Ayush @TensorThrottleX
Crafting objective proof from vast and ambiguous datasets. Validating a chosen path to enable confident, decisive action. #100DaysOfCode nowhere Joined April 2025-
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Day 18: Log your grind. Share the gains. Prove your sweat counts. #100DaysRawGrind
Day 18: Log your grind. Share the gains. Prove your sweat counts. #100DaysRawGrind
Day 133: Data Science Journey ->Started Ch 12 HOML w/ Sk-Ln & TF: Dive into NNs from scratch! ->Approaches: Seq API for linear stacks; Func API for complex graphs ->Training: Compile(opt, loss), fit(X_train, y_train, epochs); predict new data #ML #DataScience #DeepLearning
Day 17: Log your grind. Share the gains. Prove your sweat counts. #100DaysRawGrind
Day 17: Log your grind. Share the gains. Prove your sweat counts. #100DaysRawGrind
Day 132: Data Science Journey ->AdaGrad: Per-param LR scaling ->RMSProp: EMA sq grads fixes AdaGrad fade ->Adam: Momentum + RMSProp mashup ->Conv Op: Kernel slide: O(i,j)=∑I(i+m,j+n)K(m,n)+b ->Pooling: Max/avg in windows: P(i,j)=max/mean(O(window)) #ML #DataScience #DeepLearning
Day 16: Log your grind. Share the gains. Prove your sweat counts. #100DaysRawGrind
Day 16: Log your grind. Share the gains. Prove your sweat counts. #100DaysRawGrind
Day 131: Data Science Journey -> Loss Functions: MSE for regression -> Gradient: ∂aL/∂θ_j = (1/n) Σ[(f(θ, x_i) - y_i) * ∂f/∂θ_j] -> Backprop: Chain rule across layers; ∇L via forward act & backward grad ->Learning Rate: Controls step size in GD #ML #DataScience #LearningRate
Day 15: Log your grind. Share the gains. Prove your sweat counts. #100DaysRawGrind
Day 15: Log your grind. Share the gains. Prove your sweat counts. #100DaysRawGrind
Day 130 - Data Science Journey: -> Practiced Chain Rule: dL/dW = dL/dA * dA/dZ * dZ/dW – Multiply Layers to Backprop Errors -> Sigmoid Gradient: ∇σ(z) = σ(z)(1 - σ(z)): Fine-Tune Activations for Deeper Nets #MachineLearning #NeuralNetworks #GradientDescent #DataScience
Day 14: Log your grind. Share the gains. Prove your sweat counts. #100DaysRawGrind
Day 14: Log your grind. Share the gains. Prove your sweat counts. #100DaysRawGrind
Day 129: Data Science Journey ->Gradient = dY/dX: Slope cal ->Derivative: Rate, f'(x) = lim h→0 [f(x+h)-f(x)]/h ->Chain Rule: (f.g)' = f' * g' ,Partial ∂f/∂x: Freeze y ->Unleash Multi-Dim Vector Vibes!∇f = [∂f/∂x, ∂f/∂y] Cooked fr!! #ML #DataScience #Calculus
Day 13: Log your grind. Share the gains. Prove your sweat counts. #100DaysRawGrind
Day 13: Log your grind. Share the gains. Prove your sweat counts. #100DaysRawGrind
Day 128 - Data Science Journey ->Backprop Bridge: Grads comp ,optimizers apply efficiently (w_new = w - α * ∂L/∂w). -> Loss Pairings: Syncs sweet with cross-entropy or MSE ->Gradient Descent: Compute ∂L/∂w from loss, w_new = w - α * grad. balance is key. #NeuralNetworks #ML
Day 12: Log your grind. Share the gains. Prove your sweat counts. #100DaysRawGrind
Day 12: Log your grind. Share the gains. Prove your sweat counts. #100DaysRawGrind
Day 127 -Data Science Journey: ->Backprop Chain Rule: ->Loss- Activation -Weights: Grad = Outer * Inner Derivs (Multiply Back) ->Cross-Entropy: -[p ln(q)+(1-p)ln(1-q)] : Smashes Misses ->Grad Magic: ŷ - y (Sigmoid/Softmax!) ->MSE/Hinge? #ML #NeuralNets #Backprop #DeepLearning
Day 11: Log your grind. Share the gains. Prove your sweat counts. #100DaysRawGrind
Day 11: Log your grind. Share the gains. Prove your sweat counts. #100DaysRawGrind
Day 126 – Data Science Journey Activation Functions decide neuron vibes ->Sigmoid: (0–1), probs, weak gradients ->Step: binary, zero learning ->Tanh: (-1–1), zero-centered, still shrinks ->ReLU: fast, default,can “die” ->Leaky ReLU: revives dead ReLU #100DaysOfCode #DeepLearning
Day 10: Log your grind. Share the gains. Prove your sweat counts. #100DaysRawGrind
Day 10: Log your grind. Share the gains. Prove your sweat counts. #100DaysRawGrind
Day 125 – Data Science Journey ->Weights & Bias control the entire flow (not random… they decide everything) ->manual calculations: weight calc + slope ->Broke down Activation Functions and how they shape the output #100DaysOfCode #DeepLearning #NeuralNetworks #MachineLearning
If you’re into UI/UX, this is worth bookmarking. "UI Layout Sand"– a minimal tool to design gradients, explore palettes & export CSS instantly. Check it out 👉 ui-layout-sand.vercel.app Also check @CodeAyushD for more projects like this.
If you’re into UI/UX, this is worth bookmarking. "UI Layout Sand"– a minimal tool to design gradients, explore palettes & export CSS instantly. Check it out 👉 ui-layout-sand.vercel.app Also check @CodeAyushD for more projects like this.
Day 9: Log your grind. Share the gains. Prove your sweat counts. #100DaysRawGrind
Day 9: Log your grind. Share the gains. Prove your sweat counts. #100DaysRawGrind
Guri @itsguri001
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