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The Owl Centre in Kington, Herefordshire, gives unequalled access to these beautiful nocturnal birds of prey.
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Python programming is the state of art now because of its simplest syntax, implementation and availability of libraries it pressures more on code readability. Python is the best platform for Data Science and Data Analytics because of its rich ecosystem that offers multiple packages like NumPy, Pandas, Matplotlib, SciPy, etc.
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# Trying to figure out why the log of this function
# (previous photo) is used as the logistic regression
# error function
import numpy as np
import matplotlib.pyplot as plt
# predicted probabilities
a = np.linspace(0.001, 0.999, num=100);
# actual label
y = 0.999;
loss = -(pow(a,y)*pow((1-a),(1-y)))+1;
plt.plot(a, loss, 'o');
plt.xlabel('Predictions');
plt.ylabel('f(a,y)');
plt.title('f(a,y) for label = 0.999')
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For many researchers, Python is a first-class tool mainly because of its libraries for storing, manipulating, and gaining insight from data. Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all—IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools.Working scientists and data crunchers familiar with reading and writing Python code will find this comprehensive desk reference ideal for tackling day-to-day issues: manipulating, transforming, and cleaning data; visualizing different types of data; and using data to build statistical or machine learning models. Quite simply, this is the must-have reference for scientific computing in Python.With this handbook, you’ll learn how to use: * IPython and Jupyter: provide computational environments for data scientists using Python * NumPy: includes the ndarray for efficient storage and manipulation of dense data arrays in Python * Pandas: features the DataFrame for efficient storage and manipulation of labeled/columnar data in Python * Matplotlib: includes capabilities for a flexible range of data visualizations in Python * Scikit-Learn: for efficient and clean Python implementations of the most important and established machine learning algorithms
import numpy as np
import matplotlib.pyplot as plt
def plot_frequency_sine_waves(sine_wave_tuples, time):
plt.figure(figsize=(24, 6))
for sine_wave, note_name, frequency in sine_wave_tuples:
label = f"{note_name} ({frequency} Hz)"
plt.plot(time, sine_wave, alpha=0.7, label=label)
# Add the summed sine wave for the C major chord
c_major_wave = np.sum(sine_waves, axis=0)
plt.plot(time, c_major_wave, alpha=0.7, label="C major", linestyle="--")
plt.xlabel("Time (s)")
plt.ylabel("Amplitude")
plt.title("C Major Frequencies")
plt.legend(loc="upper right")
plt.grid()
plt.show()
# Example usage
duration = 0.008
sample_rate = 44100
notes = [C4, E4, G4] # Frequencies for a C major chord
note_names = ["C4", "E4", "G4"]
time = np.linspace(0, duration, int(duration * sample_rate), False)
sine_waves = [np.sin(2 * np.pi * freq * time) for freq in notes]
sine_wave_tuples = list(zip(sine_waves, note_names, notes))
plot_frequency_sine_waves(sine_wave_tuples, time)
Taken at the Owl Centre in Kington, Herefordshire. His name is Numpy and he has that wise old owl look.
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Where y is are the true labels (probabilities) and y hat is the predicted probabilitiy.
import numpy as np
import matplotlib.pyplot as plt
# predicted probabilities
yhat = np.linspace(0.001, 0.999, num=100);
y = 0.001;
logloss = -y*np.log(yhat)-(1-y)*np.log(1-yhat);
plt.plot(yhat, logloss, 'o');
plt.xlabel('Predictions');
plt.ylabel('Log Loss');
plt.title('Log loss for ground truth = 0')
What You'll Learn
Make powerful analysisMake robust Machine Learning modelsCreate strong added value to your businessBuild an army of powerful Machine Learning modelsMaking accurate predictions
About
Machine learning is the science of getting computers to act without being explicitly programmed.Many researchers also think it is the best way to make progress towards human-level AI. Machine Learning is everywhere; if you want to know how companies like Google and Amazon extract meaning and insights from massive data sets, this data science course will give you the fundamentals you need. We will walk you step-by-step into the World of Machine Learning. With every tutorial you will develop new skills and improve your understanding of this challenging yet lucrative sub-field of Data Science.
In this program, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you'll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques. course will also draw from numerous case studies and applications, so that you'll also learn how to apply learning algorithms to building smart robots (perception, control),autonomous vehicles, text understanding, computer vision, medical informatics, database mining, and other areas.
Requirements
No coding experience is necessary
Any operating system can be used like Mac,Windows and linux
Course Outline
Module 1 : Introduction
Module 2 : Python
Module 3 : Numpy
Module 4 : Scikit-Learn
norkandirblog.wordpress.com/2016/12/23/cara-instal-opencv...
Cara Instal OpenCV dan Python Beserta Linknya
Awalnya modul ini disusun dari hasil rangkuman buku OpenCV Computer Vision with Python, tetapi karena versi OpenCV 2.4.3 sudah tidak ada, maka terpaksa saya menggunakan OpenCV versi lain (yang saya gunakan adalah OpenCV 2.4.11 karena stabil). Untuk itu, software dependensinya juga menyesuaikan, yaitu numpy versi 1.9.1. Awalnya saya menginstal numpy versi numpy 1.6.2 sesuai yang disarankan oleh penyusun buku di atas, tetapi program mengalami eror karena OpenCV yang saya pakai versinya lebih tinggi dari yang disarankan. Masalah terselesaikan dengan menginstal numpy versi 1.9.1.
Masalah kedua muncul lagi, yaitu skrip (kode) yang ada di buku OpenCV Computer Vision with Python tidak bisa dijalankan, akhirnya saya memutuskan mencari skrip lain dan mendapatkannya di situs resmi OpenCV.org. Ternyata di versi terbaru ini mengalami beberapa perkembangan, dan dugaan saya hal ini yang menyebabkan sintak lama tidak kompatibel lagi, dan juga, kabarnya, OpenCV 2.4.3 belum stabil dan disempurnakan di versi 2.4.11 ini.
Untuk bisa menjalankan dan memakai modul ini dengan baik, maka Anda membutuhkan 3 (tiga) file utama, yaitu OpenCV, Python, dan Numpy. Berikut versi masing-masing software dan link untuk mendownloadnya:
1. OpenCV 2.4.11 >> nchc.dl.sourceforge.net/project/opencvlibrary/opencv-win/... (351 MB)
2. Python 2.7.3 >> www.python.org/ftp/python/2.7.3/python-2.7.3.msi (15,1 MB)
3. Nampy 1.9.1 >> nchc.dl.sourceforge.net/project/numpy/NumPy/1.9.1/numpy-1... (8,52 MB)
Untuk berjaga-jaga, download juga:
4. Scipy 0.11.0 >> nchc.dl.sourceforge.net/project/scipy/scipy/0.11.0/scipy-... (45,6 MB)
5. Matplotlib 1.5.0 >> ncu.dl.sourceforge.net/project/matplotlib/matplotlib/matp... (5,88 MB)
Komputer yang saya gunakan beraksitektur 64 bit Windows 7. Namun, semua software di atas untuk 32 bit (x86/win32).
Setelah didownload semuanya, maka tahapan berikutnya adalah mengistal dengan urutan sebagai berikut:
1. Instal OpenCV 2.4.11. Taruh hasil instalasi di drive C atau D atau lainnya, terserah. Maka hasilnya adalah C:/opencv atau D:/opencv. Adapun saya, OpenCV ditaruh di E:/opencv.
2. Instal Python 2.7.3. Secara defaul akan terinstal di C:/Python27. Setelah selesai, maka copy file cv2.pyd yang ada di “E:\opencv\build\python\2.7\x86\” dan taruh di “C:\Python27\Lib\site-packages\”. Hal ini dimaksudkan untuk membuat link antara OpenCV dengan Python. Setelah itu, daftarkan Python ke Path Environments agar saat file Python dieksekusi maka komputer bisa mengenalinya. Caranya adalah sebagai berikut:
3. Klik Start > klik kanan Computer > Properties > klik Advanced system setting > Advanced > Environment Variables
Cara Instal OpenCV dan Python Beserta Linknya
4. Kemudian, pada System variables > Path dan klik edit > tambahkan “;C:\Python27” di kotak Variables value, seperti gambar berikut:
Cara Instal OpenCV dan Python Beserta Linknya
5. Instal Numpy 1.9.1. Biarkan ia secara default. Next dan next saja saat instalasi.
Jika semua tahapan ini sudah Anda lakukan dengan benar, maka sekarang Python yang terintegrasi dengan OpenCV sudah bisa digunakan.
Artikel norkandirblog.wordpress.com
The Owl Centre in Kington, Herefordshire, gives unequalled access to these beautiful nocturnal birds of prey.
For the story, please visit: www.ursulasweeklywanders.com/travel/small-breeds-farm-par...
Deep learning (also known as deep structured learning or hierarchical learning) is part of a broader family of machine learning methods based on artificial neural networks.
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The Owl Centre in Kington, Herefordshire, gives unequalled access to these beautiful nocturnal birds of prey.
For the story, please visit: www.ursulasweeklywanders.com/travel/small-breeds-farm-par...
# examples/Python/Basic/pointcloud.py
import numpy as np
import open3d as o3d
if __name__ == "__main__":
print("Load a ply point cloud, print it, and render it")
pcd = o3d.io.read_point_cloud("../../TestData/fragment.ply")
print(pcd)
print(np.asarray(pcd.points))
o3d.visualization.draw_geometries([pcd])
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“Data science” is just about as broad of a term as they come. It may be easiest to describe what it is by listing its more concrete components: Data exploration & analysis. Included here: Pandas; NumPy; SciPy; a helping hand from Python's Standard Library.
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Python plays a key role in the fintech industry. It is the most popular language for developing financial applications. In the python course for finance singapore you will learn basic python coding concepts, object oriented programming concepts, data-visualization, conditional statements, functions, sequence, loops, best practices of financial analysis, python libraries pandas, and numpy, and matplotlib.
Python is the most widely used programming language today. When it comes to solving data science tasks and challenges, Python never ceases to surprise its users. Most data scientists are already leveraging the power of Python programming every day. Python has been built with extraordinary Python libraries. let see Top 5 Python libraries to learn for Data Science.
TensorFlowNumPySciPyPandasMatplotlib
Tensorflow
Tensorflow makes the definig functions very easy. Ituse scaler vector matrix for comupting the data.The core open source library to help you develop and train ML models.It helps in easy data model building. This model later came to train and compute the data.tesnorflow data visualization and statistical modeling tools are faster to compute the complex data
TensorFlow library do to numerical computations, which in itself doesn’t seem all too special, but these computations are done with data flow graphs. In these graphs, nodes represent mathematical operations, while the edges represent the data, which usually are multidimensional data arrays or tensors, that are communicated between these edges.
Numpy
Numpy is a toolbox for your complex mathamtical operations, data sets in any format like vector,matrix etc. It supports for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays.. www.technoflights.com/top-5-python-libraries-to-learn-for...
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This short explainer video introduces Python beginners to the three most essential libraries used in data science and programming:
📦 NumPy – Fast numerical computing
📊 Pandas – Clean and analyze structured data
📈 Matplotlib – Create powerful data visualizations
These tools help you build a solid foundation in Python for real-world applications.
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I am a Ph.D. student working on Deep Reinforcement Learning (RL). I have experience on a variety of academic and industrial-level projects, and have presented my work at top AI conferences.
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I am comfortable working with the following languages and tools:
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Keras
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