Calculus, Early Transcendentals
Appropriate for the traditional 2 or 3-term college calculus course, this textbook presents the fundamentals of calculus. Topics include, but are not limited to: a review of polynomials, trigonometric, exponential, and logarithmic functions, followed by discussions of limits, derivatives, applications of differential calculus to real-world problem areas, an overview of integration, basic techniques for integration, a variety of applications of integration, and an introduction to (systems of) differential equations.
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- I have been a Java Developer then full stack developer all my career. I want to get started on AI. I am not sure where to start as it is overwhelming to look at the rapid changes coming in. Are there a list of books i can start with? My goal is to be not outdated in 6 months and lose my job to chatGPT or some other version of it. So i asked ChatGPT to give me a detailed plan and here is what it gave me. Could you please let me know if this is a way to go or if there is a better way
Transitioning from a full-stack engineer to the AI field can be a fulfilling and exciting journey. To make the most of this transition, you should focus on building a strong foundation in mathematics, programming, and machine learning concepts. Here's a detailed learning plan, broken down into four stages:
Stage 1: Building a Strong Foundation
Mathematics:
Linear Algebra: "Linear Algebra and Its Applications" by Gilbert Strang
Calculus: "Calculus: Early Transcendentals" by James Stewart
Probability & Statistics: "Probability and Statistics for Engineers and Scientists" by Ronald E. Walpole, Raymond H. Myers, Sharon L. Myers, and Keying E. Ye
Optimization: "Convex Optimization" by Stephen Boyd and Lieven Vandenberghe
Programming:
Python: "Python Crash Course" by Eric Matthes
Git: "Pro Git" by Scott Chacon and Ben Straub
Machine Learning & Data Science Basics:
"Introduction to Data Science" by Laura Igual and Santi Seguí "Python Data Science Handbook" by Jake VanderPlas
Stage 2: Learning Machine Learning and Deep Learning
Online Courses:
Coursera: "Machine Learning" by Andrew Ng
Coursera: "Deep Learning Specialization" by Andrew Ng
Fast.ai: "Practical Deep Learning for Coders"
Books:
"Pattern Recognition and Machine Learning" by Christopher M. Bishop "Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
Stage 3: Expanding Knowledge of AI Subfields
Natural Language Processing (NLP):
"Speech and Language Processing" by Daniel Jurafsky and James H. Martin
"Natural Language Processing with Python" by Steven Bird, Ewan Klein, and Edward Loper
Computer Vision:
"Computer Vision: Algorithms and Applications" by Richard Szeliski
"Deep Learning for Computer Vision" by Adrian Rosebrock Reinforcement Learning:
"Reinforcement Learning: An Introduction" by Richard S. Sutton and Andrew G. Barto
"Deep Reinforcement Learning Hands-On" by Maxim Lapan
Stage 4: Staying Current and Gaining Practical Experience
Read research papers:
Subscribe to the arXiv mailing list in your areas of interest Regularly read papers from conferences like NeurIPS, ICML, and ACL Participate in online competitions:
Kaggle: Participate in machine learning competitions to improve your skills and build your portfolio AIcrowd: Another platform for AI competitions Contribute to open-source projects:
TensorFlow, PyTorch, or other popular AI frameworks Look for projects on GitHub related to your interests and contribute by fixing bugs, implementing new features, or improving documentation Network with AI professionals:
Attend AI conferences, workshops, and local meetups Join AI-related forums, LinkedIn groups, or online communities like Reddit's r/MachineLearning Remember that the learning plan can be adjusted based on your pace and interests. The key is to stay consistent and always be curious to learn more. Good luck on your journey into the AI field!
- For calculus, I found Thomas' Calculus (https://www.amazon.com/Thomas-Calculus-Early-Transcendentals...) the most helpful. It's a textbook but has a lot of exercises.
- In that case, I recommend starting out with Zill's Precalculus with Calculus Previews and then working through Stewart's Calculus: Early Transcendentals!
- I haven't finished it but I read most of the late James Stewart's "Calculus: Early Transcendentals" and found it very instructive. It would make a good supplement for anyone learning Calculus.
- Of the math textbooks I've used, two've been good enough to keep so far:
Linear Algebra: http://www.amazon.ca/Linear-Algebra-Applications-CD-ROM-Upda...
Calculus: http://www.amazon.ca/Calculus-Early-Transcendentals-Tools-En...
on the more computer-sciency side, Introduction to Algorithms has a lot of good word-of-mouth. I'm not such a fan, myself, but here it is: http://www.amazon.ca/Introduction-Algorithms-Thomas-H-Cormen...
- I’m currently doing the Calculus I course and while there are explanations interspersed throughout the problems, these mostly seem to be the bare minimum you need to work the problems. When I compare it to the calculus textbook I keep alongside it (Stewart’s “Calculus Early Transcendentals”) it barely seems enough.
