Technical Fridays | Blog
Personal notes on machine learning, deep learning, and software engineering — what's Technical Fridays?
- Personal
- Data Science
- Machine Learning
- R
- Python
- Algorithms
- Cryptography
- Mathematics
- Visualization
- Deep Learning
- Computer Vision
- Natural Language Processing
- Generative AI
- Speech Recognition
- PyTorch
- LLM
- CUDA
- Optimization
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Data ScienceDon't use sigmoid: Neural Nets
In neural networks, activation functions are used to introduce non-linearity in the model. There are several activation functions to choose from. Traditionally, people have been using sigmoid as...
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Deep LearningThe magic behind ConvNets
Ordinary neural networks consists of neurons that have learnable weights and biases. The input is a single vector (of features) that is transfomed through a number of hidden...
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PersonalSmart India Hackathon 2018 grand finale
Recently, I attended Smart India Hackathon grand finale 20181 at Pune, India. We’re one of the few teams selected for the finale. Our idea, which was under CSIR2,...
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Data ScienceScaling vs Normalization
Feature scaling (also known as data normalization) is the method used to standardize the range of features of data. Since, the range of values of data may vary...
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Data ScienceEnsembling is the key
Most of us have our favourite machine learning algorithms. For some, it may be state-of-the-art algos like Support Vector Machines while for others it may be something simple...
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Data Science
Computational graphs: Backpropagation
Backpropagation is an efficient method of computing gradients in directed graphs of computations, such as neural networks. This is not a learning method, but rather a nice computational...
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Data ScienceGradient descent: The core of neural networks
As discussed in the post linear algebra and deep learning, the optimization is the third and last step in solving image classification problem in deep learning. It helps...
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Data ScienceGradient boosted trees: Better than random forest?
Does gradient boosted trees generally perform better than random forest? Let’s see that. But, first what are these methods? Random forest and boosting are ensemble methods, proved to...
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Data ScienceLinear algebra: The essence behind deep learning
Mathematics lies behind every algorithm; if not mathematics then mathematical thinking. In case of deep learning algorithms, linear algebra is the driving force.
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Data ScienceData Mining: Knowledge discovery in databases
Knowledge discovery in databases (KDD) is a 7 step process to search for hidden knowledge in data. Data Mining refers to the analysis step in the KDD process....