Data Scientist (n.): Person who is better at statistics than any software engineer and better at software engineering than any statistician.I am Dr Warith Harchaoui and I have been fortunate to practice the Data Scientist job since summer of 2014 at Oscaro.com. Since then, I continue to learn every day, gathering great resources. For beginners, I strongly recommend the following:
Pattern Recognition and Machine Learning
Christopher M. Bishop, 2006
Machine Learning: A Probabilistic Perspective
Kevin Murphy, 2012
Bayesian Reasoning and Machine Learning
David Barber, 2012
Computer vision is an area of artificial intelligence that aims to replicate the capabilities of human vision by teaching computers to interpret and comprehend the visual environment in a similar way to humans. This field is applied to a variety of tasks, such as facial recognition, object detection, autonomous driving, and medical imaging.
Computer Vision: A Modern Approach, 2nd Edition
David Forsyth and Jean Ponce, 2011
Computer Vision: Algorithms and Applications, 2nd Edition
Richard Szeliski, 2022
Multiple View Geometry in Computer Vision
Richard Hartley and Andrew Zisserman, 2004
Natural Language Processing (NLP) allows computers to interpret and comprehend human language. This is achieved through the use of algorithms and software that analyze large amounts of data and extract the meaning of text, enabling computers to understand language in a similar way to humans. NLP is applied in various contexts, including search engine optimization, automatic summarization, sentiment analysis, and natural language generation.
Neural Network Methods in Natural Language Processing
Yoav Goldberg, 2017
Foundations of Statistical Natural Language Processing
Chris Manning and Hinrich Schütze, 1999
Signal processing deals with the manipulation, analysis, and transformation of signals. These signals may include sound waves, radio waves, images, and data from medical instruments. Signal processing is applied in a variety of contexts, including audio enhancement, data analysis in medical imaging, and more. Essentially, signal processing involves extracting information from signals such as frequency, amplitude, or color, or modifying to make it crisper.
A Wavelet Tour of Signal Processing, 3rd Edition
Stéphane Mallat, 2008
Reinforcement Learning (RL) is about making machines learn from their environment and perform actions that maximize rewards. To do this, the computer is given a numerical goal or objective, and then given feedback after each action it performs in the form of punishment and reward, also numerical. The computer then adjusts its actions according to this feedback, learning from its mistakes and optimizing its behavior over time. Reinforcement learning is applied in a variety of fields, including games, robotics and autonomous vehicles. In general terms, reinforcement learning is the process of teaching a computer to perform the most efficient actions in a given environment in order to maximize the rewards
Reinforcement Learning, 2nd Edition
Richard S. Sutton and Andrew G. Barto, 2018
Advances in Financial Machine Learning
Marcos Lopez de Prado, 2018
Algorithms are sets of instructions for solving problems in a systematic way. Optimization is the process of identifying the most efficient way to solve a problem. In essence, algorithms and optimization can be viewed as sister techniques for improving efficiency. Algorithms and optimization are the cornerstones of artificial intelligence to adjust model parameters to fit the data. Mastering algorithms and optimization techniques provides the theoretical, but more importantly practical, tools to create new ones and adapt old ones to meet the specificity of your real-world problems.
Introduction to Algorithms
Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest and Clifford Stein, 2009
Convex Optimization
Stephen Boyd and Lieven Vandenberghe, 2004
Numerical Optimization, 2nd Edition
J. Frédéric Bonnans, J. Charles Gilbert, Claude Lemaréchal and Claudia A. Sagastizábal, 2006
Numerical Recipes, 3rd Edition
William H. Press, Saul A. Teukolsky, William T. Vetterling and Brian P. Flannery, 2007
Computational Optimal Transport
Gabriel Peyré and Marco Cuturi, 2019