Keras Cheat Sheet: Neural Networks in Python
#keras #cheatsheet #python #library #programming #guide
https://t.me/CodeProgrammer
#keras #cheatsheet #python #library #programming #guide
https://t.me/CodeProgrammer
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π Cheat sheets for data science and machine learning
Link: https://sites.google.com/view/datascience-cheat-sheets
Link: https://sites.google.com/view/datascience-cheat-sheets
#DataScience #MachineLearning #CheatSheet #stats #analytics #ML #IA #AI #programming #code #rstats #python #deeplearning #DL #CNN
https://t.me/CodeProgrammerβ
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π19β€11
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Deep Learning with Keras :: Cheat sheet
#DataScience #MachineLearning #CheatSheet #stats #analytics #ML #IA #AI #programming #code #rstats #python #deeplearning #DL #CNN #Keras #R
https://t.me/CodeProgrammerβ
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Top_100_Machine_Learning_Interview_Questions_Answers_Cheatshee.pdf
5.8 MB
Top 100 Machine Learning Interview Questions & Answers Cheatsheet
#DataScience #MachineLearning #CheatSheet #stats #analytics #ML #IA #AI #programming #code #rstats #python #deeplearning #DL #CNN #Keras #Rο»Ώ
https://t.me/CodeProgrammerβ
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Machine Learning from Scratch by Danny Friedman
This book is for readers looking to learn new machine learning algorithms or understand algorithms at a deeper level. Specifically, it is intended for readers interested in seeing machine learning algorithms derived from start to finish. Seeing these derivations might help a reader previously unfamiliar with common algorithms understand how they work intuitively. Or, seeing these derivations might help a reader experienced in modeling understand how different algorithms create the models they do and the advantages and disadvantages of each one.
This book will be most helpful for those with practice in basic modeling. It does not review best practicesβsuch as feature engineering or balancing response variablesβor discuss in depth when certain models are more appropriate than others. Instead, it focuses on the elements of those models.
π Link: https://dafriedman97.github.io/mlbook/content/introduction.html
This book is for readers looking to learn new machine learning algorithms or understand algorithms at a deeper level. Specifically, it is intended for readers interested in seeing machine learning algorithms derived from start to finish. Seeing these derivations might help a reader previously unfamiliar with common algorithms understand how they work intuitively. Or, seeing these derivations might help a reader experienced in modeling understand how different algorithms create the models they do and the advantages and disadvantages of each one.
This book will be most helpful for those with practice in basic modeling. It does not review best practicesβsuch as feature engineering or balancing response variablesβor discuss in depth when certain models are more appropriate than others. Instead, it focuses on the elements of those models.
#DataScience #MachineLearning #CheatSheet #stats #analytics #ML #IA #AI #programming #code #rstats #python #deeplearning #DL #CNN #Keras #R
https://t.me/CodeProgrammerβ
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Master PyTorch Faster with These Free Resources!
Whether you're just getting started with PyTorch or looking to refresh your deep learning skills, these two resources are all you need:
1. PyTorch Cheatsheet
A concise reference guide packed with essential PyTorch commands and patterns. Perfect for quick look-ups during development.
Download:
https://www.dropbox.com/scl/fi/e4xngykrfoubiw3xnd6fz/PyTorch-Cheatsheet.pdf?rlkey=vgx38ckps7aie120imgozgq4g&e=2&st=hgs06d4t&dl=0
2. Learn PyTorch Deep Learning with Hands-On Code
A beginner-friendly PDF with practical examples to help you build and train deep learning models using PyTorch from scratch.
Download:
https://www.dropbox.com/scl/fi/lfo7r6fnd8wjm3gp0jteh/Learn-PyTorch-Deep-Learning-with-Hands-On-Code.pdf?rlkey=mg9cxg41yerouzp0rklm8hqa2&e=2&st=c7k7rgay&dl=0
Save them, share them, and start building smarter models today!
#PyTorch #DeepLearning #AIResources #MachineLearning #Python #Cheatsheet #HandsOnAI
β‘οΈ BEST DATA SCIENCE CHANNELS ON TELEGRAM π
Whether you're just getting started with PyTorch or looking to refresh your deep learning skills, these two resources are all you need:
1. PyTorch Cheatsheet
A concise reference guide packed with essential PyTorch commands and patterns. Perfect for quick look-ups during development.
Download:
https://www.dropbox.com/scl/fi/e4xngykrfoubiw3xnd6fz/PyTorch-Cheatsheet.pdf?rlkey=vgx38ckps7aie120imgozgq4g&e=2&st=hgs06d4t&dl=0
2. Learn PyTorch Deep Learning with Hands-On Code
A beginner-friendly PDF with practical examples to help you build and train deep learning models using PyTorch from scratch.
Download:
https://www.dropbox.com/scl/fi/lfo7r6fnd8wjm3gp0jteh/Learn-PyTorch-Deep-Learning-with-Hands-On-Code.pdf?rlkey=mg9cxg41yerouzp0rklm8hqa2&e=2&st=c7k7rgay&dl=0
Save them, share them, and start building smarter models today!
#PyTorch #DeepLearning #AIResources #MachineLearning #Python #Cheatsheet #HandsOnAI
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SciPy.pdf
206.4 KB
Unlock the full power of SciPy with my comprehensive cheat sheet!
Master essential functions for:
Function optimization and solving equations
Linear algebra operations
ODE integration and statistical analysis
Signal processing and spatial data manipulation
Data clustering and distance computation ...and much more!
π― BEST DATA SCIENCE CHANNELS ON TELEGRAM π
Master essential functions for:
Function optimization and solving equations
Linear algebra operations
ODE integration and statistical analysis
Signal processing and spatial data manipulation
Data clustering and distance computation ...and much more!
#Python #SciPy #MachineLearning #DataScience #CheatSheet #ArtificialIntelligence #Optimization #LinearAlgebra #SignalProcessing #BigData
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Matplotlib_cheatsheet.pdf
3.1 MB
Main features of Matplotlib:
#doc #cheatsheet #PythonTips
Matplotlib Cheatsheet (
https://t.me/CodeProgrammer
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β’ Apply a simple blur filter.
β’ Apply a box blur with a given radius.
β’ Apply a Gaussian blur.
β’ Sharpen the image.
β’ Find edges.
β’ Enhance edges.
β’ Emboss the image.
β’ Find contours.
VII. Image Enhancement (ImageEnhance)
β’ Adjust color saturation.
β’ Adjust brightness.
β’ Adjust contrast.
β’ Adjust sharpness.
VIII. Drawing (ImageDraw & ImageFont)
β’ Draw text on an image.
β’ Draw a line.
β’ Draw a rectangle (outline).
β’ Draw a filled ellipse.
β’ Draw a polygon.
#Python #Pillow #ImageProcessing #PIL #CheatSheet
βββββββββββββββ
By: @CodeProgrammer β¨
from PIL import ImageFilter
blurred_img = img.filter(ImageFilter.BLUR)
β’ Apply a box blur with a given radius.
box_blur = img.filter(ImageFilter.BoxBlur(5))
β’ Apply a Gaussian blur.
gaussian_blur = img.filter(ImageFilter.GaussianBlur(radius=2))
β’ Sharpen the image.
sharpened = img.filter(ImageFilter.SHARPEN)
β’ Find edges.
edges = img.filter(ImageFilter.FIND_EDGES)
β’ Enhance edges.
edge_enhanced = img.filter(ImageFilter.EDGE_ENHANCE)
β’ Emboss the image.
embossed = img.filter(ImageFilter.EMBOSS)
β’ Find contours.
contours = img.filter(ImageFilter.CONTOUR)
VII. Image Enhancement (ImageEnhance)
β’ Adjust color saturation.
from PIL import ImageEnhance
enhancer = ImageEnhance.Color(img)
vibrant_img = enhancer.enhance(2.0)
β’ Adjust brightness.
enhancer = ImageEnhance.Brightness(img)
bright_img = enhancer.enhance(1.5)
β’ Adjust contrast.
enhancer = ImageEnhance.Contrast(img)
contrast_img = enhancer.enhance(1.5)
β’ Adjust sharpness.
enhancer = ImageEnhance.Sharpness(img)
sharp_img = enhancer.enhance(2.0)
VIII. Drawing (ImageDraw & ImageFont)
β’ Draw text on an image.
from PIL import ImageDraw, ImageFont
draw = ImageDraw.Draw(img)
font = ImageFont.truetype("arial.ttf", 36)
draw.text((10, 10), "Hello", font=font, fill="red")
β’ Draw a line.
draw.line((0, 0, 100, 200), fill="blue", width=3)
β’ Draw a rectangle (outline).
draw.rectangle([10, 10, 90, 60], outline="green", width=2)
β’ Draw a filled ellipse.
draw.ellipse([100, 100, 180, 150], fill="yellow")
β’ Draw a polygon.
draw.polygon([(10,10), (20,50), (60,10)], fill="purple")
#Python #Pillow #ImageProcessing #PIL #CheatSheet
βββββββββββββββ
By: @CodeProgrammer β¨
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