prateek681

amazon-sagemaker-examples

Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.

applied-ml

📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.

best-of-ml-python

🏆 A ranked list of awesome machine learning Python libraries. Updated weekly.

C-Step-Counter

Portable C implementation of the algorithm

coding-interview-university

A complete computer science study plan to become a software engineer.

Context-Impacts-in-Hunan-Activity-Recognition

Context Impacts in Accelerometer-Based Walk Detection and Step Counting

Create-Your-Own-Image-Classifier

data-science-ipython-notebooks

Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.

Data-Science-Projects

Disaster-Response-Pipelines

prateek681/Data-Science-Projects

prateek681/Disaster-Response-Pipelines

prateek681/amazon-sagemaker-examples

Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.

prateek681/applied-ml

📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.

prateek681/best-of-ml-python

🏆 A ranked list of awesome machine learning Python libraries. Updated weekly.

prateek681/C-Step-Counter

Portable C implementation of the algorithm

prateek681/coding-interview-university

A complete computer science study plan to become a software engineer.

prateek681/Create-Your-Own-Image-Classifier

prateek681/data-science-ipython-notebooks

Data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe, Keras), scikit-learn, Kaggle, big data (Spark, Hadoop MapReduce, HDFS), matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines.

prateek681/DataSet

Data set used to tune the algorithm and validate it

prateek681/DeepLearningExamples

Deep Learning Examples

prateek681/dl-for-har

Official GitHub page of the note paper publication "Improving Deep Learning for HAR with shallow LSTMs" presented at the International Symposium on Wearable Computers 21' (ISWC 21')

prateek681/Finding-Donors

prateek681/hand-gesture-recognition-using-mediapipe

MediaPipe(Python版)を用いて手の姿勢推定を行い、検出したキーポイントを用いて、簡易なMLPでハンドサインとフィンガージェスチャーを認識するサンプルプログラムです。（Estimate hand pose using MediaPipe(Python version). This is a sample program that recognizes hand signs and finger gestures with a simple MLP using the detected key points.）

prateek681/hand_asl_recognition

ASL Recognition using Hand Landmarks

prateek681/handson-ml2

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.

prateek681/Identify-Customer-Segments

prateek681/Machine-Learning-with-Scikit-Learn-Python-3.x

In general, a learning problem considers a set of n samples of data and then tries to predict properties of unknown data. If each sample is more than a single number and, for instance, a multi-dimensional entry (aka multivariate data), it is said to have several attributes or features. Learning problems fall into a few categories: supervised learning, in which the data comes with additional attributes that we want to predict (Click here to go to the scikit-learn supervised learning page).This problem can be either: classification: samples belong to two or more classes and we want to learn from already labeled data how to predict the class of unlabeled data. An example of a classification problem would be handwritten digit recognition, in which the aim is to assign each input vector to one of a finite number of discrete categories. Another way to think of classification is as a discrete (as opposed to continuous) form of supervised learning where one has a limited number of categories and for each of the n samples provided, one is to try to label them with the correct category or class. regression: if the desired output consists of one or more continuous variables, then the task is called regression. An example of a regression problem would be the prediction of the length of a salmon as a function of its age and weight. unsupervised learning, in which the training data consists of a set of input vectors x without any corresponding target values. The goal in such problems may be to discover groups of similar examples within the data, where it is called clustering, or to determine the distribution of data within the input space, known as density estimation, or to project the data from a high-dimensional space down to two or three dimensions for the purpose of visualization (Click here to go to the Scikit-Learn unsupervised learning page).

prateek681/MachineLearningBookCamp

Machine Learning Bookcamp Course Notebooks

prateek681/mediapipe

Cross-platform, customizable ML solutions for live and streaming media.

prateek681/mlbookcamp-code

The code from the Machine Learning Bookcamp book and a free course based on the book

prateek681/object-detection-in-an-urban-environment

prateek681/Painter

prateek681/prateek681

prateek681/py

Repository to store sample python programs for python learning

prateek681/Real-time-GesRec

Real-time Hand Gesture Recognition with PyTorch on EgoGesture, NvGesture, Jester, Kinetics and UCF101

prateek681/reinforcement-learning

Implementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow. Exercises and Solutions to accompany Sutton's Book and David Silver's course.

prateek681/reinforcement-learning-an-introduction

Python Implementation of Reinforcement Learning: An Introduction

prateek681/tech-portfolio

A repository you can use to template your technical portfolio

prateek681/Yolo-to-COCO-format-converter

Yolo to COCO annotation format converter