Pytorch framework & Tensorflow Framework

PyTorch and TensorFlow are two of the most popular deep learning frameworks. 

                                                               

PyTorch (Most popular research DL framework*)

• PyTorch was developed by Facebook (Meta) using Python and was released to the public in 2016. Now open source & used by companies such as Tesla, Microsoft, and OpenAI.
• PyTorch is a low-level API for NLP (natural language processing) and computer vision. It is a more powerful version of Numpy.
• PyTorch is used by leading tech companies like 
• Tesla to build the computer vision system for their self-driving vehicles
• Meta to power the curation and understanding systems for their timelines.
• Apple to create computationally enhanced photography and even automated weed-killing tractors.

• What deep learning is good for

• Problems with long lists of rules — when the traditional approach fails, machine learning/deep learning may help.

• Continually changing environments — deep learning can adapt (‘learn’) to new scenarios.

• Discovering insights within large collections of data — can you imagine trying to hand‑craft rules for what 101 different kinds of food look like?

• Category	Algorithms
  Machine Learning	Random forest; Gradient boosted models; Naive Bayes; Nearest neighbour; Support vector machine; …many more
  Deep Learning	Neural networks; Fully connected neural network; Convolutional neural network; Recurrent neural network; Transformer; …many more

Tensor Explanation from root level

 9    ---> (single number)                            ---> scalar   (0 D Tensor)  (D means Dimensional)

[19, 82] (group of numbers --> Row)        ---> Vector (1D Tensor)

[[9 ,88 ] ,

 [19 ,8]]   (combination of rows & colums) --> Matrix ((2 D Tensor))

3X3  Matrix (Red, Green, and Blue)           ---> 3D Tensor (Ex: Image)

4D or 5D tensor (Videos or large data)

• Images are first converted into numerical values called tensors. 
• These tensors are given to a neural network, which processes them using mathematical calculations and generates an output tensor. 
• This output is then transformed into meaningful information that humans can easily understand.

 

TensorFlow was developed by Google and released to the public in 2015. Production and research are the main uses of TensorFlow.