Dive into the world of deep learning with this comprehensive course! Explore core algorithms and applications in NLP, computer vision, biology, and beyond. Build your own neural networks and gain practical skills. Delve into cutting-edge topics like large language models and generative AI. The course culminates in a project competition with valuable feedback from industry experts. Prerequisites include calculus and linear algebra, with Python experience a plus. Let's unlock the power of deep learning together!
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