Through the use of deep learning techniques, students will be able to develop their first artificial neural network in the Data Science: Deep Learning and Neural Networks in Python course.

The course requires students to understand basic mathematical concepts, Numpy and Python. It is suitable for students who want to master deep learning for Data Science or Machine Learning. The courses are usually available at **INR 3,499** on Udemy but you can click now to get **87% off** and get for **Data Science: Deep Learning and Neural Networks in Python Course** for **INR 449**.

## Who all can opt for this course?

- Students interested in learning machine learning
- Professionals who wish to incorporate neural networks into their data science and machine learning pipeline

## Course Highlights

Key Highlights | Details |
---|---|

Registration Link | Apply Now! |

Price | INR 449 (INR 3,499) 87 % off |

Duration | 11 hours |

Rating | 4.7/5 |

Student Enrollment | 51,314 students |

Instructor | Lazy Programmer Inc. https://www.linkedin.com/in/lazyprogrammerinc. |

Topics Covered | Deep Learning, Coding a Neural Network, TensorFlow |

Course Level | Intermediate |

Total Student Reviews | 8,726 |

## Learning Outcomes

- Discover how Deep Learning really functions (not just some diagrams and magical black box code)
- Learn the fundamental components that make up a neural network (the neuron)
- Utilize TensorFlow from Google to program a neural network
- Utilizing softmax, construct a neural network with an output that has K > 2 classes
- Define the words “activation,” “backpropagation,” and “feedforward” as they pertain to neural networks
- Set up TensorFlow

## Course Content

S.No. | Module (Duration) | Topics |
---|---|---|

1. | Welcome (27 minutes) | Introduction and Outline |

Where to get the code | ||

How to Succeed in this Course | ||

2. | Review (30 minutes) | Review Section Introduction |

What does machine learning do? | ||

Neuron Predictions | ||

Neuron Training | ||

Deep Learning Readiness Test | ||

Review Section Summary | ||

3. | Preliminaries: From Neurons to Neural Networks (13 minutes) | Neural Networks with No Math |

Introduction to the E-Commerce Course Project | ||

4. | Classifying more than 2 things at a time (01 hour 23 minutes) | Prediction: Section Introduction and Outline |

From Logistic Regression to Neural Networks | ||

Interpreting the Weights of a Neural Network | ||

Softmax | ||

Sigmoid vs. Softmax | ||

Feedforward in Slow-Mo (part 1) | ||

Feedforward in Slow-Mo (part 2) | ||

Where to get the code for this course | ||

Softmax in Code | ||

Building an entire feedforward neural network in Python | ||

E-Commerce Course Project: Pre-Processing the Data | ||

E-Commerce Course Project: Making Predictions | ||

Prediction Quizzes | ||

Prediction: Section Summary | ||

Suggestion Box | ||

5. | Training a neural network (02 hours 14 minutes) | Training: Section Introduction and Outline |

What do all these symbols and letters mean? | ||

What does it mean to “train” a neural network? | ||

How to Brace Yourself to Learn Backpropagation | ||

Categorical Cross-Entropy Loss Function | ||

Training Logistic Regression with Softmax (part 1) | ||

Training Logistic Regression with Softmax (part 2) | ||

Backpropagation (part 1) | ||

Backpropagation (part 2) | ||

Backpropagation in code | ||

Backpropagation (part 3) | ||

The WRONG Way to Learn Backpropagation | ||

E-Commerce Course Project: Training Logistic Regression with Softmax | ||

E-Commerce Course Project: Training a Neural Network | ||

Training Quiz | ||

Training: Section Summary | ||

6. | Practical Machine Learning (48 minutes) | Practical Issues: Section Introduction and Outline |

Donut and XOR Review | ||

Donut and XOR Revisited | ||

Neural Networks for Regression | ||

Common nonlinearities and their derivatives | ||

Practical Considerations for Choosing Activation Functions | ||

Hyperparameters and Cross-Validation | ||

Manually Choosing Learning Rate and Regularization Penalty | ||

Why Divide by Square Root of D? | ||

Practical Issues: Section Summary | ||

7. | TensorFlow, Exercises, Practice, and What to Learn Next (53 minutes) | TensorFlow plug-and-play example |

Visualizing what a neural network has learned using TensorFlow Playground | ||

Where to go from here | ||

You know more than you think you know | ||

How to get good at deep learning + exercises | ||

Deep neural networks in just 3 lines of code with Sci-Kit Learn | ||

8. | Project: Facial Expression Recognition (01 hour 02 minutes) | Facial Expression Recognition Project Introduction |

Facial Expression Recognition Problem Description | ||

The class imbalance problem | ||

Utilities walkthrough | ||

Facial Expression Recognition in Code (Binary / Sigmoid) | ||

Facial Expression Recognition in Code (Logistic Regression Softmax) | ||

Facial Expression Recognition in Code (ANN Softmax) | ||

Facial Expression Recognition Project Summary | ||

9. | Backpropagation Supplementary Lectures (30 minutes) | Backpropagation Supplementary Lectures Introduction |

Why Learn the Ins and Outs of Backpropagation? | ||

Gradient Descent Tutorial | ||

Help with Softmax Derivative | ||

Backpropagation with Softmax Troubleshooting | ||

10. | Higher-Level Discussion (38 minutes) | What’s the difference between “neural networks” and “deep learning”? |

Who should take this course in 2020 and beyond? | ||

Who should learn backpropagation in 2020 and beyond? | ||

Where does this course fit into your deep learning studies? | ||

11. | Setting Up Your Environment (FAQ by Student Request) (37 minutes) | Anaconda Environment Setup |

How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow | ||

12. | Extra Help With Python Coding for Beginners (FAQ by Student Request) (45 minutes) | How to Uncompress a .tar.gz file |

How to Code by Yourself (part 1) | ||

How to Code by Yourself (part 2) | ||

Proof that using Jupyter Notebook is the same as not using it | ||

Python 2 vs Python 3 | ||

13. | Effective Learning Strategies for Machine Learning (FAQ by Student Request) (01 hour 04 minutes) | How to Succeed in this Course (Long Version) |

Is this for Beginners or Experts? Academic or Practical? Fast or slow-paced? | ||

Where does this course fit into your deep learning studies? (Old Version) | ||

Machine Learning and AI Prerequisite Roadmap (pt 1) | ||

Machine Learning and AI Prerequisite Roadmap (pt 2) | ||

14. | Appendix / FAQ Finale (08 minutes) | What is the Appendix? |

BONUS |

## Resources Required

- Simple math (calculus derivatives, matrix arithmetic, probability)
- Install Python and Numpy
- Basic familiarity with concepts like cross-entropy cost, gradient descent, neurons, XOR, and donut)

## Featured Review

**Saurabh Mishra (5/5)**: The course is very well designed for beginners to intermediate-level students. I am a PhD student and I am really happy and thankful to udemy for providing this course at this price. The course has provided in-depth knowledge with all technical detail. The best part of the course is that they also provided Jupyter Notebook and Datasets for the experiments. Thank you Udemy and Lazyprogrammer once again.

## Pros

**Kaizen F (5/5)**: Great course! I thought the level of math / getting you to work things out for yourself was perfect.**Jim Yuzwalk (5/5)**: Excellent handling of logistic regression and how it compares to neural networks.**Ivan Doskovic (5/5)**: This course is simply superb if you have some experience with linear and logistic regression as well as Python.**Giannis Polyzos (5/5)**: The instructor has in depth knowledge and combines in a perfect way theory and implementation.

## Cons

**Margherita Di Leo (1/5)**: Except that it’s soo boring that I could not get further section 5.**Karan (1/5)**: Explanations are a bit ambiguous and people with no background in deep learning will find it very difficult to follow along.**Alan Mackey (1/5)**: just disappointed because I have seen this explained in far easier way by just understanding that forward feeding can be seen as a composite function.**Robert Ledang (1/5)**: Unfortunately, a lot of time is spent on saying what the course is not about over and over again.

## About the Author

The instructor of this course is Lazy Programmer Inc. who is an Artificial Intelligence and Machine Learning Engineer. With a 4.6 instructor rating and 1,41,144 reviews on Udemy, he offers 32 courses and has taught 5,12,747 students so far.

- Although he has also been recognized as a Data Scientist, Big Data Engineer, and Full Stack Software Engineer, he currently spends the majority of his time as an Artificial Intelligence and Machine Learning Engineer with an emphasis on Deep Learning.
- He earned his first master’s degree in Computer Engineering with a focus on Machine Learning and pattern recognition more than ten years ago.
- His second master’s degree is in statistics with a focus on financial engineering.
- He is a Data Scientist and Big Data Engineer with experience in online advertising and digital media (optimizing click and conversion rates, and building data processing pipelines).
- He routinely uses Big Data technologies like Hadoop, Pig, Hive, MapReduce, and Spark.
- He has developed Deep Learning models for text modeling, image and signal processing, user behavior prediction, and click-through rate estimation.
- He has instructed students at universities like Columbia University, NYU, Hunter College, and The New School in Data Science, Statistics, Machine Learning, Algorithms, Calculus, Computer Graphics, and Physics.
- He handles all of the server-side backend work, frontend HTML/JS/CSS work, and operations/deployment work.

## Comparison Table

Parameters | Data Science: Deep Learning and Neural Networks in Python | Data Science: Natural Language Processing (NLP) in Python | Natural Language Processing with Deep Learning in Python |
---|---|---|---|

Offers | INR 455 (87% off | INR 455 (87% off | INR 455 (87% off |

Duration | 11.5 hours | 12 hours | 12 hours |

Rating | 4.7 /5 | 4.6 /5 | 4.6 /5 |

Student Enrollments | 51,314 | 43,199 | 42,420 |

Instructors | Lazy Programmer Inc. | Lazy Programmer Inc. | Lazy Programmer Inc. |

Register Here | Apply Now! | Apply Now! | Apply Now! |

## Data Science: Deep Learning and Neural Networks in Python: FAQs

**Ques. Is OpenCV part of deep learning?**

**Ans.** OpenCV is used to identify objects, people, diseases, lesions, license plates, and even handwriting in a variety of pictures and movies. It employs vector space and performs mathematical operations on these features to recognize visual patterns and their various features using OpenCV in Deep Learning.

**Ques. Why Python is best for Machine Learning?**

**Ans.** Python provides code that is clear and readable. Python’s simplicity enables developers to create dependable systems, but machine learning and AI are powered by complicated algorithms and flexible workflows. Instead of concentrating on the technical details of the language, developers get to spend all of their efforts on solving an ML problem.

**Ques. Is Python good for deep learning?**

**Ans.** The advantages that make Python the best choice for projects based on machine learning and AI include flexibility, platform freedom, access to excellent libraries and frameworks for ML, and a large community. These increase the language’s general appeal.

**Ques. Is C++ good for deep learning?**

**Ans.** Compared to most other languages, C++ is more productive. Every single resource, including memory, the CPU, and several other things, is within your control. The majority of frameworks, including TensorFlow, Caffe, Vowpal, wabbit, and libsvm, are internally implemented in C++.

**Ques. Can neural networks be used for deep learning?**

**Ans.** An artificial intelligence technique called a neural network instructs computers to analyse data in a manner modelled after the human brain. Deep learning is a sort of machine learning that employs interconnected neurons or nodes in a layered framework to mimic the human brain.

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