From Zero to LLM: Build & Train Your Own LLM from Scratch with Keras! (Step-by-Step Visual Guide)
Ever wondered what’s really going on inside AI models like ChatGPT?
Forget the black boxes and buzzwords, this is your backstage pass to the science, math, and real code that make Large Language Models tick.
🎬 What’s Inside This Tutorial?
🔬 Visual Deep Dive
Before we write a single line of code, you’ll get a crystal-clear, visual breakdown of every core concept powering LLMs:
How Transformers process language differently from RNNs and Markov models
Why self-attention is a game-changer (with easy-to-follow diagrams)
Embeddings, positional encoding, multi-head attention, feed-forward networks, batching, loss functions, and more—all explained visually and intuitively!
GitHub Repo – https://github.com/samugit83/TheGradientPath/tree/master/Keras/transformers/text_generation
👩💻 Hands-On Python Build (With Keras!)
Next, we roll up our sleeves and actually build a text generation model from scratch—no magic, just practical steps you can follow:
Prepare and tokenize your own dataset
Create input/output pairs for text prediction
Define and construct a Transformer architecture in Keras (no copy-paste, you’ll understand every line)
Train the model right alongside me, watching the metrics improve as your AI “learns” language
Monitor and debug the process in real time, just like a real ML engineer
🔎 Output Analysis & Model Evaluation
Once training is done, we’ll:
Generate new text using your custom model
Examine its outputs: where does it shine? Where does it stumble?
Discuss the strengths and quirks of what your LLM has learned—plus, how you can keep improving it
💡 Why This Video is Different
Every complex concept made simple and visual:
No hand-waving, just clear step-by-step animations and diagrams for every part of the model pipeline
You don’t just watch—you build and experiment:
Complete, well-documented Python script in Keras included (see links below)
Total transparency:
See how batching, dropout, backpropagation, and the actual matrix math drive every decision your model makes
🧠 Who Is This For?
Beginners who want to really understand how generative AI works
Students and devs with some Python & basic math who want to get hands-on
Anyone curious about the mechanics behind GPT, Transformers, and modern text generation
📝 Grab the Code & Resources
GitHub: Download the full Python script & Jupyter notebook, with step-by-step instructions and inline explanations
Timestamps: Jump straight to theory, coding, training, or output analysis—whatever you need most
👉 By the end of this video, you’ll:
Visualize every layer and operation inside a Transformer LLM
Build and train your own text generator in Keras from scratch
Evaluate the results like a pro, and know where to go next
No shortcuts, no confusion—just the clearest, most practical guide to building and understanding real AI.
Ready to demystify LLMs?
Hit play, download the code, and let’s unlock the real secrets of modern AI—together!
Don’t forget to comment below with your questions, ideas, or even your own model’s outputs!
Subscribe for more real, code-first AI breakdowns and tutorials.
Let’s get started!
#AI #ArtificialIntelligence #MachineLearning #DeepLearning #Transformers #GPT #LLM #LanguageModels #NeuralNetworks #Python #Keras #Tensorflow #NLP #NaturalLanguageProcessing #DataScience #AIExplained #TechTutorial #Coding #Programming #DataScienceTutorial #PythonProgramming #ML #DL #TextGeneration #GenerativeAI #AIForBeginners #ComputerScience #TechEducation #LearnAI #SelfAttention #AttentionMechanism #MarkovChains #RNN #LSTM #GRU #NeuralNetwork #MatrixMath #CodingTutorial #EducationalVideo #AIModel #OpenAI #PyTorch #Jupyter #JupyterNotebook #TechExplained #CodeAlong #VisualTutorial #ExplainerVideo #ArtificialNeuralNetworks #Embeddings #PositionalEncoding #Batching #LossFunction #Softmax #Backpropagation #FeedForward #ModelTraining #ModelEvaluation #AIEducation #CodingInPython #DeepLearningTutorial #BuildAI #AIProjects #DataScienceProjects #CodeLearning #BeginnerFriendly #MachineLearningTutorial #PythonAI #AI101 #KerasTutorial #TextAI #AITextGeneration #AIConcepts #TransformerModel #GenerativeModel #AICommunity #BuildWithMe #AIJourney #TechCommunity #LearnToCode #NeuralNetworkExplained #ProgrammingForBeginners #NLPProjects #MLProjects #MLExplained #AIInPython #AIAnimation #AIDiagrams #CodingFromScratch #TechHowTo #MLFromScratch #TransformersExplained #CodeNewbie #CodeEducation #MatrixCalculations #PythonForBeginners #DeepLearningProjects #OpenAIDev #TrainYourModel #AIDevelopment #MachineLearningForAll #TextProcessing #VisualAI #AIVisualization #MLVisualization #KerasDeepLearning #VisualML #AIDebugging #TechBreakdown #TechSimplified #PythonProjects #ModernAI #AIEngineering #TransformersArchitecture #LearnWithMe #LetsCode #AIWorkflow #TechForBeginners #NextWordPrediction #CodingScience
Ever wondered what’s really going on inside AI models like ChatGPT?
Forget the black boxes and buzzwords, this is your backstage pass to the science, math, and real code that make Large Language Models tick.
🎬 What’s Inside This Tutorial?
🔬 Visual Deep Dive
Before we write a single line of code, you’ll get a crystal-clear, visual breakdown of every core concept powering LLMs:
How Transformers process language differently from RNNs and Markov models
Why self-attention is a game-changer (with easy-to-follow diagrams)
Embeddings, positional encoding, multi-head attention, feed-forward networks, batching, loss functions, and more—all explained visually and intuitively!
GitHub Repo – https://github.com/samugit83/TheGradientPath/tree/master/Keras/transformers/text_generation
👩💻 Hands-On Python Build (With Keras!)
Next, we roll up our sleeves and actually build a text generation model from scratch—no magic, just practical steps you can follow:
Prepare and tokenize your own dataset
Create input/output pairs for text prediction
Define and construct a Transformer architecture in Keras (no copy-paste, you’ll understand every line)
Train the model right alongside me, watching the metrics improve as your AI “learns” language
Monitor and debug the process in real time, just like a real ML engineer
🔎 Output Analysis & Model Evaluation
Once training is done, we’ll:
Generate new text using your custom model
Examine its outputs: where does it shine? Where does it stumble?
Discuss the strengths and quirks of what your LLM has learned—plus, how you can keep improving it
💡 Why This Video is Different
Every complex concept made simple and visual:
No hand-waving, just clear step-by-step animations and diagrams for every part of the model pipeline
You don’t just watch—you build and experiment:
Complete, well-documented Python script in Keras included (see links below)
Total transparency:
See how batching, dropout, backpropagation, and the actual matrix math drive every decision your model makes
🧠 Who Is This For?
Beginners who want to really understand how generative AI works
Students and devs with some Python & basic math who want to get hands-on
Anyone curious about the mechanics behind GPT, Transformers, and modern text generation
📝 Grab the Code & Resources
GitHub: Download the full Python script & Jupyter notebook, with step-by-step instructions and inline explanations
Timestamps: Jump straight to theory, coding, training, or output analysis—whatever you need most
👉 By the end of this video, you’ll:
Visualize every layer and operation inside a Transformer LLM
Build and train your own text generator in Keras from scratch
Evaluate the results like a pro, and know where to go next
No shortcuts, no confusion—just the clearest, most practical guide to building and understanding real AI.
Ready to demystify LLMs?
Hit play, download the code, and let’s unlock the real secrets of modern AI—together!
Don’t forget to comment below with your questions, ideas, or even your own model’s outputs!
Subscribe for more real, code-first AI breakdowns and tutorials.
Let’s get started!
#AI #ArtificialIntelligence #MachineLearning #DeepLearning #Transformers #GPT #LLM #LanguageModels #NeuralNetworks #Python #Keras #Tensorflow #NLP #NaturalLanguageProcessing #DataScience #AIExplained #TechTutorial #Coding #Programming #DataScienceTutorial #PythonProgramming #ML #DL #TextGeneration #GenerativeAI #AIForBeginners #ComputerScience #TechEducation #LearnAI #SelfAttention #AttentionMechanism #MarkovChains #RNN #LSTM #GRU #NeuralNetwork #MatrixMath #CodingTutorial #EducationalVideo #AIModel #OpenAI #PyTorch #Jupyter #JupyterNotebook #TechExplained #CodeAlong #VisualTutorial #ExplainerVideo #ArtificialNeuralNetworks #Embeddings #PositionalEncoding #Batching #LossFunction #Softmax #Backpropagation #FeedForward #ModelTraining #ModelEvaluation #AIEducation #CodingInPython #DeepLearningTutorial #BuildAI #AIProjects #DataScienceProjects #CodeLearning #BeginnerFriendly #MachineLearningTutorial #PythonAI #AI101 #KerasTutorial #TextAI #AITextGeneration #AIConcepts #TransformerModel #GenerativeModel #AICommunity #BuildWithMe #AIJourney #TechCommunity #LearnToCode #NeuralNetworkExplained #ProgrammingForBeginners #NLPProjects #MLProjects #MLExplained #AIInPython #AIAnimation #AIDiagrams #CodingFromScratch #TechHowTo #MLFromScratch #TransformersExplained #CodeNewbie #CodeEducation #MatrixCalculations #PythonForBeginners #DeepLearningProjects #OpenAIDev #TrainYourModel #AIDevelopment #MachineLearningForAll #TextProcessing #VisualAI #AIVisualization #MLVisualization #KerasDeepLearning #VisualML #AIDebugging #TechBreakdown #TechSimplified #PythonProjects #ModernAI #AIEngineering #TransformersArchitecture #LearnWithMe #LetsCode #AIWorkflow #TechForBeginners #NextWordPrediction #CodingScience
