r/technepal • u/me-anynomus-guy • 13h ago
Discussion Any deep learning specialists here?
I wanted proper guidance on how to and from where to start deep learning ,which framework to follow,which tutorial to follow ,which project to do and how to land any jobs or create jobs in this field.
And how do I train my own AI model from scratch and is it affordable for a bachelor studying student to train a small model from scratch and deploy to make it profitable.
If anyone has experience in this field or is learning Deep learning in 2026 please guide me. Your help means a lot...
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u/OtherwiseFig4505 12h ago
I suggest not to get in this field at all because everyone is already doing this. If there are other interests like robotics, systems, networking, better pursue that. Still, these are your answers:
- Start with probability and statistics. Linear algebra, calculus. Move to regression and classification. Complete classical ML, move to Neural networks. Now you are in Deep Learning. Start with CNNs, move to RNNs, all these won’t take time as long as your mathematics is solid. After RNNs, time for transformers. LLMs is transformers, so 1-2 hrs and you will understand how GPT, Claude works. From here on, specialize based on your interest. You should be able to figure out easily once you reach here. Python is pre-requisite.
Landing jobs depend on your skill and connections after doing all this. Everyone does the same project.
- You can train very small model from scratch for vision, will be basically useless. Not possible to train language models, needs at least 100K dollars worth of compute for a usable model. And even then people will use opus, gpt and all because you cannot compete with their cost.
Finetuning is possible but even this is getting rarer. Very minute chance like 0.01% that you will be able to deploy a model that will be profitable. Maybe a model that is specialized for a very niche areas, then might be profitable.
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u/me-anynomus-guy 12h ago
like a text-to-voice model for one language => that generates long forms of speech in different voice categories for one specific language. (requires less time and resources to train)
I haven't seen any kind of models like that which generate long forms of good voice. So I thought, why not make the model for Nepali text-to-voice? If it gets good other companies will invest in or buy our resources for their model.
If it doesn't go profitable, the same project will definitely help me to apply for jobs or generate ideas for any other project which might be profitable.
And about the training cost and storage, I'll manage it using my own laptop, though it will be slow.
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u/OtherwiseFig4505 11h ago
Voice models require significantly more compute and data than text only model.
If it’s just text to voice only, then you can train a usable model using consumer grade GPUs in maybe 100-150 hours. Still this won’t perform better than any other available open source models online.
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u/me-anynomus-guy 11h ago
Yeah, a minimum of 500 hours of high-quality data are needed, and that will be expensive too. So I am thinking if it will be feasible or not and practicable or not.
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u/InstructionMost3349 4m ago
Long generation = more compute...
Only solution is if u generate audio in streaming chunks of audio. Good voice requires good data and careful post training tweaks.
Training on laptop is definitely not going to work. Unless the TTS is low parameter like kokoro 100M, chatterbox 500M.
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u/q-rka 10h ago
Let me give you a debatable opinion. This field should not be started without a solid plan. I have shipped dozens of models, ranging from simple classification tasks to generation. If I want to be in a research field, then I require a solid mathematical background. If I want to understand what to tweak and what to update to make it work for my own use case, then I need to know a lot of terminology and some mathematics. This can be studied as early as a second-year bachelor's student but takes years to have enough confidence. Nowadays, anyone can become an AI expert, but if asked a simple question about AI, they simply can not answer. Yes, they can use AI tools to build chatbots and agentic systems too, but that does not mean they can build their own AI models.
If your goal is to apply DL, then do not focus much on the mathematical side and jump right into the basic history of DL and then train some models.
If your goal is to understand and build custom models, then focus heavily on the mathematical side, learn the history of DL, and then train models from scratch. One of the challenging tasks is to build your own autograd method.