Santiago

Santiago

I write about Machine Learning • Practical tips and epic stories about my experience in the field.

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20+ Book Recommendations by Santiago

  • This Book Might Make Your Realize That You Can Become And Make Something Out Of Nothing Try This Book From Dawson Barnes Who Have Researched Over The 5 Years About Essential Element In The Tech World"Whether or not you know it, odds are that machine learning powers applications that you use every day," says Bill Brock, VP of engineering at Very. "Machine learning has revolutionized countless industries; it's the underlying technology for many apps in your smartphone, from virtual assistants like Siri to predicting traffic patterns with Google Maps."Perhaps you care more about the accuracy of that traffic prediction or the voice assistant's response than what's under the hood - and understandably so. But as machine learning use cases continue to increase, you will find yourself needing to explain at least the basics of the technology to folks outside of IT, whether it's to get buy-in, to showcase the work of your team, or simply to build better communication and understanding between departments. Your understanding of ML could also bolster the long-term results of your artificial intelligence strategy.Grab A Copy Now To Know Machine Learning

    @itsMeSriq If Andrew's course feels too heavy, try "Machine Learning Crash Course" (google it.) It's free. It's more developer-oriented. I don't have recommendations for math books (the Internet has done a good job for me.) If you care about Statistics, the "The Cartoon Guide..." is good.

  • Atomic Habits

    James Clear

    James Clear presents strategies to form good habits, break bad ones, and master the tiny behaviors that help lead to an improved life.

    @plattttttttt @ezerfernandes The chart comes from Atomic Habits, an excellent book. Maybe, just maybe, a good way of trying to improve every day is not precisely to get up in arms against a motivational chart trying to discredit how mathematically realistic it is. I’d recommend you read the book.

  • Smart, hilarious, and engaging, MATH WITH BAD DRAWINGS is a delightful re-education in math that empowers readers with a joyful appreciation and powerful understanding of how math works in our daily lives. In MATH WITH BAD DRAWINGS, Ben Orlin answers math's three big questions: Why do I need to learn this? When am I ever going to use it? Why is it so hard? The answers come in various forms-cartoons, drawings, jokes, and the stories and insights of an empathetic teacher who believes that math should belong to everyone. Eschewing the tired old curriculum that begins in the wading pool of addition and subtraction and progresses to the shark infested waters of calculus (AKA the Great Weed Out Course), Orlin instead shows us how to think like a mathematician by teaching us a new game of Tic-Tac-Toe, how to understand an economic crisis by rolling a pair of dice, and the mathematical reason why you should never buy a second lottery ticket. Every example in the book is illustrated with his trademark "bad drawings," which convey both his humor and his message with perfect pitch and clarity. Organized by unconventional but compelling topics such as "Statistics: The Fine Art of Honest Lying," "Design: The Geometry of Stuff That Works," and "Probability: The Mathematics of Maybe," MATH WITH BAD DRAWINGS is a perfect read for fans of illustrated popular science.

    I started reading "Math with Bad Drawings," and I'm obsessed with it. Such a fresh, different, and intuitive perspective! For those that love machine learning, the book covers Probabilities and Statistics. Highly recommended! https://t.co/ZZski1A0MV https://t.co/IERqg0UCLH

  • Feature engineering is a crucial step in the machine-learning pipeline, yet this topic is rarely examined on its own. With this practical book, you'll learn techniques for extracting and transforming features--the numeric representations of raw data--into formats for machine-learning models. Each chapter guides you through a single data problem, such as how to represent text or image data. Together, these examples illustrate the main principles of feature engineering. Rather than simply teach these principles, authors Alice Zheng and Amanda Casari focus on practical application with exercises throughout the book. The closing chapter brings everything together by tackling a real-world, structured dataset with several feature-engineering techniques. Python packages including numpy, Pandas, Scikit-learn, and Matplotlib are used in code examples. You'll examine: Feature engineering for numeric data: filtering, binning, scaling, log transforms, and power transforms Natural text techniques: bag-of-words, n-grams, and phrase detection Frequency-based filtering and feature scaling for eliminating uninformative features Encoding techniques of categorical variables, including feature hashing and bin-counting Model-based feature engineering with principal component analysis The concept of model stacking, using k-means as a featurization technique Image feature extraction with manual and deep-learning techniques

    Feature Engineering for Machine Learning. https://t.co/gbv58C1wjf The best book I've found to improve your data skills. A practical introduction to the fundamental techniques for extracting and transforming features into a suitable format for machine learning models. https://t.co/2dzR27AUmN

  • Python Crash Course, 2nd Edition is a straightforward introduction to the core of Python programming. Author Eric Matthes dispenses with the sort of tedious, unnecessary information that can get in the way of learning how to program, choosing instead to provide a foundation in general programming concepts, Python fundamentals, and problem solving. Three real world projects in the second part of the book allow readers to apply their knowledge in useful ways. Readers will learn how to create a simple video game, use data visualisation techniques to make graphs and charts, and build and deploy an interactive web application.

    4 books to get started with machine learning: • Naked Statistics https://t.co/WcifFssR8y • Python Crash Course https://t.co/ubCtiiRRGg • The Hundred-Page Machine Learning Book https://t.co/TyKI1EzzzD • Hands-on Machine Learning https://t.co/iSqQPqLzgH https://t.co/mrhozVrXZV

  • 4 books to get started with machine learning: • Naked Statistics https://t.co/WcifFssR8y • Python Crash Course https://t.co/ubCtiiRRGg • The Hundred-Page Machine Learning Book https://t.co/TyKI1EzzzD • Hands-on Machine Learning https://t.co/iSqQPqLzgH https://t.co/mrhozVrXZV

  • Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. The updated edition of this best-selling book uses concrete examples, minimal theory, and two production-ready Python frameworks-Scikit-Learn and TensorFlow 2-to help you gain an intuitive understanding of the concepts and tools for building intelligent systems. Practitioners will learn a range of techniques that they can quickly put to use on the job. Part 1 employs Scikit-Learn to introduce fundamental machine learning tasks, such as simple linear regression. Part 2, which has been significantly updated, employs Keras and TensorFlow 2 to guide the reader through more advanced machine learning methods using deep neural networks. With exercises in each chapter to help you apply what you've learned, all you need is programming experience to get started. NEW FOR THE SECOND EDITION:Updated all code to TensorFlow 2 ; Introduced the high-level Keras API ; New and expanded coverage including TensorFlow's Data API, Eager Execution, Estimators API, deploying on Google Cloud ML, handling time series, embeddings and more With Early Release ebooks, you get books in their earliest form-the author's raw and unedited content as he or she writes-so you can take advantage of these technologies long before the official release of these titles. You'll also receive updates when significant changes are made, new chapters are available, and the final ebook bundle is released.

    4 books to get started with machine learning: • Naked Statistics https://t.co/WcifFssR8y • Python Crash Course https://t.co/ubCtiiRRGg • The Hundred-Page Machine Learning Book https://t.co/TyKI1EzzzD • Hands-on Machine Learning https://t.co/iSqQPqLzgH https://t.co/mrhozVrXZV

  • Naked Statistics

    Charles Wheelan

    4 books to get started with machine learning: • Naked Statistics https://t.co/WcifFssR8y • Python Crash Course https://t.co/ubCtiiRRGg • The Hundred-Page Machine Learning Book https://t.co/TyKI1EzzzD • Hands-on Machine Learning https://t.co/iSqQPqLzgH https://t.co/mrhozVrXZV

  • In this second edition of Automate the Boring Stuff with Python, you'll learn the basics of programming in Python, the fastest growing programming language today, before moving on to create Python programs that effortlessly perform useful and impressive feats of automation. This updated edition is full of step-by-step instructions that walk through each programme. Practice projects at the end of each chapter challenge you to improve those programmes and use your newfound skills to automate similar tasks.

    Best Python 🐍 book you can buy today: "Automate the Boring Stuff with Python." → https://t.co/Wq5cA020h7 If you are starting, this is the book for you. (I asked, and hundreds of you answered. This was the book that rose to the top of the recommendations.) https://t.co/NkzuxMS733

  • Feature engineering is a crucial step in the machine-learning pipeline, yet this topic is rarely examined on its own. With this practical book, you'll learn techniques for extracting and transforming features--the numeric representations of raw data--into formats for machine-learning models. Each chapter guides you through a single data problem, such as how to represent text or image data. Together, these examples illustrate the main principles of feature engineering. Rather than simply teach these principles, authors Alice Zheng and Amanda Casari focus on practical application with exercises throughout the book. The closing chapter brings everything together by tackling a real-world, structured dataset with several feature-engineering techniques. Python packages including numpy, Pandas, Scikit-learn, and Matplotlib are used in code examples. You'll examine: Feature engineering for numeric data: filtering, binning, scaling, log transforms, and power transforms Natural text techniques: bag-of-words, n-grams, and phrase detection Frequency-based filtering and feature scaling for eliminating uninformative features Encoding techniques of categorical variables, including feature hashing and bin-counting Model-based feature engineering with principal component analysis The concept of model stacking, using k-means as a featurization technique Image feature extraction with manual and deep-learning techniques

    If you prefer books, check out "Feature Engineering for Machine Learning." https://t.co/gbv58C1wjf It's a practical introduction to the fundamental techniques for extracting and transforming features into a suitable format for machine learning models. ↓ 4/7 https://t.co/d0MXDZtmSE

  • Effective Python

    Brett Slatkin

    Updated and Expanded for Python 3 It's easy to start developing programs with Python, which is why the language is so popular. However, Python's unique strengths, charms, and expressiveness can be hard to grasp, and there are hidden pitfalls that can easily trip you up. This second edition of Effective Python will help you master a truly "Pythonic" approach to programming, harnessing Python's full power to write exceptionally robust and well-performing code. Using the concise, scenario-driven style pioneered in Scott Meyers' best-selling Effective C++, Brett Slatkin brings together 90 Python best practices, tips, and shortcuts, and explains them with realistic code examples so that you can embrace Python with confidence. Drawing on years of experience building Python infrastructure at Google, Slatkin uncovers little-known quirks and idioms that powerfully impact code behavior and performance. You'll understand the best way to accomplish key tasks so you can write code that's easier to understand, maintain, and improve. In addition to even more advice, this new edition substantially revises all items from the first edition to reflect how best practices have evolved. Key features include 30 new actionable guidelines for all major areas of Python Detailed explanations and examples of statements, expressions, and built-in types Best practices for writing functions that clarify intention, promote reuse, and avoid bugs Better techniques and idioms for using comprehensions and generator functions Coverage of how to accurately express behaviors with classes and interfaces Guidance on how to avoid pitfalls with metaclasses and dynamic attributes More efficient and clear approaches to concurrency and parallelism Solutions for optimizing and hardening to maximize performance and quality Techniques and built-in modules that aid in debugging and testing Tools and best practices for collaborative development Effective Python will prepare growing programmers to make a big impact using Python.

    A book that will significantly help with your Python 🐍 skills: • "Effective Python. 90 specific ways to write better Python." from Brett Slatkin @haxor. → https://t.co/hgrrIFLDeB Make sure you buy the second edition. ↓ 1/3 https://t.co/gvRvWDDtob

  • An introduction to a broad range of topics in deep learning, covering mathematical and conceptual background, deep learning techniques used in industry, and research perspectives. “Written by three experts in the field, Deep Learning is the only comprehensive book on the subject.” —Elon Musk, cochair of OpenAI; cofounder and CEO of Tesla and SpaceX Deep learning is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts. Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning. The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models. Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

    Here are 4 of my favorite books. Together they provide a pretty comprehensive view into the world of deep learning. • https://t.co/Oal8LV8OJ9 • https://t.co/92n2SKA1wP • https://t.co/mNeR5GEzqs • https://t.co/WMm2ixlL2n It doesn't matter where you are; these will help. https://t.co/QAANA1qRW6

  • Summary Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher Fran�ois Chollet, this book builds your understanding through intuitive explanations and practical examples. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the Technology Machine learning has made remarkable progress in recent years. We went from near-unusable speech and image recognition, to near-human accuracy. We went from machines that couldn't beat a serious Go player, to defeating a world champion. Behind this progress is deep learning--a combination of engineering advances, best practices, and theory that enables a wealth of previously impossible smart applications. About the Book Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by Keras creator and Google AI researcher Fran�ois Chollet, this book builds your understanding through intuitive explanations and practical examples. You'll explore challenging concepts and practice with applications in computer vision, natural-language processing, and generative models. By the time you finish, you'll have the knowledge and hands-on skills to apply deep learning in your own projects. What's Inside Deep learning from first principles Setting up your own deep-learning environment Image-classification models Deep learning for text and sequences Neural style transfer, text generation, and image generation About the Reader Readers need intermediate Python skills. No previous experience with Keras, TensorFlow, or machine learning is required. About the Author Fran�ois Chollet works on deep learning at Google in Mountain View, CA. He is the creator of the Keras deep-learning library, as well as a contributor to the TensorFlow machine-learning framework. He also does deep-learning research, with a focus on computer vision and the application of machine learning to formal reasoning. His papers have been published at major conferences in the field, including the Conference on Computer Vision and Pattern Recognition (CVPR), the Conference and Workshop on Neural Information Processing Systems (NIPS), the International Conference on Learning Representations (ICLR), and others. Table of Contents PART 1 - FUNDAMENTALS OF DEEP LEARNING What is deep learning? Before we begin: the mathematical building blocks of neural networks Getting started with neural networks Fundamentals of machine learning PART 2 - DEEP LEARNING IN PRACTICE Deep learning for computer vision Deep learning for text and sequences Advanced deep-learning best practices Generative deep learning Conclusions appendix A - Installing Keras and its dependencies on Ubuntu appendix B - Running Jupyter notebooks on an EC2 GPU instance

    Here are 4 of my favorite books. Together they provide a pretty comprehensive view into the world of deep learning. • https://t.co/Oal8LV8OJ9 • https://t.co/92n2SKA1wP • https://t.co/mNeR5GEzqs • https://t.co/WMm2ixlL2n It doesn't matter where you are; these will help. https://t.co/QAANA1qRW6

  • Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. The updated edition of this best-selling book uses concrete examples, minimal theory, and two production-ready Python frameworks-Scikit-Learn and TensorFlow 2-to help you gain an intuitive understanding of the concepts and tools for building intelligent systems. Practitioners will learn a range of techniques that they can quickly put to use on the job. Part 1 employs Scikit-Learn to introduce fundamental machine learning tasks, such as simple linear regression. Part 2, which has been significantly updated, employs Keras and TensorFlow 2 to guide the reader through more advanced machine learning methods using deep neural networks. With exercises in each chapter to help you apply what you've learned, all you need is programming experience to get started. NEW FOR THE SECOND EDITION:Updated all code to TensorFlow 2 ; Introduced the high-level Keras API ; New and expanded coverage including TensorFlow's Data API, Eager Execution, Estimators API, deploying on Google Cloud ML, handling time series, embeddings and more With Early Release ebooks, you get books in their earliest form-the author's raw and unedited content as he or she writes-so you can take advantage of these technologies long before the official release of these titles. You'll also receive updates when significant changes are made, new chapters are available, and the final ebook bundle is released.

    Here are 4 of my favorite books. Together they provide a pretty comprehensive view into the world of deep learning. • https://t.co/Oal8LV8OJ9 • https://t.co/92n2SKA1wP • https://t.co/mNeR5GEzqs • https://t.co/WMm2ixlL2n It doesn't matter where you are; these will help. https://t.co/QAANA1qRW6

  • It’s the revolutionary math study guide just for middle school students from the brains behind Brain Quest. Everything You Need to Ace Math . . . covers everything to get a student over any math hump: fractions, decimals, and how to multiply and divide them; ratios, proportions, and percentages; geometry; statistics and probability; expressions and equations; and the coordinate plane and functions. The BIG FAT NOTEBOOK™ series is built on a simple and irresistible conceit—borrowing the notes from the smartest kid in class. There are five books in all, and each is the only book you need for each main subject taught in middle school: Math, Science, American History, English Language Arts, and World History. Inside the reader will find every subject’s key concepts, easily digested and summarized: Critical ideas highlighted in neon colors. Definitions explained. Doodles that illuminate tricky concepts in marker. Mnemonics for memorable shortcuts. And quizzes to recap it all. The BIG FAT NOTEBOOKS meet Common Core State Standards, Next Generation Science Standards, and state history standards, and are vetted by National and State Teacher of the Year Award–winning teachers. They make learning fun and are the perfect next step for every kid who grew up on Brain Quest.

    You can start your machine learning career with mostly high school math. Look at the pictures. This is a middle-school math book that I have at home, and it contains enough math to get you going for a while. https://t.co/VpJ8oPPXZl

  • Python Tricks

    Dan Bader

    "I don't even feel like I've scratched the surface of what I can do with Python" With Python Tricks: The Book you'll discover Python's best practices and the power of beautiful & Pythonic code with simple examples and a step-by-step narrative. You'll get one step closer to mastering Python, so you can write beautiful and idiomatic code that comes to you naturally. Learning the ins and outs of Python is difficult-and with this book you'll be able to focus on the practical skills that really matter. Discover the "hidden gold" in Python's standard library and start writing clean and Pythonic code today. Who Should Read This Book: If you're wondering which lesser known parts in Python you should know about, you'll get a roadmap with this book. Discover cool (yet practical!) Python tricks and blow your coworkers' minds in your next code review. If you've got experience with legacy versions of Python, the book will get you up to speed with modern patterns and features introduced in Python 3 and backported to Python 2. If you've worked with other programming languages and you want to get up to speed with Python, you'll pick up the idioms and practical tips you need to become a confident and effective Pythonista. If you want to make Python your own and learn how to write clean and Pythonic code, you'll discover best practices and little-known tricks to round out your knowledge. What Python Developers Say About The Book: "I kept thinking that I wished I had access to a book like this when I started learning Python many years ago." - Mariatta Wijaya, Python Core Developer "This book makes you write better Python code!" - Bob Belderbos, Software Developer at Oracle "Far from being just a shallow collection of snippets, this book will leave the attentive reader with a deeper understanding of the inner workings of Python as well as an appreciation for its beauty." - Ben Felder, Pythonista "It's like having a seasoned tutor explaining, well, tricks!" - Daniel Meyer, Sr. Desktop Administrator at Tesla Inc.

    Both of these are great books to open from time to time and read an individual section. They give you bite-sized tips and advice that you can incorporate immediately into your work. Replace 30 minutes of Netflix every week with some reading. https://t.co/zCahTfT7dy

  • Effective Python

    Brett Slatkin

    Updated and Expanded for Python 3 It's easy to start developing programs with Python, which is why the language is so popular. However, Python's unique strengths, charms, and expressiveness can be hard to grasp, and there are hidden pitfalls that can easily trip you up. This second edition of Effective Python will help you master a truly "Pythonic" approach to programming, harnessing Python's full power to write exceptionally robust and well-performing code. Using the concise, scenario-driven style pioneered in Scott Meyers' best-selling Effective C++, Brett Slatkin brings together 90 Python best practices, tips, and shortcuts, and explains them with realistic code examples so that you can embrace Python with confidence. Drawing on years of experience building Python infrastructure at Google, Slatkin uncovers little-known quirks and idioms that powerfully impact code behavior and performance. You'll understand the best way to accomplish key tasks so you can write code that's easier to understand, maintain, and improve. In addition to even more advice, this new edition substantially revises all items from the first edition to reflect how best practices have evolved. Key features include 30 new actionable guidelines for all major areas of Python Detailed explanations and examples of statements, expressions, and built-in types Best practices for writing functions that clarify intention, promote reuse, and avoid bugs Better techniques and idioms for using comprehensions and generator functions Coverage of how to accurately express behaviors with classes and interfaces Guidance on how to avoid pitfalls with metaclasses and dynamic attributes More efficient and clear approaches to concurrency and parallelism Solutions for optimizing and hardening to maximize performance and quality Techniques and built-in modules that aid in debugging and testing Tools and best practices for collaborative development Effective Python will prepare growing programmers to make a big impact using Python.

    Both of these are great books to open from time to time and read an individual section. They give you bite-sized tips and advice that you can incorporate immediately into your work. Replace 30 minutes of Netflix every week with some reading. https://t.co/zCahTfT7dy

  • @RealParsaa They are very different books. This one is about algorithms and data structures. Fundamentals of Computer Science. The other one is focused on Software Engineering principles. They are both good books and touch on really important topics.

  • What others in the trenches say about The Pragmatic Programmer... “The cool thing about this book is that it’s great for keeping the programming process fresh. The book helps you to continue to grow and clearly comes from people who have been there.” —Kent Beck, author of Extreme Programming Explained: Embrace Change “I found this book to be a great mix of solid advice and wonderful analogies!” —Martin Fowler, author of Refactoring and UML Distilled “I would buy a copy, read it twice, then tell all my colleagues to run out and grab a copy. This is a book I would never loan because I would worry about it being lost.” —Kevin Ruland, Management Science, MSG-Logistics “The wisdom and practical experience of the authors is obvious. The topics presented are relevant and useful.... By far its greatest strength for me has been the outstanding analogies—tracer bullets, broken windows, and the fabulous helicopter-based explanation of the need for orthogonality, especially in a crisis situation. I have little doubt that this book will eventually become an excellent source of useful information for journeymen programmers and expert mentors alike.” —John Lakos, author of Large-Scale C++ Software Design “This is the sort of book I will buy a dozen copies of when it comes out so I can give it to my clients.” —Eric Vought, Software Engineer “Most modern books on software development fail to cover the basics of what makes a great software developer, instead spending their time on syntax or technology where in reality the greatest leverage possible for any software team is in having talented developers who really know their craft well. An excellent book.” —Pete McBreen, Independent Consultant “Since reading this book, I have implemented many of the practical suggestions and tips it contains. Across the board, they have saved my company time and money while helping me get my job done quicker! This should be a desktop reference for everyone who works with code for a living.” —Jared Richardson, Senior Software Developer, iRenaissance, Inc. “I would like to see this issued to every new employee at my company....” —Chris Cleeland, Senior Software Engineer, Object Computing, Inc. “If I’m putting together a project, it’s the authors of this book that I want. . . . And failing that I’d settle for people who’ve read their book.” —Ward Cunningham Straight from the programming trenches, The Pragmatic Programmer cuts through the increasing specialization and technicalities of modern software development to examine the core process--taking a requirement and producing working, maintainable code that delights its users. It covers topics ranging from personal responsibility and career development to architectural techniques for keeping your code flexible and easy to adapt and reuse. Read this book, and you'll learn how to Fight software rot; Avoid the trap of duplicating knowledge; Write flexible, dynamic, and adaptable code; Avoid programming by coincidence; Bullet-proof your code with contracts, assertions, and exceptions; Capture real requirements; Test ruthlessly and effectively; Delight your users; Build teams of pragmatic programmers; and Make your developments more precise with automation. Written as a series of self-contained sections and filled with entertaining anecdotes, thoughtful examples, and interesting analogies, The Pragmatic Programmer illustrates the best practices and major pitfalls of many different aspects of software development. Whether you're a new coder, an experienced programmer, or a manager responsible for software projects, use these lessons daily, and you'll quickly see improvements in personal productivity, accuracy, and job satisfaction. You'll learn skills and develop habits and attitudes that form the foundation for long-term success in your career. You'll become a Pragmatic Programmer.

    @RealParsaa They are very different books. This one is about algorithms and data structures. Fundamentals of Computer Science. The other one is focused on Software Engineering principles. They are both good books and touch on really important topics.

  • If you are looking for a book covering these, look no further: 💰 https://t.co/g7kSUxt7Aw This one has it all. 8/8 https://t.co/36a2dEK0np

  • Zero to Sold

    Arvid Kahl

    @arvidkahl @SimonHoiberg I've been meaning to tell you that I read your book and I loved it, but for some reason, I always forget. Thanks for writing it, Arvid! Since I've been following you for some time, I knew a little bit about you. 1/

  • This book covers both classical and modern models in deep learning. The chapters of this book span three categories: The basics of neural networks: Many traditional machine learning models can be understood as special cases of neural networks. An emphasis is placed in the first two chapters on understanding the relationship between traditional machine learning and neural networks. Support vector machines, linear/logistic regression, singular value decomposition, matrix factorization, and recommender systems are shown to be special cases of neural networks. These methods are studied together with recent feature engineering methods like word2vec. Fundamentals of neural networks: A detailed discussion of training and regularization is provided in Chapters 3 and 4. Chapters 5 and 6 present radial-basis function (RBF) networks and restricted Boltzmann machines. Advanced topics in neural networks: Chapters 7 and 8 discuss recurrent neural networks and convolutional neural networks. Several advanced topics like deep reinforcement learning, neural Turing machines, Kohonen self-organizing maps, and generative adversarial networks are introduced in Chapters 9 and 10. The book is written for graduate students, researchers, and practitioners. Numerous exercises are available along with a solution manual to aid in classroom teaching. Where possible, an application-centric view is highlighted in order to provide an understanding of the practical uses of each class of techniques.

    Here is a great book to learn the core concepts of neural networks and deep learning: "Neural Networks and Deep Learning" from @michael_nielsen. It's online, 100% free, and an excellent way to build a solid foundation on the fundamentals. https://t.co/evRzQuLrLg https://t.co/cPm4rRC7LC