300Days__MachineLearningDeepLearning and 66Days_MachineLearning
These are **competitors** — both are personal learning journey repositories documenting ML/DL fundamentals over a fixed timeframe, offering similar structured educational content at different intensity levels (300 days vs. 66 days), so a learner would typically choose one based on their available time commitment rather than use them together.
About 300Days__MachineLearningDeepLearning
ThinamXx/300Days__MachineLearningDeepLearning
I am sharing my Journey of 300DaysOfData in Machine Learning and Deep Learning.
Covers 40+ implementation notebooks spanning CNNs, RNNs, GANs, attention mechanisms, and transformer architectures using PyTorch and Fastai. Projects progress from foundational algorithms (logistic regression, LeNet) through advanced applications including sentiment analysis, natural language inference with BERT, medical imaging classification, and collaborative filtering. Structured around canonical textbooks (Hands-On ML, Deep Learning with PyTorch, Dive into Deep Learning) with systematic coverage of computer vision, NLP, and neural network theory alongside practical evaluation metrics and data preprocessing techniques.
About 66Days_MachineLearning
regmi-saugat/66Days_MachineLearning
I am sharing my journey of 66DaysOfData in Machine Learning
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