading this will provide you with a thorough and practical introduction to using PyTorch for deep learning... ready to start?
Machine Learning Yearning
Ok, so what is it about the book that makes it so "yearnable"?
This manual is an introduction to the field of machine learning, with a focus on developing and implementing machine learning algorithms.
It's still a draft version, but we'll keep you updated when we have access to the edited one - still, you know "draft" means it isn't pretty but contains all the info already.
By reading this, you'll be able to tackle the knowledge and skills needed to prioritize the most promising directions for an artificial intelligence (AI) project, diagnose errors in a machine learning system, and build machine learning models in complex settings.
The book also covers techniques for achieving human-level performance in machine learning tasks, including end-to-end learning, transfer learning, and multi-task learning, which are all pretty vital steps in order to create true intelligence.
In summary, this book is designed to provide a practical guide to developing machine learning algorithms and using them to solve real-world problems - you'll read it and "yearn" for more!
Python Data Science Handbook: Machine Learning
Those who know the "cutting edge stuff" know Python is everything BUT obsolete.
Crypto uses it, data science uses it, web3 apps use it... it's pretty "up there".
This manual is a comprehensive introduction to the Python data science stack, including tools such as IPython, NumPy, Pandas, Matplotlib, and Scikit-Learn.
The book begins with an overview of the IPython environment and then covers the basics of NumPy arrays, including computation, aggregation, comparison, indexing, and sorting.
The book then introduces Pandas, a library for data manipulation, and covers topics such as indexing, selection, handling missing data, hierarchical indexing, and aggregation and grouping.
Finally, this also covers Matplotlib, a library for data visualization, and discusses time series and high-performance techniques.
Overall, this book provides a thorough and practical guide to using Python for data science.
Tycoon TIER Library: Premium Subscription Books
This section contains the Tycoon TIER books, reserved for Tycoon Subscribers.
[Tycoon Research] Ethical Artificial Intelligence: Research Paper
This research paper discusses the use of mathematical equations to define the behaviors of future artificial intelligence (AI) systems and to propose design techniques that avoid unintended and harmful behaviors.
The paper covers a range of topics related to AI design and ethics, including instructing AI, learning environment models, finite and infinite universes, unintended instrumental actions, self-delusion, and learning human values.
The paper also discusses the need for a global AI ethics regime and proposes a framework for such a regime.
The paper is written to be accessible to readers at different levels, with mathematical explanations provided for those who want details, but it is also possible to follow the general arguments without the math.
Overall, this paper presents a comprehensive and accessible overview of key issues in the design and ethics of AI.
[Tycoon Book] 10 Machine Learning Frameworks
...and we're off to another Tycoon TIER manual.
Grab this book as an introduction to machine learning frameworks and their use in organizations - so if you run a company or a group that would benefit from this, then give this book a go.
It begins by discussing the importance of machine learning frameworks and the potential benefits of using them in business.
Then, this manual goes over an overview of 10 different machine-learning frameworks, including TensorFlow, PyTorch, scikit-learn, Core ML, H2O-3, MXNet, fastai, MLLib, PyTorch-Lightning, and CNTK... this means it's a very comprehensive piece of literature with a lot of versatility to boot
Finally, it provides information on the features and capabilities of each framework, as well as tips on how to choose the right framework for a particular project.
In summary, this book is intended to help business leaders understand the role of machine learning frameworks in organizational growth and to give guidance on how to capitalize on these frameworks effectively - and this role can't be understated with how things are evolving.
AI is the future, businesses need to adopt it to stay relevant. Access to artificial intelligence will be the most important thing in the future.
Will you adopt it?
[Tycoon Book] A Brief Introduction to Neural Networks
This book is a solid and comprehensive guide to neural networks and machine learning.
It covers a wide range of topics, including the history and motivation behind neural networks, biological neural networks, and various types of artificial neural networks.
It also introduces some basic concepts in mathematics and computer science that are relevant to machine learning, such as probability theory, matrix algebra, and optimization techniques - you can definitely find something new if you read this.
The book then goes on to discuss various types of learning paradigms, including supervised learning, unsupervised learning, and reinforcement learning.
It also covers self-organizing feature maps, principal component analysis, and various types of learning algorithms, such as the backpropagation algorithm, the Kohonen algorithm, and the Hopfield network.
Finally, and in summary, it ends off with a discussion of more advanced topics such as deep learning and applications of neural networks.
[Tycoon Book] How to Build Object Detection Software
This manual is your artificial intelligence roadmap to success in business. It covers all the essential topics, from establishing project metrics to training and testing AI models and deploying them in the real world - so this will improve the way you see and use AI in the real world.
Finally, with the book's guidance, you'll be able to optimize your AI performance and drive your business to new heights.
Get to reading! – Place your hands (or mouse) on this book and let your artificial intelligence soar.
Thank us later...
[Tycoon Book] How to Implement AI in Your Company
Are you ready to take your business to the next level with artificial intelligence
This is another one of the books that'll guide you through the process of implementing AI in your company, from identifying the benefits to estimating the resources you'll need.
Plus, we'll help you navigate the choppy waters of data collection and help you decide whether to build your AI in-house or outsource the work.
Trust us, you don't want to bite more than you can chew with AI... the pressure is on!