CNNs
Kaggle - Our first challenge: Paddy
Exploratory Data Analysis(EDA) for Paddy Disease Classification
Solutions exercise CNN
Presentation from the participants of the CNN assignment from Coursera
Walk-through
PyTorchLightning
PyTorch 303 (Lab 03)
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Go for your own through the Colab Notebook above (PyTorch303) and try to understand and repeat the steps for your own.
Watch the videos on the next page
Go through the following notebooks and complete the second one (assignment notebook):
Please register at kaggle.com and join the competition. Go through the Exploratory Data Analysis Notebook session and create your own EDA. Here is the link to the competiton:
The main objective of this Kaggle competition is to develop a machine or deep learning-based model to classify the given paddy leaf images accurately. A training dataset of 10,407 (75%) labeled images across ten classes (nine disease categories and normal leaf) is provided. Moreover, the competition host also provides additional metadata for each image, such as the paddy variety and age. Your task is to classify each paddy image in the given test dataset of 3,469 (25%) images into one of the nine disease categories or a normal leaf.
So that is where we will be heading in the next sessions, trying different tools and techniques to tackle this challenge.
Here again my EDA Notebook:
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Watch them all😊
Introduction to the Concept of Word Vectors
Words to Vectors
Example of Word Embeddings
Neural Model of Text
The Softmax Function
Methods for Learning Model Parameters
More Details on How to Learn Model Parameters
The Recurrent Neural Network
Long Short-Term Memory
Long Short-Term Memory Review
Use of LSTM for Text Synthesis
Simple and Effective Alternative Methods for Neural NLP
Done!