if I want to make generative AI platform, what knowledges need to me? book, youtube, lecture anything recommends.

Creating a generative AI platform requires a combination of knowledge in machine learning, software development, and domain-specific expertise depending on the type of content you want to generate (e.g., images, text, music, etc.). Here’s a general roadmap of the knowledge areas and some resources to get you started:

1. Foundational Knowledge:
   • Linear Algebra, Probability, and Statistics: Crucial for understanding the underlying math of most algorithms.
     • Book: “Linear Algebra and Its Applications” by Gilbert Strang
     • Book: “Pattern Recognition and Machine Learning” by Christopher Bishop
   • Programming: Proficiency in a language like Python is essential.
     • Online: Codecademy’s Python Course, LeetCode for practice
2. Machine Learning Basics:
   • Book: “Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow” by Aurélien Géron
   • Online Course: Coursera’s “Machine Learning” by Andrew Ng
3. Deep Learning and Generative Models:
   • Book: “Deep Learning” by Goodfellow, Bengio, and Courville
   • Online Course: Coursera’s “Deep Learning Specialization” by Andrew Ng
   • Topics to focus on:
     • Convolutional Neural Networks (CNNs) for image data.
     • Recurrent Neural Networks (RNNs), especially Long Short-Term Memory (LSTM) networks for sequences like text or time series.
     • Generative Adversarial Networks (GANs) for generating new data samples.
     • Variational Autoencoders (VAEs) for generative tasks.
4. Generative AI Specializations:
   • For text:
     • Dive into models like GPT-2, GPT-3, or BERT. OpenAI and HuggingFace have great resources and APIs for these.
   • For images:
     • Explore DCGANs, StyleGAN, and Pix2Pix.
   • For music:
     • Look into WaveGAN or OpenAI’s MuseNet.
5. Software Development & Platform Creation:
   • Frameworks: TensorFlow, PyTorch, Keras are the popular choices for deep learning.
   • Deployment: Learn about tools like Docker, Flask/Django for web-based platforms, and cloud platforms like AWS, GCP, or Azure for scalability.
   • Book: “Flask Web Development” by Miguel Grinberg for web-based applications.
6. Continuous Learning:
   • Follow AI researchers and organizations on platforms like Twitter or LinkedIn.
   • Read arXiv for the latest research papers.
   • Attend conferences, either in-person or virtually, such as NeurIPS, ICML, ICLR.
7. YouTube Channels:
   • Two Minute Papers: Great for keeping up with the latest research.
   • Sentdex: Provides tutorials on a wide range of programming and machine learning topics.
   • Yannic Kilcher: Deep dives into AI research papers.
8. Practice:
   • Work on projects and put your knowledge to the test.
   • Participate in Kaggle competitions to apply what you’ve learned and learn from kernels shared by others.

Lastly, remember that the field of AI and machine learning is vast and constantly evolving. It’s essential to stay curious and continue learning.