Deep Learning with Python and Keras

Deep Learning with Python and Keras, available at $99.99, has an average rating of 4.23, with 150 lectures, based on 3275 reviews, and has 24419 subscribers.

You will learn about 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) This course is ideal for individuals who are Software engineers who are curious about data science and about the Deep Learning buzz and want to get a better understanding of it or Data scientists who are familiar with Machine Learning and want to develop a strong foundational knowledge of deep learning It is particularly useful for Software engineers who are curious about data science and about the Deep Learning buzz and want to get a better understanding of it or Data scientists who are familiar with Machine Learning and want to develop a strong foundational knowledge of deep learning.

Enroll now: Deep Learning with Python and Keras

Summary

Title: Deep Learning with Python and Keras

Price: $99.99

Average Rating: 4.23

Number of Lectures: 150

Number of Published Lectures: 148

Number of Curriculum Items: 150

Number of Published Curriculum Objects: 148

Original Price: $199.99

Quality Status: approved

Status: Live

What You Will 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)

Who Should Attend

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

Target Audiences

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

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.

Course Curriculum

Chapter 1: Welcome to the course!

Lecture 1: Welcome to the course!

Lecture 2: Introduction

Lecture 3: Real world applications of deep learning

Lecture 4: Download and install Anaconda

Lecture 5: Installation Video Guide

Lecture 6: Obtain the code for the course

Lecture 7: Course Folder Walkthrough

Lecture 8: Your first deep learning model

Chapter 2: Data

Lecture 1: Section 2 Intro

Lecture 2: Tabular data

Lecture 3: Data exploration with Pandas code along

Lecture 4: Visual data Exploration

Lecture 5: Plotting with Matplotlib

Lecture 6: Unstructured Data

Lecture 7: Images and Sound in Jupyter

Lecture 8: Feature Engineering

Lecture 9: Exercise 1 Presentation

Lecture 10: Exercise 1 Solution

Lecture 11: Exercise 2 Presentation

Lecture 12: Exercise 2 Solution

Lecture 13: Exercise 3 Presentation

Lecture 14: Exercise 3 Solution

Lecture 15: Exercise 4 Presentation

Lecture 16: Exercise 4 Solution

Lecture 17: Exercise 5 Presentation

Lecture 18: Exercise 5 Solution

Chapter 3: Machine Learning

Lecture 1: Section 3 Intro

Lecture 2: Machine Learning Problems

Lecture 3: Supervised Learning

Lecture 4: Linear Regression

Lecture 5: Cost Function

Lecture 6: Cost Function code along

Lecture 7: Finding the best model

Lecture 8: Linear Regression code along

Lecture 9: Evaluating Performance

Lecture 10: Evaluating Performance code along

Lecture 11: Classification

Lecture 12: Classification code along

Lecture 13: Overfitting

Lecture 14: Cross Validation

Lecture 15: Cross Validation code along

Lecture 16: Confusion matrix

Lecture 17: Confusion Matrix code along

Lecture 18: Feature Preprocessing code along

Lecture 19: Exercise 1 Presentation

Lecture 20: Exercise 1 solution

Lecture 21: Exercise 2 Presentation

Lecture 22: Exercise 2 solution

Chapter 4: Deep Learning Intro

Lecture 1: Section 4 Intro

Lecture 2: Deep Learning successes

Lecture 3: Neural Networks

Lecture 4: Deeper Networks

Lecture 5: Neural Networks code along

Lecture 6: Multiple Outputs

Lecture 7: Multiclass classification code along

Lecture 8: Activation Functions

Lecture 9: Feed forward

Lecture 10: Exercise 1 Presentation

Lecture 11: Exercise 1 Solution

Lecture 12: Exercise 2 Presentation

Lecture 13: Exercise 2 Solution

Lecture 14: Exercise 3 Presentation

Lecture 15: Exercise 3 Solution

Lecture 16: Exercise 4 Presentation

Lecture 17: Exercise 4 Solution

Chapter 5: Gradient Descent

Lecture 1: Section 5 Intro

Lecture 2: Derivatives and Gradient

Lecture 3: Backpropagation intuition

Lecture 4: Chain Rule

Lecture 5: Derivative Calculation

Lecture 6: Fully Connected Backpropagation

Lecture 7: Matrix Notation

Lecture 8: Numpy Arrays code along

Lecture 9: Learning Rate

Lecture 10: Learning Rate code along

Lecture 11: Gradient Descent

Lecture 12: Gradient Descent code along

Lecture 13: EWMA

Lecture 14: Optimizers

Lecture 15: Optimizers code along

Lecture 16: Initialization code along

Lecture 17: Inner Layers Visualization code along

Lecture 18: Exercise 1 Presentation

Lecture 19: Exercise 1 Solution

Lecture 20: Exercise 2 Presentation

Lecture 21: Exercise 2 Solution

Lecture 22: Exercise 3 Presentation

Lecture 23: Exercise 3 Solution

Lecture 24: Exercise 4 Presentation

Lecture 25: Exercise 4 Solution

Lecture 26: Tensorboard

Chapter 6: Convolutional Neural Networks

Lecture 1: Section 6 Intro

Lecture 2: Features from Pixels

Lecture 3: MNIST Classification

Instructors

Data Weekends
Learn the essentials of Data Science in just one weekend
Jose Portilla
Head of Data Science at Pierian Training
Francesco Mosconi
CEO at Catalit & Zero to Deep Learning
Pierian Training
Data Science and Machine Learning Training

Rating Distribution

1 stars: 46 votes
2 stars: 95 votes
3 stars: 369 votes
4 stars: 1233 votes
5 stars: 1531 votes

Frequently Asked Questions

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