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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.
    • You 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 to the course in coming time and will be free to access for existing and new students of this course.

Prerequisite:

  • This course is designed with expectations that students taking the course are familiar with basic python programming for data-science.
  • You may take our course: Introduction to Python Programming as prerequisite to brush up your python programming skills.

Course Curriculum

Section 1 - Machine Learning Basics
Datasets – Machine Learning Basics 00:00:00
1. Regression 00:00:00
2. Regression LAB 00:00:00
3. Logistic regression 00:00:00
4. Logit function 00:00:00
5. Building a logistic Regression Line 00:00:00
6. Multiple logistic regression 00:00:00
7. Validation Matrices – Classification Matrix 00:00:00
8. Sensitivity and Specificity 00:00:00
9. Sensitivity vs Specificity 00:00:00
10. Sensitivity Specificity LAB 00:00:00
11. ROC and AUC 00:00:00
12. ROC and AUC LAB 00:00:00
13. The training error 00:00:00
14. Over Fitting and Under Fitting 00:00:00
15. Bias Variance Tradeoff 00:00:00
16. Holdout data validation 00:00:00
17. Hold Out data validation LAB 00:00:00
Section 2 - Introduction to ANN
Datasets – Introduction to ANN 00:00:00
1. Introduction to ANN 00:00:00
2. Logistic Regression Recap LAB 00:00:00
3. Decision Boundry – Logistic Regression 00:00:00
4. Decision Boundry – LAB 00:00:00
5. New Representation for Logistic Regression 00:00:00
6. Non Linear Decision Boundry – Problem 00:00:00
7. Non Linear Decision Boundry – Solution 00:00:00
8. Intermediate Output LAB 00:00:00
9. Neural Network Intution 00:00:00
10. Neural Network Algorithm 00:00:00
11. Demo Neural Network Algorithm 00:00:00
12. Neural Network LAB 00:00:00
13. Local Minima and Number of Hidden Layers 00:00:00
14. Digit Recogniser Lab 00:00:00
15. Conclusion 00:00:00
Section 3 - TensorFLow and Keras
3.1 Introduction to Deep Learning Frameworks 00:00:00
3.2 Key Terms of Tensorflow 00:00:00
3.3 Coding basics in Tensorflow 00:00:00
3.4 Model building intution 00:00:00
3.5 LAB Building Linear and Logistic regression models with Tensorflow 00:00:00
3.6 LAB MNIST model using tensorflow 00:00:00
3.7 Tensorflow shortcomings and Intro to Keras 00:00:00
3.8 LAB MNIST model using Keras 00:00:00
3.9 Tensorflow vs Keras and conclusion 00:00:00
Section 4 ANN Hyperparameters
Datasets – ANN Hyperparameters 00:00:00
4.1 Introduction to Hyperparameters 00:00:00
4.2 LAB_calculating number of parameters 00:00:00
4.3 Regularization 00:00:00
4.4 Over-fitting of a Regression Model LAB 00:00:00
4.5 Regularization in Regression LAB 00:00:00
4.7 Demo_Regularization in Neural Networks 00:00:00
4.8 Dropout Regularization 00:00:00
4.9 LAB_ Dropout Regularization 00:00:00
4.10 Weight sharing in Dropout 00:00:00
4.11 Early stopping 00:00:00
4.12 LAB_ Early stopping notebook 00:00:00
4.13 Activation Function 00:00:00
4.14 Demo_Activation Function 00:00:00
4.15 Problem of Vanishing Gradients 00:00:00
4.16 ReLU activation Function 00:00:00
4.17 Activation Function for Last Layer 00:00:00
4.18 Learning Rate 00:00:00
4.19 Demo_ Learning Rate 00:00:00
4.20 Momentum 00:00:00
4.21 LAB Learning rate and momentum 00:00:00
4.22 Gradient Descent Batches 00:00:00
4.23 LAB Gradient Descent vs Mini Batch 00:00:00
4.24 Hyper Parameter conclusion 00:00:00
Section 5 Convolution Neural Networks (CNN)
5.1 Introduction to CNN – fundamentals of Image data 00:00:00
5.2 LAB ANN on Image data – MNIST 00:00:00
5.3 Counting parameters of ANN for Imge Data 00:00:00
5.4 LAB Parameter count in ANN on large Images 00:00:00
5.5 Issue with ANN on Image Data 00:00:00
5.6 Preserving Spatial Integrity of Images in Neural Network 00:00:00
5.7 How filters work 00:00:00
5.8 Kernal Matrix and Convoluted Layers 00:00:00
5.9 Convoluted Features 00:00:00
5.10 LAB Convolution Layer 00:00:00
5.11 Handling edges of Image in convolution 00:00:00
5.12 Depth of Convolutions 00:00:00
5.13 Number of Weights in Convolution Layers 00:00:00
5.14 Pooling Convolution Layers 00:00:00
5.15 LAB Pooling 00:00:00
5.16 The CNN Architecture 00:00:00
5.17 LAB CNN on MNIST 00:00:00
5.18 Conclusion 00:00:00
Section 6 Recurrent Neural Netoworks (RNN)
Datasets – Recurrent Neural Netoworks_RNN 00:00:00
6.1 Introduction to RNN 00:00:00
6.2 Sequential Models 00:00:00
6.3 Sequential ANNs 00:00:00
6.4 LAB Sequential ANNs 00:00:00
6.5 RNNs The Programmed Sequential Models 00:00:00
6.6 BackPropagation in RNNs 00:00:00
6.7 Number of Parameters in RNN Models 00:00:00
6.8 BPTT Details 00:00:00
6.9 LAB RNN Model Building 00:00:00
6.10 Issues with RNNs 00:00:00
6.11 RNN Conclusion 00:00:00
Section 7 LSTM
Datasets – LSTM 00:00:00
7.1 Introduction to LSTM 00:00:00
7.2 LSTM What is Vanishing Gradient 00:00:00
7.3 Mathematics of Vanishing Gradinets 00:00:00
7.4 LAB_Vanishing Gradients 00:00:00
7.5 RNN Other Issues LSTM main idea 00:00:00
7.6 LSTM Gates 00:00:00
7.7 LSTM Different Representations 00:00:00
7.8 LAB LSTM 00:00:00
7.9 LSTM Conlcusion 00:00:00

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