Top Tweets for #60daysofmachinelearning
Day 19 of my #DeepLearning journey.
Learnt about Data augmentation and various pretrained model architectures (AlexNet, VGGNet, GoogleNet, ResNet)
#AI #ds #100DaysOfCode #60DaysOfMachineLearning #MachineLearning #WomenInTech
Day 18 of my #DeepLearning journey.
Implemented Dogs-vs-Cats Image Classification using CNN with fine-tuning (Reduced overfitting by a great deal). Increased Validation accuracy from 0.76 to 0.83.
#AI #100DaysOfCode #60DaysOfMachineLearning #Python #MachineLearning
Day 17 of my #DeepLearning journey!
Learnt about similarities and differences in ANN and CNN
Backpropogation in CNN: Crazy MathπΆβπ«οΈ
#AI #100DaysOfCode #60DaysOfMachineLearning #MachineLearning #Python
Day 16 of my #DeepLearning journey.
->CNN architecture basics
->Padding, stride, pooling
->Variants of convolutional operation
->Implemented the classic LeNet-5 architecture in keras
#AI #MachineLearning #100DaysOfCode #60DaysOfMachineLearning #pythonhub #Python #LearnInPublic
Day 15 of my #DeepLearning journey π§
Got into CNNs today! Learned how convolutional operations work and how theyβre inspired by the visual cortex.
Starting to see why CNNs are so powerful for images π
#60DaysOfMachineLearning #AI #100DaysOfCode #pythonhub #python
Day 14 of my #DeepLearning journey.β
Explored Newtonβs method, conjugate gradients & BFGS βapproximate second-order methods.
Also read a research paper on how dropout helps with generalization: really helps understand how it all works!
#100DaysOfCode #AI #60DaysOfMachineLearning
@DanKornas Great progress, Dan! Deep learningβs ability to uncover complex patterns is what makes it so powerful in fields like image recognition and natural language processing. Excited to see where you go in the final stretch of #60DaysOfMachineLearning! πͺ
Day 60 of #60daysOfMachineLearning
π· Model Serving π·
Model serving refers to the process of deploying a trained machine learning model in a production environment and using it to make predictions on new data.
Day 59 of #60daysOfMachineLearning
π· Percision, Recall, F1 π·
Precision is a measure of the accuracy of the model's positive predictions. It is calculated as the number of true positive predictions divided by the total number of positive predictions made by the model.
Day 58 of #60daysOfMachineLearning
π· Accuracy, Overfitting, Underfitting π·
In machine learning, accuracy is a measure of how well the model is able to make predictions on new examples. It is usually measured as the percentage of correct predictions made by the model.
Day 57 of #60daysOfMachineLearning
π· Training, validation, test data π·
In machine learning, it is important to divide your data into three sets: training, validation, and test.
𧡠π
Day 57 of #60daysOfMachineLearning
π· Training, validation, test data π·
In machine learning, it is important to divide your data into three sets: training, validation, and test.
𧡠π
Day 56 of #60daysOfMachineLearning
π· Long Short-Term Memory Neural Networks π·
Long Short-Term Memory (LSTM) networks are a type of artificial neural network that is specifically designed to process sequential data, such as time series or natural language.
Day 55 of #60daysOfMachineLearning
π· Convolutional Neural Networks π·
Convolutional Neural Networks (CNNs) are a type of artificial neural network that is specifically designed to process data with a grid-like topology, such as images.
Day 55 of #60daysOfMachineLearning
π· Convolutional Neural Networks π·
Convolutional Neural Networks (CNNs) are a type of artificial neural network that is specifically designed to process data with a grid-like topology, such as images.
Day 54 of #60daysOfMachineLearning
π· Feed Forward Neural Networks π·
A feedforward neural network is a type of neural network that consists of multiple layers of interconnected neurons that process and transform the input data.
Day 52 of #60daysOfMachineLearning
π· Deep Learning π·
Deep learning is a type of machine learning algorithm that uses deep neural networks to learn complex patterns and relationships in data.
Day 53 of #60daysOfMachineLearning
π· Neural Networks π·
A neural network is a computational model that is inspired by the structure and function of the brain.
𧡠π
Day 51 of #60daysOfMachineLearning
π· Ensemble Learning π·
Ensemble learning is a machine learning technique that combines multiple models to improve the performance and robustness of the final model.
Day 50 of #60daysOfMachineLearning
π· Q-Learning π·
Q-learning is a popular and effective reinforcement learning algorithm for solving Markov Decision Processes (MDPs).
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