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Deep Learning for Everyone – 2023 – Tensorflow Updated

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Go from Beginner to Expert with building intuition on single neuron till deep neural networks in Keras & tensorflow


01 - Welcome
  • 001 Introduction to Deep Learning 101

  • 02 - Getting basics right
  • 001 Artificial Neural Networks
  • 002 Activation Function
  • 003 Bias
  • 004 Data
  • 005 Applications of Data
  • 006 Models
  • 007 Loss Functions
  • 008 Learning Algorithms & Model Performance

  • 03 - Python Crash Course on Basics
  • 001 Getting System Ready - Jupyter Notebook
  • 002 Accessing Google Colab Notebook
  • 003 Download Materials.html
  • 003 Python-Crash-Course.zip
  • 004 Python Basics - Data Types
  • 005 Python Basics - Containers in Python
  • 006 Control Statements Python if..else
  • 007 Python Control statments - While and For
  • 008 Functions & Classes in Python

  • 04 - Python for Data Science Crash Course
  • 001 Numpy Part 1
  • 002 Numpy Part 2
  • 003 Numpy Part 3
  • 004 Pandas in Python - Pandas Series
  • 005 Pandas Data Frame
  • 006 Pandas Data frame - cleaning & Examining the data
  • 007 Plotting with Matplotlib
  • 008 Contour Plots

  • 05 - MP Neuron Model
  • 001 MP Neuron Introduction
  • 002 Intuition of data
  • 003 Loss & finding parameters
  • 004 Mathematical Intuition

  • 06 - MP Neuron in Python
  • 001 Deep-Learning-MP-Neuron.zip
  • 001 Download Materials.html
  • 002 MP Neuron - Data import
  • 003 Train Test Split
  • 004 Modify Data
  • 005 MP Neuron in Python
  • 006 MP Neuron Class
  • 007 Assignment for MP Neuron in Python.html
  • 007 MP-Neuron-Assignment.zip

  • 07 - Summary of MP Neuron
  • 001 Summary of MP Neuron

  • 08 - Perceptron
  • 001 Perceptron
  • 002 Perceptron Model and its representation
  • 003 Loss function & Parameter Update
  • 004 Why Update Rule Works
  • 005 Update Rule in Programs

  • 09 - Perceptron in Python
  • 001 Download Materials.html
  • 001 Perceptron-Implementation.zip
  • 002 Perceptron in Python
  • 003 Visualize the Accuracy with epochs
  • 004 Perceptron Assignment.html
  • 004 Perceptron-Assignment.zip

  • 10 - Sigmoid Neuron
  • 001 Download Materials.html
  • 001 Sigmoid-Neuron-Intuition.zip
  • 002 Percepron Limitations
  • 003 Sigmoid Neuron Introduction
  • 004 Sigmoid Neuron Data
  • 005 Sigmoid Intuition
  • 006 Manual fitting of data
  • 007 Gradient descent
  • 008 Program overview
  • 009 Program in Python

  • 11 - Sigmoid Neuron Implement with Python
  • 001 Download Materials.html
  • 001 Sigmoid-Neuron-Heart-Disease-dataset.zip
  • 002 Download Dataset
  • 002 heart-disease-uci.zip
  • 003 Data Standardization -1
  • 004 Data Standardization - 2
  • 005 Class Sigmoid
  • 006 Sigmoid Assignment.html
  • 006 Sigmoid-Neuron-Assignment.zip

  • 12 - Basic Probability
  • 001 Introduction to Probability and Random Variables
  • 002 Why Random Variable is important
  • 003 Random Variable - Types
  • 004 Probability Distribution Table
  • 005 Why do we require Entropy Loss

  • 13 - Why Deep Neural Networks - Intuition
  • 001 Download Materials.html
  • 001 Why-DeepLearning-Neural-Networks.zip
  • 002 Why Deep Neural Networks
  • 003 Linear Separation of Data

  • 14 - Universal Approximation Theorem - Deep Learning Foundation
  • 001 Understanding Universal Approximation Theorem
  • 002 Confirming Universal Approximation Theorem Works
  • 003 Going deep into Neural Networks
  • 004 Challenges in Creating Deep Neural Networks from Scratch

  • 15 - Deep Learning with Tensorflow 2.x
  • 001 1-creating-first-nn-with-tf-2-x.zip
  • 001 Code File for this section.html
  • 002 Deep Neural Networks - Recap
  • 003 Introducing Tensorflow
  • 004 Building a Neural Network with Tensorflow
  • 005 Create First Neural Network with Tensorflow
  • 006 Training the Deep Neural Network
  • 007 Training Evaluation
  • 008 Summary

  • 16 - Activation Functions in Deep Learning Neural Networks
  • 001 Code File for this Section.html
  • 001 activation-functions-in-tensorflow.zip
  • 002 Activation Functions in Deep Learning Neural Networks - Introduction
  • 003 Various Activation Functions
  • 004 Summary on Activation Functions
  • 005 Common Network Configuration

  • 17 - Applying the Deep learning
  • 001 Moving from Shallow learning to Deep learning
  • 002 Assignment File for Deep learning neural networks.html
  • 002 Assignment-File-for-DeepLearning-Neural-Networks.zip
  • 003 Keras Basics
  • 004 DeepLearning-Multi-Class-Classification.zip
  • 004 Deep-Learning-Regression.zip
  • 004 Download Materials.html
  • 005 Types of Problems
  • 006 ReLU , Softmax & Cross Entropy
  • 007 DeepLearning-Multi-Class-Classification.zip
  • 007 Implementing Multi Class classification using keras
  • 008 Deep-Learning-Regression.zip
  • 008 Regression Problem
  • 009 Tensorflow Advanced Tricks - Ways to create Neural Network
  • 010 Tensorflow - Subclassing Methods
  • 011 Bonus - Get More of Learning Journey.html

  • 18 - Machine Learning for Projects
  • 001 Machine learning Deployment Part 1 - Model Prep - End to End
  • 002 Machine learning Deployment Part 2 - Deploy Flask App - End to End
  • 003 Streamlit Tutorial
  • 004 Additional content to support your learning journey.html
  • 005 Course Intro to MLOps
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    تاریخ انتشار: 20 شهریور 1402
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