This comprehensive course introduces the expansive world of AI before diving deep into its most transformative subset: Deep Learning. We'll explore fundamental concepts, build neural networks, and gain practical skills while examining state-of-the-art topics like large language models and generative AI, all while considering their diverse applications and ethical implications. A foundation in calculus and linear algebra is required, with Python experience being a plus. Join us to unlock the power of AI and deep learning!
Additional Reading Materials (free online)
AI Cheatsheets
- Deep Learning
- Deep Learning Tips & Tricks
- Recurrent Neural Networks
- Convolutional Neural Networks
- Machine Learning
- Machine Learning Tips & Tricks
- Supervised Learning
- Unsupervised Learning
- Probability and Statistics
- Algebra and Calculus
- Artificial Intelligence
- State Models
- Reflex Models
- Logic Models
- Variable Models
- Data Science
- Data Visualization with Python
- Data Retrieval with SQL
- Data Manipulation with Python
Tutorials
Computer Vision
- A Neural Network Playground: https://playground.tensorflow.org/
- Teachable Machine: https://teachablemachine.withgoogle.com/
- DeepDream Generator: https://deepdreamgenerator.com/
- ConvNet.ai: https://cs.stanford.edu/people/karpathy/convnetjs/started.html
- Kaggle Datasets: https://www.kaggle.com/datasets?tags=13207-Computer+Vision
- Papers With Code - Computer Vision: https://github.com/paperswithcode
- Deep Learning Book : https://www.deeplearningbook.org/
- A Gentle Introduction to Deep Learning for Computer Vision : https://arxiv.org/abs/2003.03253
- Vision Transformers
- How the Vision Transformer (ViT) works in 10 minutes
Natural Language Processing
- Stanford NLP Group: https://nlp.stanford.edu/
- Speech and Language Processing: https://stanford.edu/~jurafsky/slp3/
- Natural Language Processing (NLP) Tutorial: https://www.geeksforgeeks.org/natural-language-processing-nlp-tutorial/
- Beginner to Intermediate NLP Tutorial: https://www.kaggle.com/code/ashishpatel26/beginner-to-intermediate-nlp-tutorial
- Natural Language Processing Videos: https://www.youtube.com/playlist?list=PLNvQn5fLVQdhWMqZWOdBFBZpKgPdTVGVF
Generative Adversarial Networks
- Generative Adversarial Networks Tutorial: https://www.datacamp.com/tutorial/generative-adversarial-networks
- GANs: Build Your First Models: https://realpython.com/generative-adversarial-networks/
- Deep Convolutional Generative Adversarial Network: https://www.tensorflow.org/tutorials/generative/dcgan
- A Gentle Introduction to GANs: https://machinelearningmastery.com/what-are-generative-adversarial-networks-gans/
- GAN Lab: https://poloclub.github.io/ganlab/
- GAN Dissection: Visualizing and Understanding GANs: https://gandissect.csail.mit.edu/
Reinforcement Learning
- Practical Reinforcement Learning: https://github.com/yandexdataschool/Practical_RL
- RL for Traffic Signal Control: https://traffic-signal-control.github.io/
- Deep Trading Agent: https://github.com/samre12/deep-trading-agent
- Reinforcement Learning: A Tutorial
Time Series Forecasting
- Time series forecasting: https://www.tensorflow.org/tutorials/structured_data/time_series
- Tutorial: Time Series Analysis and Forecasting: https://www.kaggle.com/code/satishgunjal/tutorial-time-series-analysis-and-forecasting
- Python Time Series Forecasting Tutorial: https://www.influxdata.com/blog/python-time-series-forecasting-tutorial/
- What is time-series forecasting?: https://cloud.google.com/learn/what-is-time-series
General
- DeepLearning.AI Video Tutorials: https://www.youtube.com/c/deeplearningai
- fast.ai Deep Learning from the Foundations: https://www.fast.ai/
- Keras Deep Learning library documentation: https://keras.io/
- TensorFlow Deep Learning library documentation: hhttps://www.tensorflow.org/tutorials
- PyTorch Deep Learning library documentation: https://pytorch.org/
- ChatGPT: https://chatgpt.com/
- Edx Introduction to ChatGPT: https://www.edx.org/learn/chatgpt/edx-introduction-to-chatgpt
- Original Transformer - Attention is all you need