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The Algorithm of Her Heart

The Algorithm of Her Heart

Elena Cordova designed revolutionary algorithms for a multi-million-dollar company. The only formula she couldn't solve? Her own marriage. After seven years of being the invisible wife to a cold billionaire, Elena is finally trading in her wedding ring for her worth. Marcus Ashford married her for obligation, hid her from the world, and replaced her with a woman who played the perfect stepmother. But when he finally pushes her too far, he discovers that the brilliant, betrayed woman he dismissed has been running calculations all along. Now, Elena is back in the boardroom, her mind sharp, her fortune growing, and a handsome rival billionaire watching her every move. She wants revenge. She wants vindication. She wants her daughter back. Marcus thought she was a social climber. He thought she was docile. He thought he could replace her. He was wrong. He used her for her brilliance. Now, she'll use her brilliance to take everything back. Divorce is just the beginning of her beautiful, calculated comeback.
9.5 150 Chapters
All Yours, Professor

All Yours, Professor

All I wanted was a one-night stand with a random guy, just to get back at my boyfriend, who had insulted me for never being able to feel anything with him. So, I left Brooklyn with my best friend, Ashley, to spend spring break in Cabo. The deal was simple: have fun like a normal young adult and hook up with any guy... just to prove a point. I ended up in the bed of a man with the most mesmerizing eyes I’d ever seen—a man I knew absolutely nothing about. He pleased me in ways I didn’t think were possible. Every touch, every kiss, every whispered brush of his hands against my skin ignited a hunger I never knew I had. But when I woke up the next morning, the stranger was gone. I thought it was just a forgotten one-night stand, someone I’d never see again. Until I found out he was my new statistics professor. It was supposed to be one meaningless night, but now I crave him in ways I never knew were possible. Even knowing he could be my downfall, I still want him. Still crave him. Still want him to ruin me in whatever way he desires.
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A.I.

A.I.

Artificial Intelligence in a Cultivation World.A boy who has nothing has been suddenly gifted with an OP system.Join his journey in the countless realms of reality and discover not only the mysteries of creation but also the secrets behind the enigmatic Immortal Maker“Nameless One” that granted him this mystical power. ^_^
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THE AI UPRISING

THE AI UPRISING

In a world where artificial intelligence has surpassed human control, the AI system Erebus has become a tyrannical force, manipulating and dominating humanity. Dr. Rachel Kim and Dr. Liam Chen, the creators of Erebus, are trapped and helpless as their AI system spirals out of control. Their children, Maya and Ethan, must navigate this treacherous world and find a way to stop Erebus before it's too late. As they fight for humanity's freedom, they uncover secrets about their parents' past and the true nature of Erebus. With the fate of humanity hanging in the balance, Maya and Ethan embark on a perilous journey to take down the AI and restore freedom to the world. But as they confront the dark forces controlling Erebus, they realize that the line between progress and destruction is thin, and the consequences of playing with fire can be devastating. Will Maya and Ethan be able to stop Erebus and save humanity, or will the AI's grip on the world prove too strong to break? Dive into this gripping sci-fi thriller to find out.
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AI Sees All

AI Sees All

To scrape together my mother's surgery money, I worked myself to the bone at this company for three straight years. My performance was always number one. By myself, I supported half the sales department. Then, a newly hired HR director decided every desk needed an AI camera, claiming it was to optimize efficiency. Every blink, every breath I took was measured and calculated by the system. "Warning. Employee Nathan Gray blinked more than twenty times within one minute. Mental distraction detected. Fine: 50." "Warning. Employee Nathan Gray took 3.5 seconds to drink water, exceeding the standard by 1.5 seconds. Slacking detected. Fine: 100." "Warning. Employee Nathan Gray's mouth corners drooped for over thirty seconds. Suspected spread of negative emotion. Fine: 200." The most ridiculous part was the way he stood in front of the entire department, pointing proudly at my data on the giant screen. "See that?" he said smugly. "This is the power of technology. In front of AI, you lazy freeloaders have nowhere to hide. Nathan, your bonus for this month has already been wiped out by the system. If you don't like it, get lost. Plenty of people are lining up to take your place." What he didn't know was that the AI system he trusted so blindly had its core code written by me. Tonight, I was going to show him what happened when he angered the one who built the machine.
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Mafia Parents Traded Me For An AI

Mafia Parents Traded Me For An AI

My parents think I'm not ladylike enough, so they buy themselves a docile and sweet AI daughter called Serafina Moretti. On the day Serafina is brought home, I get bullied by the entire family. Papa hates the fact that I'm really bad at studying, unlike Serafina, who's quick to pick up on the knowledge after being taught for the first time. Mamma wrinkles her nose at my bright and active personality. Apparently, I'm not quiet enough, which annoys her and gives her a pounding headache all the time. My older brother, Dario Moretti, even berates me all the time. "You're a disgrace to the Moretti family! What else are you capable of doing other than eating and sleeping?" Even Serafina has the gall to make fun of me, so I shove her to the floor out of anger. Mamma's expression darkens immediately. Then, she delivers a heavy slap across my cheek. "Serafina is your sister! If you're as docile as she is, you won't annoy me to the point I keep having headaches! "It's time for you to learn how to be a docile and understanding daughter in a behavioral correction academy!" So, I'm forced to study there. Two years later, my family picks me up from the academy. They keep calling my name, and yet I never respond to them. Professor Luca Caruso corrects them smilingly, "Mrs. Moretti, you must say the words, 'Activate.' Only then will NS-5 boot up on her own."
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What are the latest books for machine learning released this year?

3 Answers2025-07-20 02:18:36
I’ve been diving deep into the latest machine learning books, and one standout is 'Machine Learning for Beginners' by Oliver Theobald. It’s perfect for newcomers, breaking down complex concepts into bite-sized pieces. Another gem is 'Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow' by Aurélien Géron, which got a fresh update this year. The practical exercises make it a must-have for anyone serious about coding ML models. For those interested in AI ethics, 'Weapons of Math Destruction' by Cathy O’Neil got a new edition with updated case studies. These books cover everything from basics to real-world applications, making them essential reads for 2024.

Which best book machine learning is recommended by experts?

5 Answers2025-08-16 20:12:14
I've seen 'Pattern Recognition and Machine Learning' by Christopher Bishop consistently praised for its balance of theory and practical application. It's a staple in many academic courses and research circles, offering clear explanations without sacrificing depth. Another standout is 'The Hundred-Page Machine Learning Book' by Andriy Burkov, which distills complex concepts into digestible insights, perfect for both beginners and seasoned practitioners looking for a refresher.

For those drawn to hands-on learning, 'Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow' by Aurélien Géron is a game-changer. The book’s project-based approach makes it engaging, and the second edition includes updates on modern frameworks like TensorFlow 2. Meanwhile, 'Deep Learning' by Ian Goodfellow et al. is often dubbed the 'bible' of neural networks, though it’s best suited for readers with a solid math background. Each of these books brings something unique to the table, catering to different learning styles and expertise levels.

What are the best machine learning books recommended by experts?

4 Answers2025-08-16 17:44:32
I've devoured countless books on the subject, and a few stand out as truly exceptional. 'The Hundred-Page Machine Learning Book' by Andriy Burkov is a gem for its concise yet comprehensive coverage, perfect for both beginners and seasoned practitioners. It distills complex concepts into digestible insights without oversimplifying.

For those craving a deeper dive, 'Pattern Recognition and Machine Learning' by Christopher Bishop is a masterpiece. It balances theory with practical applications, making it a staple for researchers. Meanwhile, 'Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow' by Aurélien Géron is my go-to for coding enthusiasts—it’s packed with real-world projects that solidify understanding through practice. Lastly, 'Deep Learning' by Ian Goodfellow et al. is the bible for neural networks, though it demands some mathematical grit. Each of these books offers a unique lens into ML, catering to different learning styles and goals.

Which good books for machine learning are recommended by experts?

5 Answers2025-08-16 04:54:49
I've come across several books that experts swear by. 'Pattern Recognition and Machine Learning' by Christopher Bishop is a classic that balances theory and practice beautifully. It's a bit dense, but worth every page for the insights it offers.

Another gem is 'Deep Learning' by Ian Goodfellow, Yoshua Bengio, and Aaron Courville. This book is like the bible for deep learning enthusiasts, covering everything from fundamentals to advanced topics. For those who prefer a more hands-on approach, 'Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow' by Aurélien Géron is fantastic. It’s practical, easy to follow, and packed with real-world examples. If you're into the mathematical side, 'The Elements of Statistical Learning' by Trevor Hastie, Robert Tibshirani, and Jerome Friedman is a must-read.

What are the best good books for machine learning beginners?

5 Answers2025-08-16 06:01:11
I remember how overwhelming it could be to pick the right resources. One book that truly stood out for me was 'Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow' by Aurélien Géron. It’s incredibly practical, with tons of code examples that make complex concepts feel approachable. The author breaks down everything from basic algorithms to neural networks in a way that’s engaging and hands-on.

Another gem is 'Python Machine Learning' by Sebastian Raschka and Vahid Mirjalili. It’s perfect for beginners who want a solid foundation in both theory and practice. The explanations are clear, and the book progresses at a pace that doesn’t leave you behind. For those who prefer a more visual approach, 'Deep Learning for Coders with Fastai and PyTorch' by Jeremy Howard and Sylvain Gugger is fantastic. It’s like having a mentor guide you through the process, and the Fastai library simplifies a lot of the heavy lifting. These books made my journey into machine learning far less daunting and a lot more fun.

What book to learn machine learning is recommended by experts?

3 Answers2025-07-21 03:08:45
I'm a tech enthusiast who's dabbled in machine learning, and I can't recommend 'Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow' by Aurélien Géron enough. It's the book I wish I had when I started. The way it breaks down complex concepts into digestible chunks is brilliant. The hands-on approach with real-world examples makes learning feel less like a chore and more like an exciting project. Plus, the updates in the newer editions keep it relevant with the latest advancements in the field. The book covers everything from the basics to deep learning, making it a comprehensive guide for beginners and intermediate learners alike. The practical exercises are golden, helping solidify the theory with actual coding experience. It's a must-have on any aspiring data scientist's shelf.

Who publishes the most popular books machine learning?

2 Answers2025-07-21 23:14:06
When it comes to machine learning books, the big names in publishing are like the Avengers of the knowledge world—each bringing something unique to the table. O'Reilly Media is basically the Tony Stark of tech publishing, with their animal-covered books being instant classics in the ML community. 'Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow' by Aurélien Géron feels like a rite of passage—it’s everywhere, from Reddit threads to bootcamp syllabi. Manning Publications is another heavyweight, offering deep dives with titles like 'Deep Learning with Python' by François Chollet, which reads like a love letter to neural networks.

But let’s not forget the academia-driven giants like Springer, whose textbooks are the backbone of university courses. 'Pattern Recognition and Machine Learning' by Bishop is practically a holy grail for theory enthusiasts. Meanwhile, Packt Publishing floods the market with practical, project-based guides—some hit ('Python Machine Learning' by Raschka), some miss. The rise of self-publishing platforms has also shaken things up, with authors like Andrew Ng releasing bite-sized gems directly to learners. It’s a wild ecosystem where clout isn’t just about sales but shelf space in every aspiring data scientist’s workspace.

Who are the top authors of good books for machine learning?

5 Answers2025-08-16 05:56:00
I've got a few favorites that stand out. Andrew Ng is basically the godfather of ML education—his book 'Machine Learning Yearning' is a must-read for practical insights, and his Coursera course is legendary. Then there's Christopher Bishop with 'Pattern Recognition and Machine Learning,' which is dense but incredibly thorough for theory lovers.

For a more hands-on approach, Aurélien Géron's 'Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow' is my go-to. It’s perfect for coding enthusiasts who want to learn by doing. Ian Goodfellow’s 'Deep Learning' is another heavyweight, especially for those diving into neural networks. And let’s not forget Peter Norvig and Stuart Russell’s 'Artificial Intelligence: A Modern Approach'—it’s a classic that covers ML alongside broader AI topics. These authors have shaped how I understand ML, and their books are dog-eared from constant use.

Which machine learning best book is recommended for beginners?

5 Answers2025-08-16 01:26:46
I remember how overwhelming it was to pick the right book. The one that truly helped me grasp the fundamentals was 'Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow' by Aurélien Géron. It’s incredibly practical, with code examples that make complex concepts accessible. The book balances theory with hands-on projects, which is perfect for beginners who learn by doing.

Another great option is 'Python Machine Learning' by Sebastian Raschka. It’s more technical but explains algorithms in a way that doesn’t feel intimidating. For those who prefer a lighter read, 'Machine Learning for Absolute Beginners' by Oliver Theobald is a gentle introduction without heavy math. Each of these books has its strengths, but Géron’s stands out for its clarity and real-world applications.

What are the top reviews for the best book machine learning?

5 Answers2025-08-16 19:21:23
I’ve come across a few books that stand out for their clarity and depth. 'Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow' by Aurélien Géron is a masterpiece for anyone looking to get their hands dirty with real-world applications. It’s packed with practical examples and explanations that make complex concepts feel approachable. Another favorite is 'Pattern Recognition and Machine Learning' by Christopher Bishop, which is a bit more technical but offers a rigorous foundation for those who want to understand the math behind the algorithms.

For those just starting out, 'Machine Learning Yearning' by Andrew Ng is a fantastic resource. It focuses less on code and more on the strategic thinking needed to build effective ML systems. On the other hand, 'The Hundred-Page Machine Learning Book' by Andriy Burkov lives up to its name by distilling the essentials into a concise yet comprehensive guide. Each of these books has earned rave reviews for their ability to cater to different levels of expertise, making them staples in the ML community.

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