shubham0204

Certifications

Amazon Web Services

Badges - Getting Started

• Intro to Cloud Computing 101
• AWS EC2
• AWS Storage / S3
• AWS VPC

shubham0204

Tutorials: Google Colab Notebooks

• Converting scikit-learn models to ONNX for Android
• Age + Gender Estimation in Android with TensorFlow
• Hyperparameter Optimization Using Keras Tuner API
• Image Colorization With GANs
• Chatbot using Seq2Seq LSTM models.
• [Neural Machine Translation (NMT) - Translating English sentences to French sentences](https://colab.research.google.com/github/shubham0204/Google_Colab_Notebooks/blob/main/Neural_Machine_Tra

shubham0204

• On-Device Machine Learning In Android: Frameworks and Ecosystem
• Using C/C++ in Android: A Comprehensive Guide For Beginners
• Chaquopy: Using Python In Android Apps
• Deploying Scikit-Learn Models In Android Apps With ONNX ( GitHub )
• Deploying TF models on Heroku for Android ( GitHub )
• Realtime Depth Estimation In Android ( [GitHub](https://github.com/shubham0204/Realtime_MiDaS

shubham0204

Demystifying Mathematics - Shubham Panchal

• Kernels: Everything You Need to Know
• Principal Component Analysis: Everything You Need To Know
• Demystifying Monte Carlo Integration
• Demystifying Linear Independence
• Demystifying Probability Distributions ( 1 / 3 )
• Demystifying Probability Distributions ( 2 / 3 )
• [Demystifying Probability Distributions ( 3 / 3 )](https://www.canto

shubham0204

Blogs On Medium

• On-Device Machine Learning In Android: Frameworks and Ecosystem
• Using C/C++ in Android: A Comprehensive Guide For Beginners
• Building A Cross-Platform TFIDF Text Summarizer In Rust
• Managing Deep Learning Models Easily With TOML Configurations
• glove-android: Using GloVe Word Embeddings for NLP In Android
• Chaquopy: Using Python In Android Apps
• [Using Websockets To Run Termin

shubham0204

import streamlit as st
import cv2

vid = cv2.VideoCapture( 'http://<network_ip_Address>:8080/video' )

st.title( 'Using Mobile Camera with Streamlit' )
frame_window = st.image( [] )
take_picture_button = st.button( 'Take Picture' )

while True:

shubham0204

import streamlit as st
import cv2
import numpy as np
import requests

st.title( 'Mobile Camera Preview in Streamlit' )
frame_window = st.image( [] )
take_picture_button = st.button( 'Take Picture' )

while True:

shubham0204

class Classifier( private var positiveBagOfWords : Array<String> , private var negativeBagOfWords : Array<String>) {

    companion object {

        val CLASS_POSITIVE = 0
        val CLASS_NEGATIVE = 1

        private val englishStopWords = arrayOf(
            "i", "me", "my", "myself", "we", "our", "ours", "ourselves", "you", "your", "yours", "yourself", "yourselves",
            "he", "him", "his", "himself", "she", "her", "hers", "herself", "it", "its", "itself", "they", "them", "their",

shubham0204

while True:
    # Request the image from the server
    response = requests.get(url="http://<network_ip_address>:<port>/photo.jpg")
    imgNp = np.array(bytearray(response.content), dtype=np.uint8)
    frame = cv2.imdecode(imgNp, cv2.IMREAD_UNCHANGED ) 
    # As OpenCV decodes images in BGR format, we'd convert it to the RGB format
    frame = cv2.cvtColor( frame , cv2.COLOR_BGR2RGB )

    frame_window.image(frame)

shubham0204

import streamlit as st
import cv2
import numpy as np
import requests

# Title of the app
st.title( 'Mobile Camera Preview in Streamlit' )

# An empty image container that will hold the frames we'll fetch from the server
frame_window = st.image( [] )