Deep Learning In-Depth
I’m interested in deep learning, and I want to know how it really works. I'm not afraid of backpropagation or coding, and I have a strong STEM background. I want a deep-dive into every area where deep learning is applied, like NLP, computer vision, reinforcement learning, and recommender systems. I want a detailed understanding of modern deep learning libraries like Tensorflow 2 and PyTorch.
Deep Learning Applied
I’m interested in deep learning, but math is not for me and I want to write as little code as possible. I want to apply deep learning as quickly as possible to applications like NLP, computer vision, reinforcement learning, GANs, and recommender systems.
Data Science and Machine Learning for Business Applications
I’m interested in machine learning and data science for business applications, like recommender systems, online advertising, and online marketing. I want to maximize click-through rates and conversion rates.
Reinforcement Learning and Control
I want to learn reinforcement learning and control. I want to build agents to solve mazes and play video games like Atari and Super Mario. I'm interested in robotics.
I want to learn computer vision and teach machines to understand what they see in the world. I want to build applications for image classification, facial recognition, object detection, image segmentation, super-resolution, image generation, and style transfer. I'm interested in how machines can generate art, and I'm curious about technologies like GANs, diffusion models, and DALL-E 2.
NLP (Natural Language Processing)
I want to learn NLP (natural language processing), sequence models, and transformers. I want to build applications for text classification, spam detection, sentiment analysis, topic modeling, article spinning, document clustering, information retrieval, latent semantic indexing, text generation, language translation, text summarization, and question-answering. I'm curious about technologies like BERT, GPT-3, and ChatGPT.
Bayesian Machine Learning
I want to know all about Bayesian machine learning and Bayesian statistics. Bayesian is better.
Time Series and Financial Analysis
I’m interested in applying machine learning and artificial intelligence to time series analysis, financial analysis, and algorithmic trading.
Machine Learning Fundamentals
I want to learn all the algorithms from a “typical” machine learning course, like k-means clustering, naive bayes, PCA, kernel methods, decision trees, and boosting.
Computer Science and STEM Fundamentals
I don't meet the math and programming prerequisites for STEM subjects. I need to learn calculus, linear algebra, probability, Python, and other fundamentals.
I just want to learn it all!