Скачать или смотреть Complete Neural Networks Masterclass – From Neuron to Backpropagation | VGI Skill Lab

🚀 Complete Neural Networks & Deep Learning Foundation in One Shot | From Zero to Backpropagation (With TensorFlow & PyTorch Code)

Not for absolute beginners – this is the fastest, most structured, and industry-focused Master Class + deep-dive into Neural Networks for people who already know Python and basic Machine Learning.

Perfect for:
• Data Scientists / ML Engineers preparing for Deep Learning interviews or real projects.
• Working professionals who need to quickly master Neural Networks before starting CNNs/RNNs/Transformers.
• Anyone who understands traditional ML but wants crystal-clear intuition + code for Neural Nets.

Topics Covered :

00:00 Introduction to Neural Network
02:28 ⁠What is Neural Network , How does it work ?
35:58 What is Back-Propagation , Chaining & Unchaining , Forward pass , Backward pass ?
45:27 Types of Activation Functions ? When to use while Training ?
56:02 What is Drop-out and Batch - Normalisation ?
1:02:00 Types of Optimisers and Loss function in Neural Network
1:11:04 Implementation of the Entire Neural Network ( Let’s Construct the NNets using Tensor flow )

Total duration: ~1.5 hours of pure value (watch at 1.5x–2x if you’re revising)

What you’ll master (with full code implementation in TensorFlow 2 + PyTorch):
1. Course Introduction & Biological vs Artificial Neuron
2. What exactly is a Neural Network? How does it really work under the hood? (Lecture 1 & 2)
3. Forward Pass, Backward Pass, Chain Rule, Computational Graphs
→ Complete intuitive + mathematical explanation of Backpropagation (Lecture 3)
4. All Activation Functions explained (ReLU, Leaky ReLU, Swish, GELU, Sigmoid, Tanh etc.)
→ When to use which one during training (Lecture 4)
5. Dropout, Batch Normalization, Layer Normalization – why, Gradient Flow issues & fixes (Lecture 4.1)
6. All important Optimizers (SGD, Momentum, RMSprop, Adam, AdamW, etc.)
→ All major Loss Functions (Cross-Entropy, MSE, Focal Loss, etc.) (Lecture 5)

Everything explained with:
• Clean visuals & animations
• Step-by-step math → code translation
• Both TensorFlow/Keras and PyTorch implementations
• Tips & tricks actually used in industry & research

Visit Website for More Detailed Courses :
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