Top Tweets for #100daysofdeeplearning
Game Theory isn't just about poker or chess—it's the mathematical framework behind AI decision making.
Day 22/100 ⬡ #100DaysOfDeepLearning
▪ Topic: Intro to Game Theory
#GameTheory #AI #MachineLearning #100DaysOfCode #BuildInPublic
I started blogging. and I tried to understand how Bitcoin actually works.
Day 11 of #100DaysOfDeepLearning: launched my Medium blog + went deep on how Bitcoin actually works via @3blue1brown.
#Bitcoin #Blockchain #Cryptography #AI #BuildInPublic
Most security tools just alert you. Mine fights back.
Days 9-10 of #100DaysOfDeepLearning went into building CyberGAN: an AI-Powered Autonomous Cybersecurity Agent
🔵 Blue Agent: PPO (ELO 1372)
🔴 Red Agent: PPO (ELO 628)
GitHub: https://t.co/nqzSu3geLW
#ML #CyberSecurity #AI
10,000 BTC for two pizzas.
Day 7/100 of #100DaysOfDeepLearning
🍕 Bitcoin Pizza Day with @bitshala_org — Vadodara
Best insight: Bitcoin's Proof of Work is adversarial by design.
Sometimes the best ideas come from pizza, not papers 🍕🧬
#BitcoinPizzaDay #Bitcoin #BuildInPublic
I've been mass learning Deep Learning for 7 days straight.
Built a neural net from scratch. Studied transformers. Shipped an open-source feature. Read 10+ research papers. And started building something that doesn't exist yet.
Here's the recap 🧵
#100DaysOfDeepLearning
Day 30 of #100DaysOfDeepLearning: Learned GRUs (Gated Recurrent Units)! 🧠🤖 These powerful neural networks help tackle the vanishing gradient problem in RNNs, making them great for sequential data. Excited to see where this knowledge takes me! #DeepLearning #AI #MachineLearning
Day 27 of #100DaysOfDeepLearning Performed Sentimental Analysis on Customer Churn Data Set using RNN! #AI #RNN #Sentimental
Day 25 ans 26 of #100DaysOfDeepLearning
Learned about different types of RNNs, explored backpropagation through time in RNNs! 🧠 Learned how BPTT helps in training RNNs by unfolding them over time and updating weights based on error gradients. #AI #RNN
Day 24 of #100DaysOfDeepLearning: Delved into Recurrent Neural Networks (RNNs) today! 🌟 Learned how they process sequential data and their applications in time series forecasting, language modeling, and more. I'm excited to explore further! #DeepLearning #AI #MachineLearning
Day 22& 23 of #100DaysOfDeepLearning: In these two days, I learned Data Augmentation Enhancing dataset with techniques like rotation and flipping. And also Learned about Pretrained Models! Models like VGG16 and ResNet. #DeepLearning #AI #MachineLearning #ResNet
Day 21 of #100DaysOfDeepLearning: Today, I explored backpropagation in Convolutional Neural Networks (CNNs). Understanding how errors are minimized and weights are updated is fascinating! 🧠🔄 #DeepLearning #AI #MachineLearning
Day 19 of #100DaysOfDeepLearning: Today, I dove into the world of pooling layers in CNNs! 🌊 Learned about max pooling and average pooling, crucial for reducing spatial dimensions and improving computational efficiency. I am excited to apply this knowledge! 💡 #DeepLearning #AI
Day 18 of #100DaysOfDeepLearning: Learned about strides and padding in CNNs today. These concepts are crucial for controlling the spatial dimensions of the output. 📐🔍
#MachineLearning #AI #deeplearning
Day 16 of #100DaysOfDeepLearning: Started diving into Convolutional Neural Networks (CNNs) today! I am excited to explore how these models revolutionize image recognition and more. 📷🧠
#MachineLearning #AI #DeepLearning #computervision
Day 15 of #100DaysOfDeepLearning: Delved into Hyperparameter Tuning! 🎛️🔧 Fine-tuning hyperparameters can drastically improve model performance. Exploring techniques like Grid Search, Random Search, and Bayesian Optimization. Exciting stuff! #DataScience #MachineLearning #AI
Day 14 of #100DaysOfDeepLearning: Today, I dove into optimizers! 🧠🚀 Learned how algorithms like SGD, Adam, and RMSprop improve neural network training by adjusting learning rates and minimizing loss. Crucial for efficient model training! #DataScience #MachineLearning #AI
Day 13 of #100DaysOfDeepLearning: Just learned about batch normalization! 🧠🔍 This technique normalizes input layers, speeding up training and improving stability. It’s a game-changer for deep neural networks! #DataScience #MachineLearning #AI
Day 12 of #100DaysOfDeepLearning: Today, I learned about the Exponential Weighted Moving Average! 🧠📊 It's a powerful technique to smooth out data and highlight trends, giving more weight to recent observations. Perfect for time series analysis! #DataScience #MachineLearning
Day 11 of #100DaysOfDeepLearning: learned about batch normalization! This technique helps speed up training and improves model stability by normalizing the inputs of each layer. I'm excited to apply this to my neural networks!
#DeepLearning #AI #MachineLearning #100DaysOfCode
Day 10 of #100DaysOfDeepLearning: Today, I dived into weight initialization techniques! It is good to know how proper initialization can improve model training speed and performance. Learned about Xavier weight initialization technique. #DeepLearning #AI #MachineLearning
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