PyTorch for Machin Learning & Deep Learning

01_pytorch_fundamentals.ipynb⏬
1️⃣ CPU (Central Processing Unit) & GPU (Graphics Processing Unit)
2️⃣ N-d Tensor
3️⃣ Tensor datatypes
4️⃣ Getting information from tensors
5️⃣ Math Operations:
6️⃣ Special Arrays
7️⃣ Random Arrays
8️⃣ Indexing & Slicing
9️⃣ Unsqueeze & squeeze
🔟 PyTorch tensors & NumPy

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02_data_preprocessing.ipynb⏬
1️⃣ Dataset
2️⃣ Load data
3️⃣ Data Cleaning
4️⃣ Encoding Categorical Variables
5️⃣ Imbalance Data
6️⃣ Feature Extraction
7️⃣ Feature Selection
8️⃣ Split data into training, Validation and test sets
9️⃣ Scaling/Normalization