All Projects → llSourcell → Classifying_data_using_a_support_vector_machine

llSourcell / Classifying_data_using_a_support_vector_machine

This is the code for the "Classifying Data using Gradient Descent" by Siraj Raval on Youtube

Projects that are alternatives of or similar to Classifying data using a support vector machine

Ddad
Dense Depth for Autonomous Driving (DDAD) dataset.
Stars: ✭ 204 (+0%)
Mutual labels:  jupyter-notebook
Mosquito
Trading Bot with focus on Evolutionary Algorithms and Machine Learning
Stars: ✭ 201 (-1.47%)
Mutual labels:  jupyter-notebook
Multihopkg
Multi-hop knowledge graph reasoning learned via policy gradient with reward shaping and action dropout
Stars: ✭ 202 (-0.98%)
Mutual labels:  jupyter-notebook
Hivemq Mqtt Tensorflow Kafka Realtime Iot Machine Learning Training Inference
Real Time Big Data / IoT Machine Learning (Model Training and Inference) with HiveMQ (MQTT), TensorFlow IO and Apache Kafka - no additional data store like S3, HDFS or Spark required
Stars: ✭ 204 (+0%)
Mutual labels:  jupyter-notebook
Machine Learning For Finance
Machine Learning for finance and investment introduction
Stars: ✭ 204 (+0%)
Mutual labels:  jupyter-notebook
Emoji2vec
emoji2vec: Learning Emoji Representations from their Description
Stars: ✭ 204 (+0%)
Mutual labels:  jupyter-notebook
Coursera
These are my learning exercices from Coursera
Stars: ✭ 203 (-0.49%)
Mutual labels:  jupyter-notebook
Yolo Digit Detector
Implemented digit detector in natural scene using resnet50 and Yolo-v2. I used SVHN as the training set, and implemented it using tensorflow and keras.
Stars: ✭ 205 (+0.49%)
Mutual labels:  jupyter-notebook
Aind Nlp
Coding exercises for the Natural Language Processing concentration, part of Udacity's AIND program.
Stars: ✭ 202 (-0.98%)
Mutual labels:  jupyter-notebook
Unsupervisedscalablerepresentationlearningtimeseries
Unsupervised Scalable Representation Learning for Multivariate Time Series: Experiments
Stars: ✭ 205 (+0.49%)
Mutual labels:  jupyter-notebook
Tensorflow2 Deep Reinforcement Learning
Code accompanying the blog post "Deep Reinforcement Learning with TensorFlow 2.1"
Stars: ✭ 204 (+0%)
Mutual labels:  jupyter-notebook
X2face
Pytorch code for ECCV 2018 paper
Stars: ✭ 204 (+0%)
Mutual labels:  jupyter-notebook
Udemy Machine Learning
Notebooks for Course
Stars: ✭ 204 (+0%)
Mutual labels:  jupyter-notebook
Pytorch graph Rel
A PyTorch implementation of GraphRel
Stars: ✭ 204 (+0%)
Mutual labels:  jupyter-notebook
Pytorch Vq Vae
PyTorch implementation of VQ-VAE by Aäron van den Oord et al.
Stars: ✭ 204 (+0%)
Mutual labels:  jupyter-notebook
Clandmark
Open Source Landmarking Library
Stars: ✭ 204 (+0%)
Mutual labels:  jupyter-notebook
Raspberrypi Facedetection Mtcnn Caffe With Motion
MTCNN with Motion Detection, on Raspberry Pi with Love
Stars: ✭ 204 (+0%)
Mutual labels:  jupyter-notebook
Pytorch realtime multi Person pose estimation
Pytorch version of Realtime Multi-Person Pose Estimation project
Stars: ✭ 205 (+0.49%)
Mutual labels:  jupyter-notebook
Spacy Ru
Russian language models for spaCy
Stars: ✭ 205 (+0.49%)
Mutual labels:  jupyter-notebook
Python For Data Science
A collection of Jupyter Notebooks for learning Python for Data Science.
Stars: ✭ 205 (+0.49%)
Mutual labels:  jupyter-notebook

Classifying_Data_Using_a_Support_Vector_Machine

This is the code for the "Classifying Data using a Support Vector Machine" by Siraj Raval on Youtube

Overview

This is the code for this video on Youtube by Siraj Raval on how to use a Support Vector Machine to classify some data. Basically, we create an n-1 dimensional hyperplane that linearly seperates a set of classes in n-dimensional space. Awesome AF. This model is able to tell what category something is of, be that text or numbers or videos or images.

Dependencies

  • numpy
  • matplotlib

Usage

Just run jupyter notebook in terminal and it will run in your browser.

Install Jupyter here i've you haven't.

Credits

The credits for this code go to maviccprp. ive merely created a wrapper to get people started.

Note that the project description data, including the texts, logos, images, and/or trademarks, for each open source project belongs to its rightful owner. If you wish to add or remove any projects, please contact us at [email protected].