Tensorflow Tutorial Tamil | Tensorflow in Tamil for beginners #10

Опубликовано: 27 Май 2026
на канале: Sivanu Jan
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In this Tensorflow tutorial Tamil, I explained indexing and slicing in 1D and 2D tensors in a simple step-by-step way. Understanding indexing is very important because without it you cannot properly access or manipulate your data inside tensors.

🔥If you want to master Tensorflow step by step in Tamil, check out my complete Tensorflow playlist. start from Episode 1 and grow your skills like a pro! :    • Tensorflow Tutorial for beginners in Tamil...  

🔥If you want to master Numpy step by step in Tamil, check out my complete Numpy playlist. start from Episode 1 and grow your skills like a pro! :    • Complete Numpy Tutorial in Tamil | Numpy T...  

🔥If you want to master Pandas step by step in Tamil, check out my complete Pandas playlist. start from Episode 1 and grow your skills like a pro! :    • Pandas Full Course Python | Python Pandas ...  

✍In this video, you will learn:

00:00 Introduction
00:29 Create 1D Tensor for Indexing
03:39 Slicing in 1D Tensor
06:40 Create 2D Tensor for Indexing
08:42 Indexing in 2D Tensor
13:11 Slicing in 2D Tensor
15:43 Examples: 2D Tensor Indexing & Slicing
20:38 Next Video

👉What is indexing in a 1D tensor (how to access single elements).

👉How to use negative indexing to access elements from the end.

👉What is slicing in a 1D tensor (extracting a range of values).

👉How to use step values in slicing (e.g., skipping elements).

👉Indexing in 2D tensors (rows & columns selection).

👉How to extract entire rows, specific columns, and sub-matrices.

👉The difference between 1D vs 2D indexing.

👉Similarities between TensorFlow slicing vs Python lists & Numpy arrays.

✍By the end of this tutorial, you will clearly understand how to access, slice, and reshape data in Tensorflow tensors, which is a must-have skill for machine learning and deep learning projects.

🔥 This tutorial is beginner-friendly and explained in Tamil for easy understanding.

#Tensorflow #Tamil #DeepLearning #MachineLearning #DataScience

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