#Modl Deep Learning Framework Tutorial

Let’s see if I can cover the ML pipeline in 60 seconds ⏰😅 Machine learning isn’t just training a model. A production ML lifecycle typically looks like this: 1️⃣ Define the problem & objective 2️⃣ Collect and (if needed) label data 3️⃣ Split into train / validation / test sets 4️⃣ Data preprocessing & feature engineering 5️⃣ Train the model (forward pass + backpropagation in deep learning) 6️⃣ Evaluate on held-out data to measure generalization 7️⃣ Hyperparameter tuning (learning rate, architecture, etc.) 8️⃣ Final testing before release 9️⃣ Deploy (batch inference or real-time serving behind an API) 🔟 Monitor for data drift, concept drift, latency, cost, and reliability 1️⃣1️⃣ Retrain when performance degrades Training updates weights. Evaluation measures performance. Deployment serves predictions. Monitoring keeps the system healthy. It’s not linear. It’s a loop. And once you move beyond a single experiment, that loop becomes a systems problem. At scale, the challenge isn’t just modeling … it’s building reliable, scalable infrastructure that supports the entire lifecycle. Curious if this type of content is helpful! Lmk in the comments & as always Happy Building! 🤍

If I were to get started to learn LLMs — here’s exactly how I’d go about it 👇 (No degree, no prior experience needed. Just consistency and curiosity.) Step 1: Learn Python (Weeks 1–4) → Do “Python for Everybody” or freeCodeCamp → Practice daily for 30 minutes → Push everything to GitHub — even your messy code → Start building your proof of work from Day 1 Step 2: Get ML Foundations (Weeks 5–8) → Take Andrew Ng’s ML course (still gold) → Pick 1 project on Kaggle (Titanic is great for starters) → Train a basic model → deploy it with Streamlit → Now you’ve built your first ML app 👏 Step 3: Deep Learning Phase (Months 3–4) → Learn through fast.ai — super hands-on → Fine-tune a small transformer (like BERT) on Hugging Face → Don’t aim for perfection, aim to finish one project well Step 4: Understand Transformers & LLMs (Months 5–6) → Rebuild a mini GPT using Karpathy’s nanoGPT → This will help you actually understand self-attention, tokens, and training loops → Watch explainer videos + read blog posts to reinforce the concepts Step 5: Learn the Real LLM Stack (Months 7–9) → LangChain for chaining prompts & calling tools → LangGraph for multi-agent workflows and memory → Weaviate or LanceDB for retrieval (RAG setups) → QLoRA for fine-tuning open models → vLLM for efficient inference Step 6: Build and Publish (Months 10–12) → Choose one simple use case (like a research assistant or AI chatbot) → Build it end-to-end using the stack you’ve learned → Make a demo video, write a short architecture breakdown → Share on GitHub, LinkedIn, and Twitter — this is your new resume Portfolio project ideas: https://www.instagram.com/p/DG6l_MrShZ8/?igsh=NTc4MTIwNjQ2YQ== ( Link also in “Learn AI” highlights) [ai roadmap, llm learning path, genai career, how to learn ai, langchain tutorial, huggingface projects, python for ai, llm from scratch, build in public, ai agent builder, nanoGPT, deep learning 2025, vector dbs, fine tuning llms, ai portfolio project, ml roadmap, data science career guide, prompt engineering, ai learning journey]

Let’s build a Machine Learning Model for Sentiment Analysis! 🤖💬 Using this dataset that I found online, I was able to experiment with building ML Models using Tensorflow and Python. 💻 This is the first time I’ve made a video about building an ML Model, so let me know if you’d like to see more! 🎥 After testing this, I was pretty impressed with the results. Would you like to see that video? 👀

In most deep learning models, an input is transformed by passing it through a series of large matrices filled with tunable parameters. Each matrix represents a layer, and when your input reaches it, the model performs a bunch of math operations - mainly matrix multiplication, addition, and a nonlinear activation. In reality, deep learning models are simply layers and layers of math transformations and matrix multiplications applied to an input vector. Each layer reshapes the information slightly, highlighting some features and reducing others. During training, the model adjusts the numbers inside these matrices so the transformations produce better and better outputs. Through this mathematical process, deep learning models can gradually turn raw input (like text, images, or audio) into meaningful predictions or representations. C: 3blue1brown #machinelearning #deeplearning #statistics #computerscience #coding #mathematics #math #physics #science #education

comment ‘AI’ and I’ll send you the link in your DMs this is such a great resource to guide you on your AI/ML journey! #techcareer #ai #machinelearning #careergrowthtips #datascience #coding

In most deep learning models, an input is transformed by passing it through a series of large matrices filled with tunable parameters. Each matrix represents a layer, and when your input reaches it, the model performs a bunch of math operations—mainly matrix multiplication, addition, and a nonlinear activation. In reality, deep learning models are simply layers and layers of math transformations and matrix multiplications applied to an input vector. Each layer reshapes the information slightly, highlighting some features and reducing others. During training, the model adjusts the numbers inside these matrices so the transformations produce better and better outputs. Through this mathematical process, deep learning models can gradually turn raw input (like text, images, or audio) into meaningful predictions or representations. Want to learn ML/AI? Accelerate your learning in our Weekly AI Newsletter—educational, easy to understand, mathematically explained, and completely free (link in bio 🔗). C: 3blue1brown Join our AI community for more posts like this @aibutsimple 🤖 #machinelearning #deeplearning #statistics #computerscience #coding #mathematics #math #physics #science #education

Data Analysis & coding with AI (not ChatGPT) part 5 📈 Follow @sundaskhalidd for more 💕 Have you tried using Bard, ChatGPT or other AI tools for date analysis? I recently learned that Bard launched coding in +20 languages and the integration with Google Colab is pretty neat 💯 Let me know if you have any cool hacks 👇🏽 also, let me know if you want me to cover any data analysis technique next 😀 Follow @sundaskhalidd for data science, tech and career educational content✨ Tags 🏷️ #python #learnpython #datavisualization #googlecolab #dataanalysis #programming #codinglife💻 #sql #softwareengineer learntocode #datascience #dataanalyst #datascientist #datacareer #vscode #genieai #chatgpt #tabnine #pandas #bard

Learn to Learn Machine Learning 👾 This one is basic af, and I’ve abstracted a lot of the complexity but we’ll get more complex as we go. (It was 54 seconds, then I yapped in the intro too much) If there are any topics you want me to cover, let me know #machinelearning #ai

Here’s your full roadmap on how to get into machine learning. Comment “Roadmap” to get the pdf. Save and follow for more. #ai #machinelearning #coding #programming #cs

Deep learning 😋 some ideas if you want to learn more on your journey for any stage in your career. Lots of people think that these are just for college students looking for a job but that is not the case. I even like to do projects to learn something new, a new field, topic or experiment with libraries or models I haven’t dealt with. #coding #codingprojects #swe #developer #chatgpt #deeplearning #artificialintelligence #ml #productmanager

Large Language Models (LLMs) such as ChatGPT are based on neural networks called transformers, an architecture built using multiple attention mechanisms and multilayer perceptrons (MLPs). These models process input text by learning context through self-attention mechanisms, which weighs the importance of each pair of words. This way, long sequences are no longer an issue. This contextual understanding is passed through MLPs, which learn the representations and patterns of the sequence. To generate text, the model generates a probability distribution of the next word; we choose the highest-probability word and keep predicting the next word, iterating to create a sentence or paragraph. C: 3blue1brown Join our AI community for more posts like this @aibutsimple 🤖 #neuralnetwork #llm #gpt #artificialintelligence #machinelearning #3blue1brown #deeplearning #neuralnetworks #datascience #python #ml #pythonprogramming #datascientist

Recommendations by llm #datascience #machinelearning #womeninstem #learningtogether #progresseveryday #tech #consistency #recommedations