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Deep Neural Networks using Python Practice Exam

Deep Neural Networks using Python Practice Exam


About the Deep Neural Networks using Python Exam

Deep Neural Networks using Python focuses on building and training advanced neural networks for complex machine learning tasks. Using Python and libraries like TensorFlow and Keras, learners will dive into architectures such as multi-layer perceptrons, and explore techniques for optimization, regularization, and model evaluation. This course provides hands-on experience in creating models for applications like image recognition, natural language processing, and predictive analytics, empowering learners to solve real-world problems with deep learning.


Skills Required to learn 

  • Basic understanding of Python programming.
  • Familiarity with machine learning concepts and algorithms.
  • Knowledge of linear algebra, calculus, and statistics.
  • Understanding of neural networks and how they work.
  • Experience with Python libraries such as NumPy, Pandas, and Matplotlib.
  • Basic knowledge of deep learning frameworks like TensorFlow or Keras (optional but beneficial).
  • Experience with data preprocessing and manipulation techniques.


Knowledge Gained

  • Proficiency in building and training deep neural networks using Python and libraries like TensorFlow and Keras.
  • Understanding of key deep learning concepts such as backpropagation, activation functions, and gradient descent.
  • Hands-on experience in implementing neural network architectures like multi-layer perceptrons (MLPs) and deep feedforward networks.
  • Skills in model optimization, regularization, and hyperparameter tuning to improve model performance.
  • Knowledge of techniques for evaluating and fine-tuning deep learning models for real-world applications.
  • Ability to apply deep neural networks for tasks like image recognition, speech processing, and predictive analytics.
  • Experience in deploying and scaling deep learning models for production environments.


Who should take the Exam?

  • Aspiring machine learning engineers and data scientists who want to specialize in deep learning.
  • Professionals looking to deepen their knowledge of neural networks and their applications.
  • Software developers interested in transitioning to AI and deep learning roles.
  • Researchers and academicians working with neural networks in fields like AI, computer vision, and NLP.
  • IT professionals aiming to validate their expertise in deep learning and neural network models.
  • Students preparing for careers in deep learning and artificial intelligence.
  • Individuals looking to earn certification and demonstrate proficiency in building deep neural networks with Python.


Course Outline

Introduction

  • Introduction to Instructor
  • Introduction to Course

Basics of Deep Learning

  • Problem to Solve Part 1
  • Problem to Solve Part 2
  • Problem to Solve Part 3
  • Linear Equation
  • Linear Equation Vectorized
  • 3D Feature Space
  • N-Dimensional Space
  • Theory of Perceptron
  • Implementing Basic Perceptron
  • Logical Gates for Perceptrons
  • Perceptron Training Part 1
  • Perceptron Training Part 2
  • Learning Rate
  • Perceptron Training Part 3
  • Perceptron Algorithm
  • Coding Perceptron Algo (Data Reading and Visualization)
  • Coding Perceptron Algo (Perceptron Step)
  • Coding Perceptron Algo (Training Perceptron)
  • Coding Perceptron Algo (Visualizing the Results)
  • Problem with Linear Solutions
  • Solution to Problem
  • Error Functions
  • Discrete Versus Continuous Error Function
  • Sigmoid Function
  • Multi-Class Problem
  • Problem of Negative Scores
  • Need of SoftMax
  • Coding SoftMax
  • One-Hot Encoding
  • Maximum Likelihood Part 1
  • Maximum Likelihood Part 2
  • Cross Entropy
  • Cross Entropy Formulation
  • Multi-Class Cross Entropy
  • Cross Entropy Implementation
  • Sigmoid Function Implementation
  • Output Function Implementation

Deep Learning

  • Introduction to Gradient Descent
  • Convex Functions
  • Use of Derivatives
  • How Gradient Descent Works
  • Gradient Step
  • Logistic Regression Algorithm
  • Data Visualization and Reading
  • Updating Weights in Python
  • Implementing Logistic Regression
  • Visualization and Results
  • Gradient Descent Versus Perceptron
  • Linear to Non-Linear Boundaries
  • Combining Probabilities
  • Weighted Sums
  • Neural Network Architecture
  • Layers and DEEP Networks
  • Multi-Class Classification
  • Basics of Feed Forward
  • Feed Forward for DEEP Net
  • Deep Learning Algo Overview
  • Basics of Backpropagation
  • Updating Weights
  • Chain Rule for Backpropagation
  • Sigma Prime
  • Data Analysis NN (Neural Networks) Implementation
  • One-Hot Encoding (NN Implementation)
  • Scaling the Data (NN Implementation)
  • Splitting the Data (NN Implementation)
  • Helper Functions (NN Implementation)
  • Training (NN Implementation)
  • Testing (NN Implementation)

Optimizations

  • Underfitting vs Overfitting
  • Early Stopping
  • Quiz
  • Solution and Regularization
  • L1 and L2 Regularization
  • Dropout
  • Local Minima Problem
  • Random Restart Solution
  • Vanishing Gradient Problem
  • Other Activation Functions

Final Project

  • Final Project Part 1
  • Final Project Part 2
  • Final Project Part 3
  • Final Project Part 4
  • Final Project Part 5

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