It’s like any of their other courses. A lot of stuff you probably know or are aware of but a few hidden gem(ini)s along the way More on reddit.com

Please are my recommendations buddy! Generative AI Courses Generative AI Unleashed (Udemy): Comprehensive course covering principles, techniques, and applications. Mastering Generative AI and ChatGPT (GeeksforGeeks): Interactive learning, hands-on exercises, and expert guidance. Introduction to Generative AI (Google Cloud): Free, self-paced course introducing Generative AI concepts. LLM and Langchain Courses Introduction to LangChain, ChatGPT, and Gemini Pro (Udemy): Covers Langchain, LLMs, and applications. Langchain Ecosystem (Udemy): Dives deeper into Langchain ecosystem and applications. Hugging Face Courses Hugging Face Transformers (Udemy): Introduction to Hugging Face Transformers library. Skills for Software Engineer AI Programming languages (Python, Java, C++, R) Machine learning frameworks (TensorFlow, PyTorch, Keras) Deep learning (neural networks, NLP, computer vision) Generative AI (LLMs, Langchain, Hugging Face) Data structures and algorithms Communication and collaboration More on reddit.com

Hey! I am a “Gen AI Engineer” :’) so i think i might be able to provide some guidance here. I’ve only talked about text models here. So: Learn about the attention mechanism. (No need to deep dive. Just understand what it does). Transformers vs RNNs vs LSTM/GRU (Again a brief overview should suffice). Different types of LLMs based on transformers. Encoder-Decoder, Decoder-Decoder, etc. Just skim through what types of architectures are popular LLMs such as GPT 3.5/4, Llama2, Mistral 7B or 8x7B based on. Open Source vs Closed Source LLMs: Which ones are better at the moment? Different companies involved in the LLM rat race such as OpenAI, Google DeepMind, Mistral, Anthropic, etc. How to access these? For open source explore platforms such as Huggingface and Ollama. Prompt Engineering: Get comfortable with writing prompts. I would suggest Andrew NGs short course on prompt engineering to understand methods such as few shot learning. Learn about each of these: What are tokens? What are Vector Embeddings and what are some popular embedding model available today?Why do we need VectorDBs such as FAISS, Pinecone or ChromaDB etc? What does context length of an LLM mean? What is Quantization of LLM weights? Difference between 4-bit, 8-bit, 16-bit LLMs. Retrieval Augmented Generation or RAG: Understand how training data used for LLMs might not have all the info you need, RAG allows you to perform question answering on your personal documents. At this point, you might want to explore frameworks such as Langchain anf LlamaIndex. These provide one stop solution for all GenAI related requirements of your application. Finetuning LLMs: Why do we need to finetune LLMs? How is it different from RAG? How much GPU memory/VRAM would I need to finetune a small LLM such as Llama2? Techniques such as LoRA, QLoRA, PEFT, DPO etc. Finetuning an LLM would require some understanding of frameworks such as Pytorch or tensorflow. Advanced features such as Agents, Tool use, Funtion calling, Multimodal LLMs, etc. Access various opensource models such from ollama or huggingface. Also get familiarized with using OpenAI’s API. I would also suggest try to work with streamlit. It’s a very convenient way of creating a frontend for your application. These were some points that i thought you might find useful. If you have any further questions, please feel free to reach out. More on reddit.com

I did the generative ai in marketing (university of Virginia) and thought it was fairly meh. I didn't learn a whole lot that I didn't already know. Certainly want to take another one but it'll be a bit. More on reddit.com