Category: Artificial Intelligence

  • Overview of AWS Tech Portfolio 2023

    Overview of AWS Tech Portfolio 2023

    This article provides with an overview of 50+ Amazon Web Services (AWS) 2023. AWS is the leading vendor of cloud services and infrastructure, dominating the cloud computing market: Amazon net sales increased by 15% to $127.1 billion in Q3 2022 as compared to $110.8 billion in Q3 2021. AWS segment sales increased by 27% year-over-year to reach…

  • Gold ETF Price Prediction using the Bayesian Ridge Linear Regression

    Gold ETF Price Prediction using the Bayesian Ridge Linear Regression

    Featured Photo by Pixabay. Let’s set the working directory GOLD import osos.chdir(‘GOLD’) os. getcwd() and import the following libraries from sklearn.linear_model import LinearRegression import pandas as pdimport numpy as np import matplotlib.pyplot as plt%matplotlib inlineplt.style.use(‘seaborn-darkgrid’) import yfinance as yf Let’s read the dataDf = yf.download(‘GLD’, ‘2022-01-01’, ‘2023-03-25’, auto_adjust=True) Df = Df[[‘Close’]] Df = Df.dropna() Let’s…

  • Deep Reinforcement Learning (DRL) on $MO 8.07% DIV USA Stock Data 2022-23

    Deep Reinforcement Learning (DRL) on $MO 8.07% DIV USA Stock Data 2022-23

    MLQ.ai: In fact, many AI experts agree that DRL is likely to be the best path towards AGI, or artificial general intelligence. Spinning Up in DRL at OpenAI: “We believe that deep learning generally—and DRL specifically—will play central roles in the development of powerful AI technology.” Key assumptions and limitations of the DRL framework: Key…

  • Predicting the JPM Stock Price and Breakouts with Auto ARIMA, FFT, LSTM and Technical Trading Indicators

    Predicting the JPM Stock Price and Breakouts with Auto ARIMA, FFT, LSTM and Technical Trading Indicators

    Featured Photo by Pixabay In this post, we will look at the JPM stock price and relevant breakout strategies for 2022-23. Referring to the previous case study, our goal is to combine the Auto ARIMA, FFT, LSTM models and Technical Trading Indicators (TTIs) into a single framework to optimize advantages of each. Specifically, we will…

  • Risk-Return Analysis and LSTM Price Predictions of 4 Major Tech Stocks in 2023

    Risk-Return Analysis and LSTM Price Predictions of 4 Major Tech Stocks in 2023

    The open-source Python workflow breaks down our investigation into the following 4 steps: (1) invoke yfinance to import real-time stock information into a Pandas dataframe; (2) visualize different dataframe columns with Seaborn and Matplotlib; (3) compare stock risk/return using historical data; (4) predict stock prices in 2023 with the trained LSTM model. Input Data Let’s…

  • Performance Analysis of Face Recognition Out-of-Box ML/AI Workflows

    Performance Analysis of Face Recognition Out-of-Box ML/AI Workflows

    Featured Photo by cottonbro studio, Pexels Facial Recognition (FR) is a category of biometric software that maps an individual’s facial features mathematically and stores the data as a faceprint. The goal of this case study is the Exploratory Data Analysis (EDA) and performance QC analysis of out-of-box ML/AI workflows tested on public-domain datasets and real-time webcam GUI.…

  • AI-Driven Object Detection & Segmentation with Meta Detectron2 Deep Learning

    AI-Driven Object Detection & Segmentation with Meta Detectron2 Deep Learning

    Method Computer Resources Python 3 Google Compute Engine backend (GPU) Install Detectron2 !python -m pip install pyyaml==5.1 import sys, os, distutils.core # Note: This is a faster way to install detectron2 in Colab, but it does not include all functionalities. # See https://detectron2.readthedocs.io/tutorials/install.html for full installation instructions !git clone ‘https://github.com/facebookresearch/detectron2’ dist = distutils.core.run_setup(“./detectron2/setup.py”) !python -m pip install {‘ ‘.join([f”‘{x}’” for x in dist.install_requires])} sys.path.insert(0, os.path.abspath(‘./detectron2’)) # Properly install detectron2. (Please do not install twice in both ways) # !python -m pip install ‘git+https://github.com/facebookresearch/detectron2.git’ Import Libraries import torch, detectron2 !nvcc –version TORCH_VERSION = “.”.join(torch.__version__.split(“.”)[:2]) CUDA_VERSION = torch.__version__.split(“+”)[-1] print(“torch: “, TORCH_VERSION, “; cuda: “, CUDA_VERSION) print(“detectron2:”, detectron2.__version__) nvcc: NVIDIA (R) Cuda compiler driver Copyright (c) 2005-2022 NVIDIA Corporation Built on Tue_Mar__8_18:18:20_PST_2022 Cuda compilation tools, release 11.6, V11.6.124 Build cuda_11.6.r11.6/compiler.31057947_0 torch: 1.13 ;…

  • Multi-Label Keras CNN Image Classification of MNIST Fashion Clothing

    Multi-Label Keras CNN Image Classification of MNIST Fashion Clothing

    The Fashion MNIST Dataset Fashion-MNIST is a dataset of Zalando’s article images—consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28×28 grayscale image, associated with a label from 10 classes: Each image pixel has a single pixel-value associated with it, indicating the lightness or darkness…