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Deep Learning Made Easy – Python

7,999

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Description

Learn how Deep Learning works and how people apply it to solve data science problems.

This course has been created, designed and assembled by professional Data Scientists who have worked in this field for nearly a decade. We can help you understand the complex Deep Learning algorithms from the very base while keeping you grounded to the implementation on real data science problems.

We are sure that you will have fun while learning from our tried and tested structure of course to keep you interested in what’s coming next.

Here is how the course is going to work:

  • Session 1      – Machine Learning Basics.
    • This is the part where we will learn basic perquisite concepts of machine learning.
    • Concepts like regression, logistic regression, model validation techniques etc.
    • We may skip this session if you are familiar with these concepts
  • Session 2      – Introduction to Artificial Neural Networks
    • We will build our intuition behind the Artificial Neural Network algorithm from logistic regression.
    • We will cover concepts like: Hidden Layers, Decision Boundary in ANN,  Back Propagation, Model Optimization etc.
    • Train a basic ANN model using Python
  • Session 3      – TensorFlow and Keras
    • In this mostly practice based session we will understand the very basics of using Tensorflow and see why Keras works best for us.
    • We will also build our very first Deep Learning model using both Tensorflow and Keras.
  • Session 4      – ANN Hyper Parameters
    • This session is more about fine tuning an ANN model and regularization to combat over-fitting.
    • We will cover learning rate, momentum, dropout etc
  • Session 5      – CNN : Convolutional Neural Networks
    • Modified advanced version of ANNs to accommodate Image kind of data.
    • We will go thorough how CNN model learns new features from given image data and classifies images with amazing accuracy while building our own CNN model in Keras.
    • We will understand concepts like Convolutions layers, Pooling Layers and intuition behind CNN architectures.
  • Session 6      – Recurrent Neural Networks (RNN)
    • Another modified version of ANN to accommodate text and sequential data and perform basic NLP tasks.
    • We will understand the inner working of RNN models and build word a character predictor model that will predict next word given previous words.
  • Session 7      – Long Short Term Memory (LSTM)
    • A modified advanced version of RNN that overcomes the short-comings of vanilla RNN and build better text based models.
    • We will also train LSTM model that will outperform RNN model for character prediction.

Features:

  • Fully packed with LAB Sessions. One to learn from and one for you to do it yourself.
  • Course includes Python source code, Datasets and other supporting material at the beginning of each section for you to download and use on your own.
  • Quiz after each section to test your learning.

 

Bonus:

  • We are always updating our content and adding more and more conceptual details in the course.
  • New projects and case studied will be added free to the course in coming time and will be free to access for existing students of this course.