The ‘Deep Learning with Python and Keras’ course gives a thorough introduction to deep learning. The course begin by reviewing Deep Learning applications and giving a quick rundown of machine learning tools and methods. The course describes what Deep Learning is in a simple yet accurate way.

By the end of the course, you will be able to identify issues that Deep Learning can answer, develop and train a range of Neural Network models, and use cloud computing to expedite training and enhance the performance of your model. The course is usually available for **INR 2,699** on Udemy but students can click on the link and get the ‘**Deep Learning with Python and Keras**’ for **INR 449**.

## Who all can opt for this course?

- Software developers those are interested in learning more about data science and the buzz surrounding deep learning
- Anyone who wish to have a solid basic understanding of deep learning and are experienced with machine learning

## Course Highlights

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

Registration Link | Apply Now! |

Price | INR 449 (INR 2,699) 80% off |

Duration | 10 Hours |

Rating | 4.2/5 |

Student Enrollment | 23,432 students |

Instructor | Data Weekends https://www.linkedin.com/in/dataweekends |

Topics Covered | Machine Learning, Deep Learning, Cloud GPUs, Neural Networks |

Course Level | Intermediate |

Total Student Reviews | 3,132 |

## Learning Outcomes

- To succinctly and precisely define what deep learning is
- To describe how predictive models can be created using deep learning
- T o determine which applications in the real world can profit from deep learning
- Create deep learning models using Python and Keras then install them
- Using deep learning to handle supervised and unsupervised learning issues using time series, tabular data, text, and pictures
- To train and build fully connected, convolutional, and recurrent neural networks
- To be able to adjust a deep learning model’s parameters while being able to examine its internals without feeling intimidated
- To utilise a GPU to train and run models in the cloud
- To calculate the expense of training huge models
- To reuse previously trained models in order to reduce training costs and time (transfer learning)

## Course Content

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

1. | Welcome to the course! (37 minutes) | Welcome to the course! |

Introduction | ||

Real world applications of deep learning | ||

Download and install Anaconda | ||

Installation Video Guide | ||

Obtain the code for the course | ||

Course Folder Walkthrough | ||

Your first deep learning model | ||

2. | Data (01 hour 04 minutes) | Section 2 Intro |

Tabular data | ||

Data exploration with Pandas code along | ||

Visual data Exploration | ||

Plotting with Matplotlib | ||

Unstructured Data | ||

Images and Sound in Jupyter | ||

Feature Engineering | ||

Exercise 1 Presentation | ||

Exercise 1 Solution | ||

Exercise 2 Presentation | ||

Exercise 2 Solution | ||

Exercise 3 Presentation | ||

Exercise 3 Solution | ||

Exercise 4 Presentation | ||

Exercise 4 Solution | ||

Exercise 5 Presentation | ||

Exercise 5 Solution | ||

3. | Machine Learning (02 hours 04 minutes) | Section 3 Intro |

Machine Learning Problems | ||

Supervised Learning | ||

Linear Regression | ||

Cost Function | ||

Cost Function code along | ||

Finding the best model | ||

Linear Regression code along | ||

Evaluating Performance | ||

Evaluating Performance code along | ||

Classification | ||

Classification code along | ||

Overfitting | ||

Cross Validation | ||

Cross Validation code along | ||

Confusion matrix | ||

Confusion Matrix code along | ||

Feature Preprocessing code along | ||

Exercise 1 Presentation | ||

Exercise 1 solution | ||

Exercise 2 Presentation | ||

Exercise 2 solution | ||

4. | Deep Learning Intro (01 hour 16 minutes) | Section 4 Intro |

Deep Learning successes | ||

Neural Networks | ||

Deeper Networks | ||

Neural Networks code along | ||

Multiple Outputs | ||

Multiclass classification code along | ||

Activation Functions | ||

Feed forward | ||

Exercise 1 Presentation | ||

Exercise 1 Solution | ||

Exercise 2 Presentation | ||

Exercise 2 Solution | ||

Exercise 3 Presentation | ||

Exercise 3 Solution | ||

Exercise 4 Presentation | ||

Exercise 4 Solution | ||

5. | Gradient Descent (01 hour 45 minutes) | Section 5 Intro |

Derivatives and Gradient | ||

Backpropagation intuition | ||

Chain Rule | ||

Derivative Calculation | ||

Fully Connected Backpropagation | ||

Matrix Notation | ||

Numpy Arrays code along | ||

Learning Rate | ||

Learning Rate code along | ||

Gradient Descent | ||

Gradient Descent code along | ||

EWMA | ||

Optimizers | ||

Optimizers code along | ||

Initialization code along | ||

Inner Layers Visualization code along | ||

Exercise 1 Presentation | ||

Exercise 1 Solution | ||

Exercise 2 Presentation | ||

Exercise 2 Solution | ||

Exercise 3 Presentation | ||

Exercise 3 Solution | ||

Exercise 4 Presentation | ||

Exercise 4 Solution | ||

Tensorboard | ||

6. | Convolutional Neural Networks (01 hour 20 minutes) | Section 6 Intro |

Features from Pixels | ||

MNIST Classification | ||

MNIST Classification code along | ||

Beyond Pixels | ||

Images as Tensors | ||

Tensor Math code along | ||

Convolution in 1 D | ||

Convolution in 1 D code along | ||

Convolution in 2 D | ||

Image Filters code along | ||

Convolutional Layers | ||

Convolutional Layers code along | ||

Pooling Layers | ||

Pooling Layers code along | ||

Convolutional Neural Networks | ||

Convolutional Neural Networks code along | ||

Weights in CNNs | ||

Beyond Images | ||

Exercise 1 Presentation | ||

Exercise 1 Solution | ||

Exercise 2 Presentation | ||

Exercise 2 Solution | ||

7. | Cloud GPUs (01 minutes) | Google Colaboratory GPU notebook setup |

Floyd GPU notebook setup | ||

8. | Recurrent Neural Networks (45 minutes) | Section 8 Intro |

Time Series | ||

Sequence problems | ||

Vanilla RNN | ||

LSTM and GRU | ||

Time Series Forecasting code along | ||

Time Series Forecasting with LSTM code along | ||

Rolling Windows | ||

Rolling Windows code along | ||

Exercise 1 Presentation | ||

Exercise 1 Solution | ||

Exercise 2 Presentation | ||

Exercise 2 Solution | ||

9. | Improving performance (01 hour 01 minutes) | Section 9 Intro |

Learning curves | ||

Learning curves code along | ||

Batch Normalization | ||

Batch Normalization code along | ||

Dropout | ||

Dropout and Regularization code along | ||

Data Augmentation | ||

Continuous Learning | ||

Image Generator code along | ||

Hyperparameter search | ||

Embeddings | ||

Embeddings code along | ||

Movies Reviews Sentiment Analysis code along | ||

Exercise 1 Presentation | ||

Exercise 1 Solution | ||

Exercise 2 Presentation | ||

Exercise 2 Solution | ||

Exercise 3 Presentation |

## Resources Required

- Experience with control flow (if/else, for loops), Python knowledge, and pythonic constructions (functions, classes, iterables, generators)
- To copy and transfer files, use the bash shell (or a comparable command prompt) and simple commands
- Basic familiarity with linear algebra (what is a vector, what is a matrix, how to calculate dot product)
- Connecting to a cloud machine using SSH

## Featured Review

**Swapnil Jadhav (5/5) ** : This is one of the best courses I have ever completed. I knew the concepts but coding and analyzing them was the problem for me. With this course, now I can implement and prototype my ideas better and faster with keras. Looking forward for other relevent courses from “Data Weekends”.

## Pros

**Noriyuki Nakae (5/5)**: This is the best Deap Learning course in Udemy I have ever taken.**kim (5/5)**: This lecture explains functions being used in ML perfectly, including both codes along and meanings taught in statistics classes.**Neal Cariello (5/5)**: I’m about 15% through and the pace and detail are perfect.**Gabriela Surpi (5/5)**: Wonderful explanation on how to setup a separate environment with all the packages and versions that will be needed and that work well together.

## Cons

**Francisco Ramos (2/5)**: Unfortunately, the fact that the course is not fully finished and Q&A seems to be abandoned, I feel a bit disappointed and I have no other option but giving it only 2 stars.**David Aubin (2/5)**: I think if he had a qualified reviewer, review the class and fix the missing pieces.**Eric Peeters (1/5)**: Course is not complete (final solution sections are missing), unbalanced (some simple topics get a lot of coverage, complicated topics are rushed).**Abhijit Bhattacharya (2/5)**: I’m comparing this with machine learning A to Z which was extremely well delivered

## About the Author

The instructor of this course is Data Weekends who is a Learn the essentials of Data Science in just one weekend. With 4.4 Instructor Rating and 3,174 Reviews on Udemy, he/she offers 3 Courses and has taught 24,661 Students so far.

- Data WeekendsTM are accelerated data science workshops for programmers that teach you how to use predictive analytics on actual data in a short amount of time
- Data analytics, machine learning, deep learning, and reinforcement learning are all topics that are discussed in the course
- They also provide business training and consultancy on data science, machine learning, and deep learning through their parent firm, Catalit LLC
- Francesco Mosconi, PhD, is the founder and principal lecturer of Data Weekends

## Comparison Table

Parameters | Deep Learning with Python and Keras | Artificial Intelligence: Reinforcement Learning in Python | Advanced AI: Deep Reinforcement Learning in Python |
---|---|---|---|

Offers | INR 455 (80% off | INR 455 (87% off | INR 455 (87% off |

Duration | 10 hours | 14.5 hours | 10.5 hours |

Rating | 4.2/5 | 4.8/5 | 4.6/5 |

Student Enrollments | 23,414 | 43,648 | 36,737 |

Instructors | Data Weekends | Lazy Programmer Team | Lazy Programmer Team |

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

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