Okay PyTorch New Tweets Update: Can't do more that 10 tweets in one sitting. We will be looking into matrix in the upcoming Tweets. #buildinpublic#PyTorch#Python#DeepLearning
Vector Dimension: ndim can be used for vectors as well. The easiest way to figure out the dimensions of a tensor having vector data is the check the number square brackets [ ]
Vectors: We have dealed with scalars already now lets observe vector. Lets create one first and then we will learn most of its other properties. This is a simple vector will dive into the multiverse of it soon
What if we want the original value of the tensor we created. item() - Upon using this with a tensor it will give us the original items inside the tensor rather that returning the tensor itself.
Dimension of a Scalar ndim: It allows us to see the dimensions of a scalar. We are not into serious stuff here. We will see tensors will dimensions eventually.
I think this is pretty self explanatory. Import Torch to get all of it functtionalities. In future we will be looking to create a set of tensors and how to manipulate them The version of torch i have used in 2.0.1 #buildinpublic#PyTorch#Python#DeepLearning
Start with the installtion of all these essential libraries. Most of them are quite familiar if you have done ML before 'torch' and 'torchvision' are new here.
Use a very flexible IDE or workspace. Google Colab or Jupyter Notebook is my most recommened one. While studying Pytorch I have used Jupyter Notebook in my case. Should Look something like this. #buildinpublic#PyTorch#Python#DeepLearning
- RNNs, text generation w/ TensorFlow & Keras - Anki ML Q: Cross-validation methods - Data preprocessing & feature engineering on Titanic dataset, visualization, unit testing
Took part in a machine learning hackathon and made a tumor detection system with accuracy of 100% in training set and 95% in validation and test set it was a supervised learning model with a data set of 1300 #buildinpublic#ml#ai#DeepLearning#machinelearning#progress#Python