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@marketizit: Cómo manejar muchas redes sociales y no desvivirte en el intento #monetizar

Elena Díaz / Marketizit
Elena Díaz / Marketizit
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Wednesday 09 September 2026 04:19:14 GMT
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josselingperez45
Jossy mendoza :
Como es amiga
2026-09-26 19:51:36
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bellezaconanamary
Anamary :
Cómo puedo aprender
2026-09-09 20:56:43
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صباح النور #عصفوره #صباح_الخير #عصفوره_بغداد #صباح#النور
صباح النور #عصفوره #صباح_الخير #عصفوره_بغداد #صباح#النور
1000-7 #tokyoghoul #kenkaneki #tokyoghouledit #Anime #fyp
1000-7 #tokyoghoul #kenkaneki #tokyoghouledit #Anime #fyp
#cinta #cintamodeladora #foryou #gym
#cinta #cintamodeladora #foryou #gym
If you’re moving from traditional ML into Deep Learning, TensorFlow is one of the frameworks worth learning. 🔹 WHAT IS TENSORFLOW? TensorFlow is an open-source machine learning framework used to build, train, evaluate, and deploy ML and deep learning models. Think of the workflow as: Data → Model → Loss → Gradients → Optimizer → Training → Evaluation → Deployment 🧱 1. BUILD THE MODEL Learn how neural networks are structured: → Input layer → Dense layers → Activation functions → Output layer Common activations: ⚡ ReLU 🎯 Sigmoid 📊 Softmax 〰️ Tanh 📚 2. PREPARE YOUR DATA Before training: → Clean data → Handle missing values → Encode categories → Normalize/standardize features → Split into train/validation/test sets Garbage data → garbage model. 🗑️➡️🤖 ⚙️ 3. COMPILE THE MODEL Define: 🎯 Loss function → Measures prediction error 📈 Optimizer → Updates model parameters 📊 Metrics → Measures performance Common choices: Loss: MSE, Binary Cross-Entropy, Categorical Cross-Entropy Optimizer: SGD, Adam, RMSprop 🔥 4. TRAIN THE MODEL During training: Input → Prediction → Loss → Gradient → Weight Update → Repeat The model gradually learns patterns from the training data. Key concepts: 📌 Epochs 📌 Batch size 📌 Learning rate 📌 Forward propagation 📌 Backpropagation 📌 Gradients 🧪 5. EVALUATE Never judge a model only by training performance. Check: → Validation loss → Test performance → Precision → Recall → F1-score → Confusion matrix Watch for: ⚠️ Overfitting ⚠️ Underfitting ⚠️ Data leakage 🚀 6. IMPROVE THE MODEL Experiment with: 🧠 Architecture 📚 More/better data ⚙️ Learning rate 📦 Batch size 🔄 Regularization 🛑 Early stopping 🎯 Dropout 🔧 Hyperparameter tuning 👁️ 7. GO BEYOND DENSE NETWORKS Once you understand the basics: CNNs → Images & computer vision RNNs / LSTMs → Sequential data Transfer Learning → Reuse pretrained models Transformers → Modern NLP and multimodal AI 🎯 TENSORFLOW LEARNING ROADMAP Python ↓ NumPy ↓ Machine Learning Fundamentals ↓ Neural Networks ↓ TensorFlow / Keras ↓ CNNs ↓ Transfer Learning ↓ Advanced Deep Learning ↓ Model Deployment 💡 DON’T JUST LEARN TENSORFLOW Build projects. 🔥 Image classifier 🔥 Customer churn predictor 🔥 Time-series forecasting 🔥 Plant disease detector 🔥 Sentiment classifier 🔥 Object detection system 🔥 Recommendation system Framework knowledge gets you started. Projects prove you can build. 🚀 📌 Save this if you’re learning TensorFlow. #TensorFlow #Keras #DeepLearning #creatorsearchinsights #computerprogrammingforbeginners
If you’re moving from traditional ML into Deep Learning, TensorFlow is one of the frameworks worth learning. 🔹 WHAT IS TENSORFLOW? TensorFlow is an open-source machine learning framework used to build, train, evaluate, and deploy ML and deep learning models. Think of the workflow as: Data → Model → Loss → Gradients → Optimizer → Training → Evaluation → Deployment 🧱 1. BUILD THE MODEL Learn how neural networks are structured: → Input layer → Dense layers → Activation functions → Output layer Common activations: ⚡ ReLU 🎯 Sigmoid 📊 Softmax 〰️ Tanh 📚 2. PREPARE YOUR DATA Before training: → Clean data → Handle missing values → Encode categories → Normalize/standardize features → Split into train/validation/test sets Garbage data → garbage model. 🗑️➡️🤖 ⚙️ 3. COMPILE THE MODEL Define: 🎯 Loss function → Measures prediction error 📈 Optimizer → Updates model parameters 📊 Metrics → Measures performance Common choices: Loss: MSE, Binary Cross-Entropy, Categorical Cross-Entropy Optimizer: SGD, Adam, RMSprop 🔥 4. TRAIN THE MODEL During training: Input → Prediction → Loss → Gradient → Weight Update → Repeat The model gradually learns patterns from the training data. Key concepts: 📌 Epochs 📌 Batch size 📌 Learning rate 📌 Forward propagation 📌 Backpropagation 📌 Gradients 🧪 5. EVALUATE Never judge a model only by training performance. Check: → Validation loss → Test performance → Precision → Recall → F1-score → Confusion matrix Watch for: ⚠️ Overfitting ⚠️ Underfitting ⚠️ Data leakage 🚀 6. IMPROVE THE MODEL Experiment with: 🧠 Architecture 📚 More/better data ⚙️ Learning rate 📦 Batch size 🔄 Regularization 🛑 Early stopping 🎯 Dropout 🔧 Hyperparameter tuning 👁️ 7. GO BEYOND DENSE NETWORKS Once you understand the basics: CNNs → Images & computer vision RNNs / LSTMs → Sequential data Transfer Learning → Reuse pretrained models Transformers → Modern NLP and multimodal AI 🎯 TENSORFLOW LEARNING ROADMAP Python ↓ NumPy ↓ Machine Learning Fundamentals ↓ Neural Networks ↓ TensorFlow / Keras ↓ CNNs ↓ Transfer Learning ↓ Advanced Deep Learning ↓ Model Deployment 💡 DON’T JUST LEARN TENSORFLOW Build projects. 🔥 Image classifier 🔥 Customer churn predictor 🔥 Time-series forecasting 🔥 Plant disease detector 🔥 Sentiment classifier 🔥 Object detection system 🔥 Recommendation system Framework knowledge gets you started. Projects prove you can build. 🚀 📌 Save this if you’re learning TensorFlow. #TensorFlow #Keras #DeepLearning #creatorsearchinsights #computerprogrammingforbeginners
One of the most important optimization algorithms behind Machine Learning. But what is it actually doing? 🧠 THE SIMPLE IDEA Imagine you’re standing on a mountain and want to reach the lowest point. You can’t see the whole landscape. So you: 👀 Look at the slope ⬇️ Move in the direction of steepest descent 🔁 Repeat 🎯 Eventually reach a low point That’s essentially what Gradient Descent does. 📐 THE CORE FORMULA New Parameter = Old Parameter − Learning Rate × Gradient In other words: θ ← θ − α∇J(θ) Where: 🔹 θ = model parameters 🔹 α = learning rate 🔹 ∇J(θ) = gradient of the loss function ⚙️ HOW IT WORKS 1️⃣ Initialize parameters Start with random or initial values. ⬇️ 2️⃣ Make predictions The model produces predictions. ⬇️ 3️⃣ Calculate loss Measure how wrong the predictions are. ⬇️ 4️⃣ Calculate the gradient Find which direction increases the loss. ⬇️ 5️⃣ Move in the opposite direction Update the parameters. ⬇️ 6️⃣ Repeat Keep updating until the loss becomes sufficiently small. 🎯 LEARNING RATE MATTERS 🐢 Too small → Training becomes extremely slow. 🐇 Too large → The model may overshoot the minimum. ⚡ Good learning rate → Faster and more stable convergence. 🔥 TYPES OF GRADIENT DESCENT Batch Gradient Descent Uses the entire dataset for each update. Stochastic Gradient Descent (SGD) Uses one training example at a time. Mini-Batch Gradient Descent Uses a small batch of examples. 👉 Mini-batch gradient descent is widely used in modern deep learning. 🤖 WHERE IS IT USED? Gradient-based optimization appears throughout ML and deep learning: 🧠 Neural Networks 👁️ Computer Vision 💬 NLP 🤖 Transformers 📈 Regression 🎯 Classification 💡 REMEMBER THIS Loss tells you HOW WRONG you are. Gradient tells you WHICH DIRECTION to move. Learning rate tells you HOW BIG the step should be. Gradient Descent repeats those steps to minimize the loss. 📌 Save this if you’re learning Machine Learning. #MachineLearning #GradientDescent #DeepLearning #creatorsearchinsights #gradientdescent
One of the most important optimization algorithms behind Machine Learning. But what is it actually doing? 🧠 THE SIMPLE IDEA Imagine you’re standing on a mountain and want to reach the lowest point. You can’t see the whole landscape. So you: 👀 Look at the slope ⬇️ Move in the direction of steepest descent 🔁 Repeat 🎯 Eventually reach a low point That’s essentially what Gradient Descent does. 📐 THE CORE FORMULA New Parameter = Old Parameter − Learning Rate × Gradient In other words: θ ← θ − α∇J(θ) Where: 🔹 θ = model parameters 🔹 α = learning rate 🔹 ∇J(θ) = gradient of the loss function ⚙️ HOW IT WORKS 1️⃣ Initialize parameters Start with random or initial values. ⬇️ 2️⃣ Make predictions The model produces predictions. ⬇️ 3️⃣ Calculate loss Measure how wrong the predictions are. ⬇️ 4️⃣ Calculate the gradient Find which direction increases the loss. ⬇️ 5️⃣ Move in the opposite direction Update the parameters. ⬇️ 6️⃣ Repeat Keep updating until the loss becomes sufficiently small. 🎯 LEARNING RATE MATTERS 🐢 Too small → Training becomes extremely slow. 🐇 Too large → The model may overshoot the minimum. ⚡ Good learning rate → Faster and more stable convergence. 🔥 TYPES OF GRADIENT DESCENT Batch Gradient Descent Uses the entire dataset for each update. Stochastic Gradient Descent (SGD) Uses one training example at a time. Mini-Batch Gradient Descent Uses a small batch of examples. 👉 Mini-batch gradient descent is widely used in modern deep learning. 🤖 WHERE IS IT USED? Gradient-based optimization appears throughout ML and deep learning: 🧠 Neural Networks 👁️ Computer Vision 💬 NLP 🤖 Transformers 📈 Regression 🎯 Classification 💡 REMEMBER THIS Loss tells you HOW WRONG you are. Gradient tells you WHICH DIRECTION to move. Learning rate tells you HOW BIG the step should be. Gradient Descent repeats those steps to minimize the loss. 📌 Save this if you’re learning Machine Learning. #MachineLearning #GradientDescent #DeepLearning #creatorsearchinsights #gradientdescent
#تطوير_الذات #علم_النفس #قصص #اكسبلور #الوعي_النفسي
#تطوير_الذات #علم_النفس #قصص #اكسبلور #الوعي_النفسي

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