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holbertonschool-machine_learning's Introduction

Machine Learning πŸŽ“

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β€œNobody phrases it this way, but I think that artificial intelligence is almost a humanities discipline. It's really an attempt to understand human intelligence and human cognition.” β€”Sebastian Thrun

Neural Network

About me 🌐

Economist who likes coding and do Machine Learning stuff. I love the sea, reading books and listen to music.

Twitter: @Kenneth_ca7 LinkedIn: Kenneth Cortes

Math πŸ“

Mathematical and Statistical foundations

Topics
Linear Algebra
Matplotlib
Calculus
Convolution and Pooling
Probability
Multivariable Probability
Bayesian Probability

Pipelines πŸ’½

Automating data extraction and processing

Topics
Pandas
APIs

Reinforced Learning 🏫

Topics
Q Learning

Supervised Learning πŸ“•

Topics
Tensorflow & Keras
Binary Classification
Multiclass Classification
Optimization
Error Analysis
Regularization
Convolutional Neural Networks (CNN)
Deep CNN
Transfer Learning
Object Detection
Recurrent Neural Network (RNN)
Time Series
Word Embeddings
Natural Language Processing metrics
Attention
Transformers APPs

Unsupervised Learning πŸ“–

Topics
Dimensionality Reduction
Clustering
HMM
Hyperparameter Tuning
Autoencoders

holbertonschool-machine_learning's People

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holbertonschool-machine_learning's Issues

Expection size is False

Hi, Kenneth. Your working is very helpfull. I appreaciated you. while I reviewing your code , I faced error. This error is occured in code snippet below;

input_t = K.Input(shape=(32, 32, 3)).

This code means model need input which has size (32x32x3). But Resnet expect input which has size (224, 224, 3). If we ran model you built, we'll faced error like this;

"ValueError: Input 0 is incompatible with layer resnet50: expected shape=(None, 32, 32, 3), found shape=(None, 224, 224, 3)".

Solution:
You should change code such as below;
input_t = K.Input(shape=(224, 224, 3))

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