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30-Day Money-Back Guarantee
Development Data Science Deep Learning

Deep Learning with Python and Keras

Understand and build Deep Learning models for images, text and more using Python and Keras
Bestseller
Rating: 4.5 out of 54.5 (2,725 ratings)
20,797 students
Created by Data Weekends, Jose Portilla, Francesco Mosconi
Last updated 12/2018
English
English [Auto], Portuguese [Auto], 
30-Day Money-Back Guarantee

What you'll learn

  • To describe what Deep Learning is in a simple yet accurate way
  • To explain how deep learning can be used to build predictive models
  • To distinguish which practical applications can benefit from deep learning
  • To install and use Python and Keras to build deep learning models
  • To apply deep learning to solve supervised and unsupervised learning problems involving images, text, sound, time series and tabular data.
  • To build, train and use fully connected, convolutional and recurrent neural networks
  • To look at the internals of a deep learning model without intimidation and with the ability to tweak its parameters
  • To train and run models in the cloud using a GPU
  • To estimate training costs for large models
  • To re-use pre-trained models to shortcut training time and cost (transfer learning)
Curated for the Udemy for Business collection

Course content

9 sections • 148 lectures • 9h 56m total length

  • Preview01:13
  • Preview01:31
  • Preview09:29
  • Preview02:35
  • Preview06:21
  • Obtain the code for the course
    00:29
  • Course Folder Walkthrough
    05:02
  • Your first deep learning model
    10:31

  • Preview01:00
  • Tabular data
    06:06
  • Data exploration with Pandas code along
    10:47
  • Visual data Exploration
    04:54
  • Plotting with Matplotlib
    10:56
  • Unstructured Data
    04:59
  • Images and Sound in Jupyter
    05:14
  • Feature Engineering
    02:42
  • Exercise 1 Presentation
    01:45
  • Exercise 1 Solution
    03:17
  • Exercise 2 Presentation
    01:04
  • Exercise 2 Solution
    04:06
  • Exercise 3 Presentation
    00:56
  • Exercise 3 Solution
    01:53
  • Exercise 4 Presentation
    00:48
  • Exercise 4 Solution
    01:36
  • Exercise 5 Presentation
    01:10
  • Exercise 5 Solution
    01:45

  • Preview01:45
  • Machine Learning Problems
    03:41
  • Supervised Learning
    05:13
  • Linear Regression
    04:46
  • Cost Function
    03:23
  • Cost Function code along
    06:26
  • Finding the best model
    02:43
  • Linear Regression code along
    10:56
  • Evaluating Performance
    05:04
  • Evaluating Performance code along
    04:31
  • Classification
    07:45
  • Classification code along
    07:45
  • Overfitting
    05:01
  • Cross Validation
    06:22
  • Cross Validation code along
    04:18
  • Confusion matrix
    05:57
  • Confusion Matrix code along
    03:29
  • Feature Preprocessing code along
    06:00
  • Exercise 1 Presentation
    02:34
  • Exercise 1 solution
    11:37
  • Exercise 2 Presentation
    02:40
  • Exercise 2 solution
    12:15

  • Preview01:23
  • Deep Learning successes
    04:36
  • Neural Networks
    05:21
  • Deeper Networks
    03:55
  • Neural Networks code along
    06:25
  • Multiple Outputs
    05:28
  • Multiclass classification code along
    09:14
  • Activation Functions
    04:42
  • Feed forward
    05:20
  • Exercise 1 Presentation
    01:41
  • Exercise 1 Solution
    07:25
  • Exercise 2 Presentation
    01:28
  • Exercise 2 Solution
    08:12
  • Exercise 3 Presentation
    01:28
  • Exercise 3 Solution
    03:14
  • Exercise 4 Presentation
    01:03
  • Exercise 4 Solution
    05:44

  • Preview01:22
  • Derivatives and Gradient
    05:26
  • Backpropagation intuition
    03:58
  • Chain Rule
    04:17
  • Derivative Calculation
    03:44
  • Fully Connected Backpropagation
    03:58
  • Matrix Notation
    04:20
  • Numpy Arrays code along
    07:33
  • Learning Rate
    02:01
  • Learning Rate code along
    09:24
  • Gradient Descent
    03:27
  • Gradient Descent code along
    03:42
  • EWMA
    04:12
  • Optimizers
    04:17
  • Optimizers code along
    04:16
  • Initialization code along
    04:33
  • Inner Layers Visualization code along
    08:16
  • Exercise 1 Presentation
    01:22
  • Exercise 1 Solution
    05:22
  • Exercise 2 Presentation
    01:09
  • Exercise 2 Solution
    03:51
  • Exercise 3 Presentation
    01:30
  • Exercise 3 Solution
    04:17
  • Exercise 4 Presentation
    01:49
  • Exercise 4 Solution
    03:39
  • Tensorboard
    03:29

  • Preview01:35
  • Features from Pixels
    03:37
  • MNIST Classification
    01:26
  • MNIST Classification code along
    06:12
  • Beyond Pixels
    03:19
  • Images as Tensors
    05:24
  • Tensor Math code along
    06:14
  • Convolution in 1 D
    03:08
  • Convolution in 1 D code along
    01:36
  • Convolution in 2 D
    03:22
  • Image Filters code along
    02:27
  • Convolutional Layers
    06:15
  • Convolutional Layers code along
    06:19
  • Pooling Layers
    01:32
  • Pooling Layers code along
    01:56
  • Convolutional Neural Networks
    02:12
  • Convolutional Neural Networks code along
    05:51
  • Weights in CNNs
    02:46
  • Beyond Images
    02:39
  • Exercise 1 Presentation
    02:02
  • Exercise 1 Solution
    04:12
  • Exercise 2 Presentation
    02:55
  • Exercise 2 Solution
    03:34

  • Google Colaboratory GPU notebook setup
    00:31
  • Floyd GPU notebook setup
    00:37

  • Preview01:05
  • Time Series
    05:27
  • Sequence problems
    04:47
  • Vanilla RNN
    02:57
  • LSTM and GRU
    06:17
  • Time Series Forecasting code along
    06:31
  • Time Series Forecasting with LSTM code along
    03:44
  • Rolling Windows
    02:39
  • Rolling Windows code along
    06:45
  • Exercise 1 Presentation
    01:22
  • Exercise 1 Solution
    02:27
  • Exercise 2 Presentation
    01:12
  • Exercise 2 Solution
    00:00

  • Preview00:52
  • Learning curves
    03:02
  • Learning curves code along
    07:05
  • Batch Normalization
    01:58
  • Batch Normalization code along
    05:41
  • Dropout
    02:53
  • Dropout and Regularization code along
    02:40
  • Data Augmentation
    02:57
  • Continuous Learning
    02:53
  • Image Generator code along
    06:15
  • Hyperparameter search
    04:04
  • Embeddings
    03:32
  • Embeddings code along
    02:34
  • Movies Reviews Sentiment Analysis code along
    10:53
  • Exercise 1 Presentation
    01:08
  • Exercise 1 Solution
    00:00
  • Exercise 2 Presentation
    00:53
  • Exercise 2 Solution
    00:00
  • Exercise 3 Presentation
    02:20

Requirements

  • Knowledge of Python, familiarity with control flow (if/else, for loops) and pythonic constructs (functions, classes, iterables, generators)
  • Use of bash shell (or equivalent command prompt) and basic commands to copy and move files
  • Basic knowledge of linear algebra (what is a vector, what is a matrix, how to calculate dot product)
  • Use of ssh to connect to a cloud computer

Description

This course is designed to provide a complete introduction to Deep Learning. It is aimed at beginners and intermediate programmers and data scientists who are familiar with Python and want to understand and apply Deep Learning techniques to a variety of problems.

We start with a review of Deep Learning applications and a recap of Machine Learning tools and techniques. Then we introduce Artificial Neural Networks and explain how they are trained to solve Regression and Classification problems.

Over the rest of the course we introduce and explain several architectures including Fully Connected, Convolutional and Recurrent Neural Networks, and for each of these we explain both the theory and give plenty of example applications.

This course is a good balance between theory and practice. We don't shy away from explaining mathematical details and at the same time we provide exercises and sample code to apply what you've just learned.

The goal is to provide students with a strong foundation, not just theory, not just scripting, but both. At the end of the course you'll be able to recognize which problems can be solved with Deep Learning, you'll be able to design and train a variety of Neural Network models and you'll be able to use cloud computing to speed up training and improve your model's performance.


Who this course is for:

  • Software engineers who are curious about data science and about the Deep Learning buzz and want to get a better understanding of it
  • Data scientists who are familiar with Machine Learning and want to develop a strong foundational knowledge of deep learning

Featured review

Pawan Methre
Pawan Methre
4 courses
3 reviews
Rating: 5.0 out of 59 months ago
awesome tutorial, nice tutorial to kickstart for deep deep learning, but i found that there are over thousands of questions not answered yet on QA, even i posted many questions not found any response since ages...why ????

Instructors

Data Weekends
Learn the essentials of Data Science in just one weekend
Data Weekends
  • 4.5 Instructor Rating
  • 2,725 Reviews
  • 20,797 Students
  • 1 Course

Data Weekends™ are accelerated data science workshop for programmers where you can quickly learn to apply predictive analytics to real-world data. We offer courses in Data Analytics, Machine Learning, Deep Learning and Reinforcement Learning.

Through our parent company Catalit LLC we also offer corporate training and consulting on Data Science, Machine Learning and Deep Learning.

Data Weekends' founder and lead instructor is Francesco Mosconi, PhD.

Jose Portilla
Head of Data Science, Pierian Data Inc.
Jose Portilla
  • 4.6 Instructor Rating
  • 721,648 Reviews
  • 2,206,162 Students
  • 32 Courses

  Jose Marcial Portilla has a BS and MS in Mechanical Engineering from Santa Clara University and years of experience as a professional instructor and trainer for Data Science and programming. He has publications and patents in various fields such as microfluidics, materials science, and data science technologies. Over the course of his career he has developed a skill set in analyzing data and he hopes to use his experience in teaching and data science to help other people learn the power of programming the ability to analyze data, as well as present the data in clear and beautiful visualizations. Currently he works as the Head of Data Science for Pierian Data Inc. and provides in-person data science and python programming training courses to employees working at top companies, including General Electric, Cigna, The New York Times, Credit Suisse, McKinsey and many more. Feel free to contact him on LinkedIn for more information on in-person training sessions or group training sessions in Las Vegas, NV.

Francesco Mosconi
Francesco Mosconi
  • 4.5 Instructor Rating
  • 2,725 Reviews
  • 20,797 Students
  • 1 Course

Francesco is a Data Science consultant and trainer. With Catalit LLC he helps companies acquire skills and knowledge in data science and harness the power of machine learning and deep learning to reach their goals

Before Data Weekends, Francesco served as lead instructor in Data Science at General Assembly and The Data Incubator and he was Chief Data Officer and co-­founder at Spire, a YCombinator-­backed startup company that invented the first consumer wearable device capable of continuously tracking respiration and activity.

He earned a joint PhD in biophysics at University of Padua and Université de Paris VI and is also a graduate of Singularity University summer program of 2011.

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