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What Code LLMs Mean for the Future of Software Development | IBM

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Welcome Overview Machine learning types Machine learning algorithms Statistical machine learning Linear algebra for machine learning Data visualization for machine learning Uncertainty quantification Bias variance tradeoff Bayesian Statistics Singular value decomposition Overview Feature selection Feature extraction Vector embedding Latent space Principal component analysis Linear discriminant analysis Upsampling Downsampling Synthetic data Data leakage Overview Linear regression Lasso regression Ridge regression State space model Time series Autoregressive model Overview Decision trees K-nearest neighbors (KNNs)Naive bayes Random forest Support vector machine Logistic regression Overview Boosting Bagging Gradient boosting Gradient boosting classifier Overview Transfer learning Overview Overview K means clustering Hierarchical clustering A priori algorithm Gaussian mixture model Anomaly detection Overview Collaborative filtering Content based filtering Overview Reinforcement learning human feedback Deep reinforcement learning Overview Overview Backpropagation Encoder-decoder model Recurrent neural networks Long short-term memory (LSTM)Convolutional neural networks Overview Attention mechanism Grouped query attention Positional encoding Autoencoder Mamba model Graph neural network Overview Generative model Generative AI vs. predictive AI Overview Reasoning models Small language models Instruction tuning LLM parameters LLM temperature LLM benchmarks LLM customization LLM alignment Tutorial: Multilingual LLM agent Diffusion models Variational autoencoder (VAE)Generative adversarial networks (GANs)Overview Vision language models Tutorial: Build an AI stylist Tutorial: Multimodal AI queries using Llama Tutorial: Multimodal AI queries using Pixtral Tutorial: Automatic podcast transcription with Granite Tutorial: PPT AI image analysis answering system Overview GraphRAG Tutorial: Build a multimodal RAG system with Docling and Granite Tutorial: Evaluate RAG pipline using Ragas Tutorial: RAG chunking strategies Tutorial: Graph RAG using knowledge graphs Tutorial: Inference scaling to improve multimodal RAG Overview Vibe coding Visit the 2025 Guide to AI Agents Overview LLM training Loss function Training data Model parameters Overview Gradient descent Stochastic gradient descent Hyperparameter tuning Learning rate Overview Parameter efficient fine tuning (PEFT)LoRA Tutorial: Fine tuning Granite model with LoRA Regularization Foundation models Overfitting Underfitting Few shot learning Zero shot learning Knowledge distillation Meta learning Data augmentation Catastrophic forgetting Overview Scikit-learn XGboost PyTorch Overview AI lifecyle AI inference Model deployment Machine learning pipeline Data labeling Model risk management Model drift AutoML Model selection Federated learning Distributed machine learning AI stack Overview Natural language understanding Overview Sentiment analysis Tutorial: Spam text classifier with PyTorch Machine translation Overview Information retrieval Information extraction Topic modeling Latent semantic analysis Latent Dirichlet Allocation Named entity recognition Word embeddings Bag of words Intelligent search Speech recognition Stemming and lemmatization Text summarization Conversational AI Conversational analytics Natural language generation Overview Image classification Object detection Instance segmentation Semantic segmentation Optical character recognition Image recognition Visual inspection Rina Diane Caballar Cole Stryker ChatGPT generative AI code generation Artificial intelligence (AI)Claude Google’s Gemini IBM Bob Mistral large language models (LLMs)Kaoutar El Maghraoui Bridget McGinn Rameswar Panda Fumiko Satoh IBM Privacy Statement trained fine-tuned datasets training data AI models natural language processing (NLP)debugging Code generation Code refactoring documentation legacy code Java open-source DeepSeek IBM Granite Code Go to episode software development prompt engineering generative computing ethics security automation upskilling benchmarks bias vibe coding AI agents software development multiagent agentic AI software engineering (SWE) agents small language models (SLMs)edge memory reasoning APIs workflows Spotify Apple Podcasts Find more episodes Rina Diane Caballar Cole Stryker Upcoming webinar - May 20, 2026 Upcoming webinar on AI‑native development Engineering teams are strained by legacy tools and tech debt. Discover how an AI‑native SDLC helps teams move faster while reducing risk. Join now IBM Bob Meet Bob™, your AI development partner An AI powered tool that helps you code, debug and deliver high quality software without breaking your flow. Start building with Bob Report Accelerate AI innovation through better development Discover how AI-native development is reshaping software engineering and how leading teams are using it to accelerate innovation and scale impact. Get the report Blue Pearl case study From 30 Days to 3 See how Blue Pearl transformed a legacy codebase, eliminated security risks and accelerated modernization with Bob—cutting delivery time by nearly 90%. Read the article Case Study Unraveling the past, rebuilding the future with Bob™ Discover how APIs IT generated complete documentation for decades old systems and modernized critical workloads—ten times faster with AI. Read the story IBM TechXchange IBM AI community Join the community for Al architects and builders to learn, share ideas and connect with others. Find your community Solutions Boost coding workflows with AI coding solutions Accelerate development, reduce repetitive work and modernize applications with AI-powered tools designed for enterprise-scale engineering teams. Explore AI coding Article What is AI code-generation software? See how large language models are reshaping software engineering—from writing new code to modernizing legacy systems at scale. Learn how it works Explore IBM Bob Explore AI coding solutions Explore AI consulting services Discover IBM Bob Explore AI coding solutions

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