List of topics you should cover to get started:
1. Machine Learning Basics:
- Understand supervised learning, unsupervised learning, and reinforcement learning.
- Learn about training data, validation data, and testing data.
- Get familiar with common machine learning algorithms like linear regression, logistic regression, decision trees, k-nearest neighbors, support vector machines, etc.
2. Deep Learning:
- Learn about neural networks, including their architecture, layers, and activation functions.
- Understand how to train neural networks using techniques like backpropagation and gradient descent.
- Dive into popular deep learning frameworks like TensorFlow and PyTorch.
3. Natural Language Processing (NLP):
- Understand the basics of text preprocessing, tokenization, and vectorization.
- Learn about word embeddings (e.g., Word2Vec, GloVe) and their applications.
- Explore sequence models like recurrent neural networks (RNNs) and long short-term memory networks (LSTMs) for NLP tasks.
- Discover transformers and attention mechanisms, which are pivotal in modern NLP models like BERT, GPT, etc.
4. Computer Vision:
- Learn about image preprocessing techniques.
- Understand convolutional neural networks (CNNs) and their applications in tasks like image classification, object detection, and image segmentation.
5. Reinforcement Learning:
- Understand the basics of reinforcement learning, including Markov decision processes, rewards, and policies.
- Learn about Q-learning, deep Q-networks (DQN), policy gradients, and actor-critic methods.
6. Ethical Considerations and Bias in AI:
- Explore ethical considerations in AI, such as fairness, accountability, transparency, and privacy.
- Understand the importance of addressing bias in AI models and data.
7. Deployment and Productionization:
- Learn how to deploy machine learning models into production environments.
- Understand concepts like model serving, scalability, and monitoring.
8. OpenAI GPT (Generative Pre-trained Transformer):
- Familiarize yourself with GPT architecture and its applications in natural language generation and understanding.
- Learn about fine-tuning GPT models for specific tasks.
As for websites to refer to, here are some excellent resources:
- Towards Data Science (https://towardsdatascience.com/)
- Coursera (https://www.coursera.org/)
- Udacity (https://www.udacity.com/)
- TensorFlow (https://www.tensorflow.org/)
- PyTorch (https://pytorch.org/)
- OpenAI's official documentation and blog (https://openai.com/)
Make sure to thoroughly understand these topics and practice coding and implementing algorithms. Good luck with your learning journey!
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