Sijan Bhandari

📍Gorkha🇳🇵 In the high mountains of #Chumling, #Ranagaun & #Dorjung, women masons walk two hours everyday to work.👩‍🏭🚧 Here, @WFP reconstructed 8.5 km of trails, benefiting 1,844 ppl w/improved livelihoods & easy access to markets, schools & health posts. Thank you, Germany🇩🇪.

@BHampson @DjangoConEurope Hi Ben, Happy to see you again. Thanks for connecting here. I am loving virtual conferences :)

@SijanOnly @DjangoConEurope Sijan, it seems we meet again, haha! Great to hear you're attending this conference too.

Starting my Django conference with the first talk "Programming for pleasure" :) @DjangoConEurope

Precision and Recall are confusing initially🤔 The TP, TF notations don't do a favor while trying to understand them intuitively. I never could remember them until I understood the concept. So let's talk about them in terms of picking apples and oranges🍎🍊, because why not! 👇

🚨 TICKET GIVEAWAY 🚨 Retweet this tweet for a chance to win a #djangocon ticket! ℹ️ DjangoCon Europe 2021 🌍 Online 🗓 2-6 June 2021 2021.djangocon.eu/about/tickets/ #ticketgiveaway #django #python #conference

Bayesian Inference can be summarized into 4 major steps: a. Calculate Likelihood b. Calculate/Collect prior c. Calculate posterior d. Inference. Which one represents the correct order ? #MachineLearning #DeepLearning #NeuralNetworks #ArtificialIntelligence #DataScience

Given the data 'D' and the model (parameter) 'θ', which of the following is called likelihood ? #MachineLearning #DeepLearning #NeuralNetworks #ArtificialIntelligence #DataScience #bayesianlearning #quiz

I have 5 copies of Machine Learning Bookcamp To win one: 🔸 Follow me 🔸 Retweet this tweet Winners selected randomly. Results announced on Wednesday ML Bookcamp - Learn machine learning by doing projects 🔗 bit.ly/mlbookcamp @ManningBooks

@haltakov Formulation : b= the distance between two cameras f= focal length of camera, d= disparity: D = Distance of point in real world, ->D = b*f/d 4/4

@haltakov 1. Both capture the images. 2. we will find the location of the front-car in both of the images. -> we need object detection here. 3. We match the object in both the images (stereo matching) 4. compute the disparity measure 5. use disparity to estimate the distance. 3/4

Machine Learning Interview Question #13 🤖🧠🧐 We talked about using CNNs to detect objects. Now imagine we have a self-driving car driving around. ❓ How can we estimate the distance to a detected object (for example a car)❓ Answer in the replies. Read the rules 👇

Python default arguments are evaluated ONCE when the function is defined. This can have unexpected side effects:

@haltakov While formulating this solution, I just figured out the flaw :). How to decide the patch size because objects may have different aspect ratios. 3/3

@haltakov So, in general, we can apply CNN with many patches of the given images and CNN should classify each patch as : either object or maybe background and each patch itself gives the bounding box of the object. 2/3

Machine Learning Interview Question #12 🤖🧠🧐 We talked about CNNs so let's use one of them now... ❓ How can you use a CNN to detect the presence and the location (bounding box) of a specific object in an image (for example car)?❓ Answer in the replies. Read the rules 👇

@chuston1776 @svpino At the same time, the loss function with smaller batch sizes will give unstable landscapes in each iteration. In this way, it is easier to escape those sharp minima. 4/4

@chuston1776 @svpino One intuition I have is - When it comes to using Large batch size, the shape of the loss function will remain consistent over the batches. This consistency is not good. Because this won't allow us to escape or so to say use other possible landscapes in the error surface. 3/4

Imagine you are training a neural network using mini-batch gradient descent, and the training accuracy starts oscillating. What's the most likely reason for this? 1. Imabalanced dataset 2. Batch size is too big 3. Learning rate is too high Make sure to explain your answer 👇