C++ is one of the most popular programming languages in the world, and for good reason. It was created in 1979 as an upgrade to C, so it offers many of the advantages associated with its predecessor — providing control over hardware components and system resources — and makes it easier to build stable, efficient software and applications.

But it can be a lot to learn, and while interactive courses can give you the hands-on experience you need to get a grasp of the language, books can provide a double-click into sub-topics and how they relate to each other. “If you want to learn about something more in-depth, find a book that talks about it,” says Jiwon Shin, Senior Curriculum Developer at Codecademy. “Books tend to be more dense, but more dense means more details.”

Plus, supplementing your learning with books is a great way to reinforce the material. “Multimodal learning — learning through different mediums — is one of the best ways to learn new skills,” says Codecademy Curriculum Developer Lucas White. “You might think you’re a visual, hands-on, or auditory learner; but the reality is, the more ways in which you digest information, the more likely it is to stick.”

So to help you take your C++ skills to the next level, here are a few books to read to help supplement your coursework.

If you’re completely new to programming

If you’re starting from scratch, Hisham Touma, Content Contributor at Codecademy, suggests picking up C++ Primer. “It’s a great introduction to the language,” he says. “It assumes no knowledge of C++ or any other programming language, so it thoroughly covers the basics.”

C++ Primer offers a unique perspective from Computer Scientist Stanley B. Lippman — who contributed to C++’s development — and real-life examples of different programming styles and design methods.

If you’ve already mastered the basics

If you already have a language or two under your belt, check out The C++ Programming Language — written by none other than the language’s creator, Bjarne Stroustrup. You’ll learn about C++ concepts and functionalities as you explore upgrades to the language over time and the new features released with each version.

This book offers a comprehensive overview of C++, and according to Codecademy Software Engineer Mariel Frank, it was used as a reference in the creation of our Learn C++ course.

If you want more hands-on practice

Codecademy Director of Engineering Akash Mohapatra recommends Object-Oriented Programming with C++, noting that it’s a popular resource in Indian colleges.

You’ll learn about the object-oriented programming paradigm, its fundamental concepts, and data structure and algorithms as you complete the projects included in every chapter — and the most recent version also includes updates on the latest standards and best practices.

If you want to write better code

Hisham recommends reading Effective C++ if you want to learn more about best practices. “It presents everything in short paragraphs that explain how to write efficient, less error-prone, and bug-free code,” he says.

Effective C++ is better suited for those who already have some programming skills, and later versions of the book are designed to help you transition to C++ from other C-based languages like Java. You’ll also learn how to apply C++ styles and principles in other languages, and the lessons are structured to help you build your practical skills along with your conceptual knowledge.

If you’re ready to go pro

Finally, if you’re gearing up to start applying for jobs, Hisham recommends Professional C++. “It covers industry-standard practices in C++ and how to best write clean, efficient, and debuggable code,” he says.

Professional C++ is designed to help you get the most out of the language. You’ll explore its full functionality, along with niche features and real-world use cases that illustrate how you can start applying your new skills.

Learn more about C++

Want more C++? Check out the courses and content below:

C++ Courses & Tutorials | Codecademy

C++ is a very popular language for performance-critical applications that rely on speed and efficient memory management. It’s used in a wide range of industries including software and game development, VR, robotics, and scientific computing.

You know the unsettling feeling you get when an ad for a super-specific item you searched for maybe once just mysteriously appears on your Instagram feed? You might ask yourself, How did they know?! Well, the answer is recommender systems, algorithms that use data about products and users’ preferences to make recommendations for the best options to choose.

Recommender systems make up a uniquely challenging (but exciting!) area of artificial intelligence. Whereas a predictive machine learning algorithm is designed to come up with one correct answer, recommender system algorithms are preference-based. As a developer, you need to carefully consider human emotion, behavior, ethics, and logic when building a recommender system.

The thing is, recommender systems are not meant to freak out users — quite the opposite, actually. Recommender systems use a handful of techniques to capture users’ interests and tastes, and help them make decisions about everything from what to buy to who to date.

In our new path Build a Recommender System you’ll learn how to use Python and machine learning to create a recommender system. You’ll also get to know the differences between various recommender system techniques, and will understand how to measure the success of a recommender system. Read on to learn more about the elements that make a recommender system good — and sometimes too good.

Factors that make a “good” recommender system

A recommender system performs well if it strikes the right balance between randomness and specificity — if a recommender system suggests something that’s too specific, it can feel off-putting to a user. When you’re building a recommender system or imagining how you might implement one, you have to keep these  factors in mind.

Relevance: This might sound obvious, but it’s important that recommender systems make recommendations that are relevant to the user, meaning it’s very likely that a user will like what’s put in front of them. For example, a dog owner who only buys dog food or browses dog toys on an online pet retailer shouldn’t get recommendations for, say, bird seed or fish tanks.  

Novelty: On the other hand, recommender systems should present recommendations that a user hasn’t seen before so they can discover more items that are relevant to their interests. Going back to the dog owner example, if they’re only getting recommendations for the most purchased or highest-rated dog foods, the user might not be satisfied with their experience shopping with the retailer.

Serendipity: People are typically delighted when recommender systems make recommendations that are unexpected but relevant. The dog owner in our scenario might get a recommendation for grooming tools or dog clothing, items that they haven’t shopped for, but would presumably like given their interests and previous shopping activity.

Diversity: Having a diverse array of items to offer users increases the chances that the user will like at least one of them. Back to the pet retailer: If a pet owner always buys one brand of dog food from a site, but constantly gets recommended more dog kibble, that could be annoying or redundant. A recommender system that prompts other items — in this case, dog supplies like bowls and beds — could motivate a dog owner to add more things to their cart.

Technical Complexity: Given how ubiquitous recommender systems are, organizations need engineers who can understand and interpret the complex algorithms and maintain the parts as needed. You can learn the technical skills that go into creating and maintaining a recommender system with our path Build a Recommender System. If you’re new to coding, don’t sweat it — we’ll teach you Python and machine learning basics in this Codecademy path.

Why recommender systems can seem almost too good

Even though recommender systems are all around us, their effectiveness or accuracy can occasionally catch users off-guard. As a user, you might not realize how much data is being captured when you’re online shopping or using an app.

The way that developers can measure a user’s preferences is by examining how they rate items. Sometimes ratings are very explicit, like giving a book 5 stars on your Goodreads app. But even subtle behaviors — like how many times we view a specific pair of shoes or how long we spend on a page — can be interpreted as an implicit endorsement of the item.

The challenging thing is that recommender systems aren’t an exact science, because our preferences, interests, and internet behaviors may change over time, Nitya says. There’s a “mutually reinforcing feedback loop” between a user and the machine that creates a constant puzzle for developers to tackle, she says.

In the path Build a Recommender System, we’ll demystify the different types of data that’s used in a recommender system, and teach you to create a mathematical model that determines a user’s preferences.

The bottom line

“There’s no such thing as a ‘good’ algorithm or a ‘bad’ algorithm ultimately,” Nitya says. “A lot of it has to do with how cleverly it is applied and how much thought is there behind the design of it.” In other words, while machines can do their best to figure out what someone wants, it’s up to humans to make a decision and contextualize their options.

Of course, shopping for dog food is a relatively trivial example of recommender systems in our daily lives, but it’s important to note that there can be a “dark side” of recommender systems. Recommender systems are both sociological and technical puzzles, according to Nitya. In some cases, recommender systems that constantly “reward” users for certain problematic behaviors can exacerbate polarization, addictive behavior (like incessant scrolling), alienation, and powerlessness, she explains.

Throughout the process of building a recommender system, Nitya suggests keeping these questions in mind: What user behavior is being rewarded here, and is it enhancing or diminishing the user’s quality of life? Are there consequences of scaling this recommender system that might cause harm down the line? In our courses, you’ll learn how to take these questions into consideration when building recommender systems, and be able to identify a “successful” one.

Ready to learn more about this fascinating machine learning application? Check out the beginner-friendly path Build a Recommender System to learn how to use the programming language Python to create a recommender system from scratch. If you already have experience with Python and Pandas, you might want to start with the free intermediate course Learn Recommender Systems.  
Be sure to check out Codecademy’s full catalog of machine learning and data science courses to learn more about the technology that you use every day.

Machine Learning Courses & Tutorials | Codecademy

Machine Learning is an increasingly hot field of data science dedicated to enabling computers to learn from data. From spam filtering in social networks to computer vision for self-driving cars, the potential applications of Machine Learning are vast.