Neural Networks in Python: Deep Learning for Beginners

LEARN ARTIFICIAL NEURAL NETWORKS(ANN) IN PYTHON. BUILD PREDICTIVE DEEP LEARNING MODELS USING KERAS AND TENSORFLOW.


Course content:

Section 1: Introduction
Section 2: Setting up Python and Jupyter Notebook
Section 3: Single Cells - Perceptron and Sigmoid Neuron
Section 4: Neural Networks - Stacking cells to create network
Section 5: Important Concepts - Common Interview Questions
Section 6: Standard Model Parameters
Section 7: Tensorflow and Keras
Section 8: Python - Dataset for classification problem
Section 9: Python - Building and training the Model
Section 10: Python - Solving a Regression problem using ANN
Section 11: Complex ANN Architectures using Functional API
Section 12: Saving and Restoring Models
Section 13: Hyperparameter Tuning
Section 14: Data Preprocessing
Section 15: Classic ML Models- Linear Regression
Section 16: Practice Assignment
Section 17: Bonus

Total Course Duration:

9 hours on-demand video.


This course is for:

  • People pursuing a career in data science
  • Working Professionals beginning their Neural Network journey
  • Statisticians needing more practical experience
  • Anyone curious to master ANN from Beginner level in short span of time

What you will learn:

  • Get a solid understanding of Artificial Neural Networks(ANN) and Deep Learning
  • Building an Artificial Neural Networks(ANN) in Python.
  • Learn usage of Keras and Tensorflow libraries.
  • Understand the business scenarios where Artificial Neural Networks(ANN) is applicable.
  • Use Artificial Neural Networks(ANN) too make predictions.
  • Use Pandas DataFrames to manipulate data and made statistical computations.

Requirements:

Students will need to install Python and Anaconda software but we have a separate lecture to help you install the sameStudents will need to install Python and Anaconda software but we have a separate lecture to help you install the same

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