@datascibykashi: 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

Data Scientist | Kashi

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Tuesday 08 September 2026 12:43:02 GMT