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Scrambled cities: If you're not gonna do the work(on your soul), then you might as well work til you die

 There are two tiers in normal life. Those who are survive and those who thrive.  But nothing is a mistake in life: Nothing comes by chance. You have to prepare for it.  If you thought life was about making money and beating out the Jones's, you've lost already.  Life is: Desire for connection. All unhealthy competition is isolating. We live in an isolated-enough world already. This connection comes from a surprising place: Self-acceptance. A lot of us need to go back to the classroom. A lot of us need to take a silent breather and check in with what we need. This takes serious work. It's the real work. It relies on respect for others and self-respect.  A lot of people in the self-help community try to push the self aside: Manning up is the only side of it they see. But there are certain non-negotiables in this life. Certain things we can't cheat ourselves out of. We are not a piece of meat being acted on.    However, this is where God comes in. Some o...
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Screw Chat GPT: Here's how to build your own LLM

 Becoming proficient in machine learning and natural language processing (NLP) and eventually working on large language models (LLMs) like GPT-3 as a rookie is a rewarding but challenging journey. Here's a roadmap to help you get started and progress in this field:

Phase 1: Foundations (3-6 months)

  1. Learn Python:

    • Start with Python, a widely used programming language in the field of machine learning and NLP.
    • Learn the basics of Python syntax and data structures.

  2. Math Fundamentals:

    • Build a strong foundation in mathematics, particularly linear algebra, calculus, and probability.

  3. Machine Learning Basics:

    • Study the fundamentals of machine learning, including supervised and unsupervised learning, classification, regression, and evaluation metrics.

  4. Python Libraries:

    • Familiarize yourself with Python libraries such as NumPy, Pandas, and Matplotlib for data manipulation, analysis, and visualization.

Phase 2: NLP Fundamentals (3-6 months)

  1. NLP Basics:

    • Start learning about NLP concepts like tokenization, text preprocessing, and basic feature engineering.

  2. NLP Libraries:

    • Explore NLP libraries like NLTK and spaCy to perform common NLP tasks.

Phase 3: Deep Learning (6-12 months)

  1. Deep Learning Foundations:

    • Learn the basics of deep learning, neural networks, and how they work.
    • Study activation functions, loss functions, and gradient descent.

  2. TensorFlow or PyTorch:

    • Choose one of these deep learning frameworks and become proficient in it.
    • Follow tutorials and build simple neural networks.

Phase 4: Specialize in NLP (6-12 months)

  1. Advanced NLP Concepts:

    • Dive deeper into NLP with topics like word embeddings (e.g., Word2Vec, GloVe), sequence-to-sequence models, and attention mechanisms.

  2. NLP Projects:

    • Start working on small NLP projects to apply what you've learned.
    • Build text classifiers, sentiment analysis models, or chatbots.

  3. Natural Language Processing Libraries:

    • Explore libraries like Transformers, Hugging Face, and spaCy for advanced NLP tasks.

Phase 5: Large Language Models (12+ months)

  1. Study Large Language Models:

    • Learn about large language models like GPT-3, BERT, and their architectures.

  2. Transfer Learning:

    • Understand transfer learning in NLP and how to fine-tune pre-trained models for specific tasks.

  3. Ethics and Bias:

    • Be aware of ethical considerations, biases, and responsible AI practices when working with LLMs.

  4. Projects and Collaborations:

    • Start small and work on projects that involve LLMs.
    • Collaborate with others in the field to learn from their experiences.

Phase 6: Continual Learning (Ongoing)

  1. Stay Updated:

    • NLP and AI are rapidly evolving fields. Stay up-to-date with the latest research papers, conferences, and developments.

  2. Online Courses and Certificates:

    • Consider taking online courses or earning certificates from platforms like Coursera, edX, or fast.ai to deepen your knowledge.

  3. Contribute and Publish:

    • Contribute to open-source NLP projects.
    • Share your work and findings through blog posts, GitHub, or research papers.

  4. Networking:

    • Attend AI and NLP meetups, conferences, and webinars.
    • Connect with professionals in the field through LinkedIn or other networking platforms.

Remember that learning and mastering machine learning, NLP, and working with LLMs is a gradual process. It's essential to build a strong foundation before delving into more advanced topics. Be patient, stay curious, and keep working on projects to gain practical experience. Collaboration and networking can also be invaluable in your journey.

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