#Deep Learning Frameworks

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! 🤍

Backpropagation is one of the core ideas behind deep learning today, even though its origins go farther back than most people realize. Paul Werbos first formalized the method in his 1974 PhD thesis, showing how gradients could be calculated efficiently in multi-layer neural networks. Early AI researchers, including Marvin Minsky, were cautious about it at first. It looked slow, costly, and difficult to use compared to simpler approaches. But as computing power increased, real experiments started proving how effective it actually was. Gradually, backpropagation became the standard way to train deep networks. It changed the entire field by allowing models to learn complex structures in data, powering breakthroughs in areas like computer vision, language understanding, and more. Today, backpropagation remains essential. Modern systems, including large language models such as ChatGPT and DeepSeek, are built on this foundation. C: Welch Labs #machinelearning #deeplearning #neuralnetworks #ai #computerscience

Google replaced the traditional education system with AI. Google’s new “Learn Your Way” uses AI to personalize learning from any book, document, or research paper based on how you understand best. Instead of one-size-fits-all lessons, it rewrites concepts using examples, analogies, visuals, and formats that actually make sense to you. This goes beyond education. Personalized learning powered by AI can transform how teams onboard, how founders train employees, and how professionals upskill. Internal documents, training material, and research can now be turned into content people retain. The real advantage in the AI era isn’t access to information. It’s how fast you can learn and apply it.

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

Gradient descent is a fundamental optimization algorithm used by most AI models to learn from data by minimizing a loss function, which measures how far the model’s predictions are from the true values. Conceptually, it treats the loss function as a landscape (we call this the loss landscape) with peaks and valleys representing high and low errors. At any point on this landscape, the gradient (vector of slopes) indicates the direction and steepness of the fastest increase in loss. Gradient descent uses the gradient to move in the opposite direction, downhill toward a valley, where the loss is minimized. With each step, the model adjusts its internal parameters (also known as the weights and biases) slightly to reduce the error, slowly improving its performance. This iterative process continues until the model reaches a point where further iterations don’t net much gain in performance. Or, in other words, the loss doesn’t change much. Essentially, this is how nearly all AI models “learn”: by following the gradient of the loss function to find parameter values that produce accurate predictions. C: Welch Labs #machinelearning #deeplearning #statistics #computerscience #coding #mathematics #math #physics #science #education #animation

Huge news from Google DeepMind: This paper is a new announcement about AlphaFold, the tool being used to solve what has been called one of the most important issues in modern science. Our team at HUGE* got to read this paper before it just got released, so let me explain what AlphaFold does and why this announcement is a big deal.... This is truly huge if true! If you like optimistic science and tech stories, follow here for more. #science #tech #discovery #googledeepmind #alphafold

Visualizing the architecture of intelligence. 🕸️✨ Every neural network is built on the same fundamental concept: Layers. 🟡 Input Layer: Receives the raw data (pixels, text, numbers). 🟢 Hidden Layers: Where the magic happens—processing features and finding patterns. 🟠 Output Layer: Delivers the final prediction or decision. From the simple Perceptron to the complex loops of an RNN, these structures are the blueprints for how machines learn. 📐 #NeuralNetworks #MachineLearning #DeepLearning #DataScience #AI #Education #Visualized

Comment "SKILLS" to get this new AI Learning Platform from Google. Google just dropped Google Skills. It's a huge free platform. Over 3,000 AI courses. Made by experts at Google and DeepMind. Think of it like a personal trainer for your career. It teaches the exact AI skills jobs need now. And in the future. The good news? It has everything. From super basic stuff. To advanced builds. Start here. AI prompting basics. Like telling ChatGPT what to do. Clear and simple. Then level up. Learn ethical AI. Handle data with tools like TensorFlow. Or set up no-code automations. Want pro level? Build your own language model. Fine-tune it. Add image gen or predictions. Like crafting a custom robot for your apps. They added AI Boost Bites too. Quick 10-minute lessons. Perfect for busy days. I recently tried one on prompt tricks. Asked it to fix my Python code. Boom. Fixed in seconds. Here's what you'll pick up fast: 1. Prompt better for marketing or code 2. Automate browser tasks like a pro 3. Test new AI tools without hassle Most courses? Totally free. Like free coffee on the house. Subscribe if you want more. Get official Google certificates. Skill badges for your resume. Proof you know your stuff. Split long projects into bits. Even if a lesson feels tough. Keep going. You got this. It's like having Google experts in your pocket. For developers building GitHub repos. Or no-code apps. Hands-on projects. Fresh updates on new models. The pressure to learn AI? Real. But this makes it easy. Jump in today. #googleskills #googleai #googledeepmind #aicourses #ailearning

Read our Weekly AI Newsletter—educational, easy to understand, mathematically explained, and completely free (link in bio 🔗). Backpropagation, a fundamental deep learning algorithm central to training neural networks, was popularized in the 1980s but has roots tracing back earlier. Paul Werbos is credited with formalizing backpropagation in his 1974 PhD thesis, providing a systematic way to compute gradients efficiently in multi-layer networks. Despite its potential, pioneers in the AI space such as Marvin Minsky were initially skeptical, hesitating to use backpropagation since it seemed computationally expensive and unreliable compared to simpler models. However, as computational power grew (and so did FLOPs), practical implementations showed better success—leading to the wide use of backpropagation that we know of today. It revolutionized machine learning by enabling deep networks to learn complex patterns, fueling advances in all fields of machine learning, such as computer vision, natural language processing, and more. As backpropagation is key to training machine learning models, it remains useful for any modern machine learning application—seen in LLMs like ChatGPT, DeepSeek r1, and more. C: welch labs Join our AI community for more posts like this @aibutsimple 🤖 #computervision #deeplearning #statistics #machinelearning #computerscience #coding #mathematics #math #physics #science #education

Machines don’t learn by magic — they learn by correcting mistakes step by step. Gradient Descent is the simple idea that powers every deep learning model you see today. From mountain slopes to neural networks — this is how learning really happens. #GradientDescent #DeepLearningBasics #MachineLearning #AIExplained #NeuralNetworks #LearningRate #DataScience #AIReels

Should I drop the method #foryou

Basics of Deep Learning 😮‍💨 . . • Instagram Algorithm • How Instagram Works • Deep Learning Instagram • Deep Learning