Explore tweets tagged as #30daysofpytorch
Day 9&10: #30daysofpytorch, #deeplearning Concepts studied: I.studied how to build a model for non- linear data, replicating its activation functions, building a multi-class classification model and reading further evaluation metrics. Completed Neural Networks in PyTorch.
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Day 16-18: #30daysofpytorch,.#deeplearning Concepts studied: Completed Chapter 4 ( PyTorch Custom Datasets), learned how to transform, load & augment data. Also studied TinyVGG , how to deal with underfitting & overfitting, predicting custom images.
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Day 11: #30daysofpytorch,.#deeplearning Concepts studied:.Started with a new chapter, PyTorch Computer Vision,by studying how to get the dataset, visualize data, prepare data (using torchvision.transforms), build baseline model, training and testing model on batches of data.
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Starting a challenge to master Pytorch for the next 30 days. Tracking my goal using my Goal Tracker Template in Notion. Each day, I'll share a sample of lesson notes from my daily studies. #deeplearning , #30daysofpytorch
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Day 19-21: #30daysofpytorch,.#deeplearning Concepts studied:Chapter 5 (PyTorch Going Modular). How to turn the most useful code in a notebook into a Python script. Why modular? Well, it's easier to run Larger scripts easier and more reproducible.
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Day 2: #30daysofpytorch, #deeplearning Concepts studied: basic operations, matrix multiplication, neural networks , change tensor datatype, reshaping, stacking, squeezing & unsqueezing.
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Day 4: #30daysofpytorch, #deeplearning Concepts studied: the first two steps of a Pytorch workflow fundamentals. Step 1: Data (preparing & loading). Step 2: Build model. For step 2, further goes into details of the necessary Pytorch model building essentials.
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Day 8: #30daysofpytorch, #deeplearning Concepts studied: Making predictions and evaluating the model and Improving the model by changing its hyperparameters to curb the underfitting problem.
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Day 5&6: #30daysofpytorch, #deeplearning Concepts studied: Training a PyTorch (training & test loop), Saving a PyTorch Model (recommended: state_dict( ) ) & Putting it all together ( showing how to build a model from creating data to evaluating a loaded model).
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Day 1: #30daysofpytorch , #deeplearning .Concepts studied: studied the fundamentals of PyTorch, Tensor introduction and creating sensors, data types, getting info and manipulating tensors. Link:
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Day 12-15: #30daysofpytorch, #deeplearning Studied how to make predictions, set up device, building non-linearity,building CNNs, training & testing using our training & test functions,make & evaluate best model,make & evaluate random predictions with best model &confusion matrix
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Day 7: #30daysofpytorch,.#deeplearning Concepts studied: Started a new chapter with PyTorch Neural Network Classification. Started off with Make Classification data, Building and Training model. Model was generated with scikit-learn & trained on a neural network
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Day 3: #30daysofpytorch, #deeplearning Concepts studied: Pytorch tensors & Numpy, Reproducibility (trying to take the random out of random). Completed Chapter 1 of the course, PyTorch Fundamentals.
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It's been long time, I haven't cooked any Deep learning recipe. For last 6 months and still, life is moving around .IVA application development and cloud terminologies. Just started Deep Learning with PyTorch @learnpytorch. #30DaysOfPyTorch.
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