RT @tunguz: Men would rather move to high dimensional vector spaces than go to therapy.

RT @sesigl: I interviewed over 70 backend engineers in the last 2 years. I created a Notion based "Learn Backend Development Pack V4" that….

I asked GPT-4 to demystify the process of a Variational Autoencoder (VAE) handling the MNIST problem. It illustrated how 784-pixel images of handwritten digits are distilled down into a 2D latent space. #GPT4 #VAE #MNIST #AIVisualization 🎨🤖🔢

RT @DeepMind: We’re proud to announce that DeepMind and the Brain team from @Google Research will become a new unit: 𝗚𝗼𝗼𝗴𝗹𝗲 𝗗𝗲𝗲𝗽𝗠𝗶𝗻𝗱. Toge….

Hey, Tech enthusiasts!.Are you interested to learn more about Azure Machine learning?.Event Details:.••Date: 25th April, 2023.•Time: 8.00 PM – 9.00 PM (GMT + 5).Registration Link:

RT @rasbt: Do duplicates in the training set matter in classification?. Nice blog post giving away one of my exam q's!. TLDR.Nonparametric….

RT @_akhaliq: GPT-4 Technical Report. pdf: blog:

What are the use cases of AI & ML in Astronomy and Space Sciences? #MachineLearning #ComputerVision #naturallanguageprocessing #AI #DataScience #SpaceSciences #astronomy.

In summary, overfitting is a common problem in machine learning but can be overcome using regularization, early stopping, data augmentation, ensemble methods, and monitoring performance on new data. #MachineLearning #Overfitting #Regularization #DataAugmentation #EnsembleMethods.

5/ Finally, it's important to monitor the performance of your model on new, unseen data to ensure it's not overfitting. You can do this by using a separate validation set or by using techniques such as cross-validation.

4/ Ensemble methods such as bagging or boosting can also help overcome overfitting by combining the predictions of multiple models trained on different subsets of the data.

3/ You can also use data augmentation techniques such as random cropping or flipping to increase the amount of training data and reduce overfitting.

2/ Another approach is to use early stopping, where you stop training the model when the validation loss stops improving. This prevents the model from overfitting to the training data and gives better generalization performance.

1/ To overcome overfitting, one approach is to use regularization techniques such as L1 or L2 regularization, which add a penalty term to the loss function to discourage the model from becoming too complex.

Overfitting is a common problem in machine learning where a model becomes too complex and starts fitting the noise in the training data instead of the underlying pattern. This can lead to poor performance on new, unseen data.

Finally got my hacktoberfest swags 😍🥳 @hacktoberfest @Docker @appwrite @rapidapi @github #Opensource

On day 12 of the #15DaysOfSystemDesign journey on @algo_expert and I learned about polling and streaming. These techniques are crucial for real-time communication and data processing in modern systems. Took a break yesterday. Now continuing my journey again.#SystemDesign.

Day 11 of my #15DaysOfSystemDesign journey on @algo_expert and I learned about peer-to-peer networks on AlgoExpert. These networks allow for decentralized communication and file sharing, making them an important part of modern communication systems.#AlgoExpert.

On day 10 of my #15DaysOfSystemDesign journey on @algo_expert I learned about leader election and consensus algorithms and I learned about Paxos, Raft, and more, and how they're used to ensure system consistency and reliability. #SystemDesign

Day 9 of my #15DaysOfSystemDesign journey and I learned about replication and sharding on @algo_expert . These techniques are used to improve system performance and ensure high availability by distributing data across multiple servers. #Algoexpert #SystemDesign