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Tensorflow Deep Learning – Data Science in Python

سرفصل های دوره

Tensorflow Deep Learning Python : Tensorflow Neural Network Training : Tensorflow Models - Android Java : Tensorflow C#


1 - INTRODUCTION TO TENSORFLOW The Key Concepts and Software Tools
  • 1 - Welcome to the World of TensorFlow
  • 2 - Introduction to the Course
  • 3 - Data and Scripts For the Course.html
  • 4 - What is Artificial Intelligence
  • 5 - Python Data Science Environment
  • 6 - For Mac Users
  • 7 - Introduction to IPython
  • 8 - IPython in Browser
  • 9 - Install Tensorflow
  • 10 - Written Tensorflow Installation Instructions.html

  • 2 - Introduction to TensorFlow
  • 11 - A Brief Touchdown
  • 12 - A Brief Touchdown Computational Graphs
  • 13 - Common Mathematical Operators in Tensorflow.html
  • 14 - A Tensorflow Session
  • 15 - Interactive Tensorflow Session
  • 15 - Lecture-18-interactive-session.txt
  • 15 - lecture-14-interactive.txt
  • 16 - Constants and Variables in Tensorflow
  • 16 - Lecture-19-constant-var.txt
  • 17 - Lecture-21-place.txt
  • 17 - Placeholders in Tensorflow
  • 18 - TensorBoard Visualize Graphs in TensorFlow
  • 19 - Access TensorBoard Graphs

  • 3 - Other Python Packages and Their Interaction with Tensorflow
  • 20 - Miscellaneous Python Packages for Data Science
  • 21 - Introduction to Numpy
  • 22 - Create Numpy Arrays
  • 22 - numpy-create.txt
  • 23 - Numpy Operations
  • 23 - numpy-op.txt
  • 24 - Numpy for Statistical Operation
  • 24 - numpy-stats.txt
  • 25 - Introduction to Pandas
  • 26 - Read in Data from CSV
  • 26 - Resp2.csv
  • 26 - bostonTxt.txt
  • 26 - read-csv-pd.txt
  • 26 - winequality-red.csv
  • 27 - Read in Excel Data
  • 27 - boston1.xls
  • 27 - read-excel-pd.txt
  • 28 - Basic Data Cleaning
  • 29 - Convert to Tensor Objects

  • 4 - Statistical Modelling with Tensorflow
  • 30 - Correlation Analysis
  • 31 - Linear RegressionTheory
  • 32 - Linear Regression From First Principles With Tensorflow
  • 32 - ols-reg.txt
  • 33 - Lecture-32-Viz.txt
  • 33 - Visualize the Results of OLS
  • 34 - Lecture-33-ML-Reg.txt
  • 34 - Multiple Regression With TensorflowPart 1
  • 35 - Multiple Regression With TensorflowMachine Learning Approach
  • 36 - Estimate With Tensorflow Estimators
  • 37 - Multiple Regression With Tensorflow Estimators
  • 38 - More on Linear Regressor Estimator
  • 38 - linear-regressor-csv-data.txt
  • 38 - listings.csv
  • 39 - GLM Generalized Linear Model
  • 40 - Linear Classifier For Binary Classification
  • 40 - creditcard.csv
  • 40 - linear-classifier-python.txt
  • 41 - Accuracy Assessment For Binary Classification
  • 42 - Linear Classification with Binary Classification With Mixed Predictors
  • 42 - Linear-classifier-mixed-pred.txt
  • 42 - titanic.csv

  • 5 - Introduction to Machine Learning
  • 43 - Introduction
  • 44 - What is Machine Learning

  • 6 - Unsupervised Learning
  • 45 - What is Unsupervised Learning
  • 46 - KMeans ClusteringTheory
  • 47 - Implement KMeans on Real Data
  • 47 - hw-data.txt
  • 47 - kmeans-hw.txt

  • 7 - Supervised Learning
  • 48 - Softmax Classification
  • 48 - softmax.txt
  • 49 - Random Forest RF for Binary Classification
  • 49 - random-forest-binary.txt
  • 49 - titanic.csv
  • 50 - Random Forest RF for Multiclass Classification
  • 51 - kNN Classification
  • 51 - knn-class.txt

  • 8 - Artificial Neural Networks and Deep Learning with Tensorflow
  • 52 - Introduction to Artificial Neural Networks ANN
  • 53 - Multi Layer Perceptron MLP
  • 53 - mlp-mnist.txt
  • 54 - Deep Neural Network DNN Classifier
  • 54 - Iris.csv
  • 54 - dnnclass.txt
  • 55 - Deep Neural Network DNN Classifier With Mixed Predictors
  • 55 - dnnclass-titanic.txt
  • 55 - titanic.csv
  • 56 - Deep Neural Network DNN Regression
  • 56 - dnnreg.txt
  • 57 - Wide and Deep Learning
  • 57 - adult.csv
  • 57 - dnn-wide-deep.txt
  • 58 - Autoencoders Theory
  • 59 - Autoencoders for Credit Card Fraud Detection
  • 59 - autoencoder-binary.txt
  • 59 - creditcard.csv
  • 60 - Autoencoders for Multiple Classes
  • 60 - sec8-lect61.txt

  • 9 - Convolution Neural Network CNN For Image Analysis
  • 61 - Introduction to CNN
  • 62 - Implement a CNN for MultiClass Supervised Classification
  • 62 - cnn1.txt
  • 63 - Activation Functions
  • 64 - More on CNN
  • 65 - PreRequisite For Working With Imagery Data
  • 65 - cnn-image.txt
  • 66 - CNN on Image Data
  • 66 - cnn-cat-dog.txt
  • 67 - More on TFLearn
  • 67 - sec9-lect68.txt
  • 68 - Autoencoders with CNN
  • 68 - sec9-lect69.txt

  • 10 - Miscellaneous Section
  • 69 - Use Colabs for Jupyter Data Science
  • 69 - colab.txt
  • 70 - Colab GPU
  • 71 - Introduction To Github
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    تاریخ انتشار: ۲۸ اردیبهشت ۱۴۰۲
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