Python

In this track, you'll expand your deep learning knowledge and take your machine learning skills to the next level. Working with Keras and PyTorch, you’ll learn about neural networks, the deep learning model workflows, and how to optimize your models. You'll then use TensorFlow to build linear regression models and neural networks. Throughout the track, you'll use machine learning techniques to solve real-world challenges, such as predicting housing prices, building a neural network to predict handwritten numbers, and identify forged banknotes.  By the end of the track, you'll be ready to use Keras to train and test complex, multi-output networks and dive deeper into deep learning.

Take your machine learning skills to the next level. Use the powerful Keras, TensorFlow, and PyTorch libraries to create and optimize neural networks.

in Python

Deep Learning

In this chapter, you'll become familiar with the fundamental concepts and terminology used in deep learning, and understand why deep learning techniques are so powerful today. You'll build simple neural networks and generate predictions with them.

Basics of deep learning and neural networks

Learn how to optimize the predictions generated by your neural networks. You'll use a method called backward propagation, which is one of the most important techniques in deep learning. Understanding how it works will give you a strong foundation to build on in the second half of the course.

Optimizing a neural network with backward propagation

In this chapter, you'll use the Keras library to build deep learning models for both regression and classification. You'll learn about the Specify-Compile-Fit workflow that you can use to make predictions, and by the end of the chapter, you'll have all the tools necessary to build deep neural networks.

Building deep learning models with keras

Learn how to optimize your deep learning models in Keras. Start by learning how to validate your models, then understand the concept of model capacity, and finally, experiment with wider and deeper networks. 

Fine-tuning keras models

Hourly wages

MNIST

Titanic

Deep learning is the machine learning technique behind the most exciting capabilities in diverse areas like robotics, natural language processing, image recognition, and artificial intelligence, including the famous AlphaGo. In this course, you'll gain hands-on, practical knowledge of how to use deep learning with Keras 2.0, the latest version of a cutting-edge library for deep learning in Python.

Supervised Learning with scikit-learn

Introduction to Deep Learning in Python

Learn the fundamentals of neural networks and how to build deep learning models using Keras 2.0.

Machine Learning

Before you can build advanced models in TensorFlow 2, you will first need to understand the basics. In this chapter, you’ll learn how to define constants and variables, perform tensor addition and multiplication, and compute derivatives. Knowledge of linear algebra will be helpful, but not necessary.

Introduction to TensorFlow

In this chapter, you will learn how to build, solve, and make predictions with models in TensorFlow 2. You will focus on a simple class of models – the linear regression model – and will try to predict housing prices. By the end of the chapter, you will know how to load and manipulate data, construct loss functions, perform minimization, make predictions, and reduce resource use with batch training.

Linear models

The previous chapters taught you how to build models in TensorFlow 2. In this chapter, you will apply those same tools to build, train, and make predictions with neural networks. You will learn how to define dense layers, apply activation functions, select an optimizer, and apply regularization to reduce overfitting. You will take advantage of TensorFlow's flexibility by using both low-level linear algebra and high-level Keras API operations to define and train models.

Neural Networks

In the final chapter, you'll use high-level APIs in TensorFlow 2 to train a sign language letter classifier. You will use both the sequential and functional Keras APIs to train, validate, make predictions with, and evaluate models. You will also learn how to use the Estimators API to streamline the model definition and training process, and to avoid errors.

High Level APIs

King County House Sales

UCI Credit Card Default

Sign Language MNIST

Not long ago, cutting-edge computer vision algorithms couldn’t differentiate between images of cats and dogs. Today, a skilled data scientist equipped with nothing more than a laptop can classify tens of thousands of objects with greater accuracy than the human eye. In this course, you will use TensorFlow 2.4 to develop, train, and make predictions with the models that have powered major advances in recommendation systems, image classification, and FinTech. You will learn both high-level APIs, which will enable you to design and train deep learning models in 15 lines of code, and low-level APIs, which will allow you to move beyond off-the-shelf routines. You will also learn to accurately predict housing prices, credit card borrower defaults, and images of sign language gestures.

Learn the fundamentals of neural networks and how to build deep learning models using TensorFlow.

Introduction to TensorFlow in Python

In this first chapter, we introduce basic concepts of neural networks and deep learning using PyTorch library.

Introduction to PyTorch

In this second chapter, we delve deeper into Artificial Neural Networks, learning how to train them with real datasets.

Artificial Neural Networks

In this third chapter, we introduce convolutional neural networks, learning how to train them and how to use them to make predictions.

Convolutional Neural Networks (CNNs)

In this last chapter, we learn how to make neural networks work well in practice, using concepts like regularization, batch-normalization and transfer learning.

Using Convolutional Neural Networks

Neural networks have been at the forefront of Artificial Intelligence research during the last few years, and have provided solutions to many difficult problems like image classification, language translation or Alpha Go. PyTorch is one of the leading deep learning frameworks, being at the same time both powerful and easy to use. In this course you will use PyTorch to first learn about the basic concepts of neural networks, before building your first neural network to predict digits from MNIST dataset. You will then learn about convolutional neural networks, and use them to build much more powerful models which give more accurate results. You will evaluate the results and use different techniques to improve them. Following the course, you will be able to delve deeper into neural networks and start your career in this fascinating field.

Object-Oriented Programming in Python

Learn to create deep learning models with the PyTorch library.

Introduction to Deep Learning with PyTorch

In this first chapter, you will get introduced to neural networks, understand what kind of problems they can solve, and when to use them. You will also build several networks and save the earth by training a regression model that approximates the orbit of a meteor that is approaching us!

Introducing Keras

By the end of this chapter, you will know how to solve binary, multi-class, and multi-label problems with neural networks. All of this by solving problems like detecting fake dollar bills, deciding who threw which dart at a board, and building an intelligent system to water your farm. You will also be able to plot model training metrics and to stop training and save your models when they no longer improve.

Going Deeper

In the previous chapters, you've trained a lot of models! You will now learn how to interpret learning curves to understand your models as they train. You will also visualize the effects of activation functions, batch-sizes, and batch-normalization. Finally, you will learn how to perform automatic hyperparameter optimization to your Keras models using sklearn.

Improving Your Model Performance

It's time to get introduced to more advanced architectures! You will create an autoencoder to reconstruct noisy images, visualize convolutional neural network activations, use deep pre-trained models to classify images and learn more about recurrent neural networks and working with text as you build a network that predicts the next word in a sentence.

Advanced Model Architectures

Darts

Banknotes

Irrigation Machine

Digits

Deep learning is here to stay! It's the go-to technique to solve complex problems that arise with unstructured data and an incredible tool for innovation. Keras is one of the frameworks that make it easier to start developing deep learning models, and it's versatile enough to build industry-ready models in no time. In this course, you will learn regression and save the earth by predicting asteroid trajectories, apply binary classification to distinguish between real and fake dollar bills, use multiclass classification to decide who threw which dart at a dart board, learn to use neural networks to reconstruct noisy images and much more. Additionally, you will learn how to better control your models during training and how to tune them to boost their performance.

Learn to start developing deep learning models with Keras.

Introduction to Deep Learning with Keras

In this chapter, you'll become familiar with the basics of the Keras functional API.  You'll build a simple functional network using functional building blocks, fit it to data, and make predictions.

The Keras Functional API

In this chapter, you will build two-input networks that use categorical embeddings to represent high-cardinality data, shared layers to specify re-usable building blocks, and merge layers to join multiple inputs to a single output. By the end of this chapter, you will have the foundational building blocks for designing neural networks with complex data flows.

Two Input Networks Using Categorical Embeddings, Shared Layers, and Merge Layers

In this chapter, you will extend your 2-input model to 3 inputs, and learn how to use Keras' summary and plot functions to understand the parameters and topology of your neural networks. By the end of the chapter, you will understand how to extend a 2-input model to 3 inputs and beyond.

Multiple Inputs: 3 Inputs (and Beyond!)

In this chapter, you will build neural networks with multiple outputs, which can be used to solve regression problems with multiple targets.  You will also build a model that solves a regression problem and a classification problem simultaneously.

Multiple Outputs

Basketball data

Basketball models

This course shows you how to solve a variety of problems using the versatile Keras functional API. You will start with simple, multi-layer dense networks (also known as multi-layer perceptrons), and continue on to more complicated architectures. The course will cover how to build models with multiple inputs and a single output, as well as how to share weights between layers in a model. We will also cover advanced topics such as category embeddings and multiple-output networks. If you've ever wanted to train a network that does both classification and regression, then this course is for you!

Build multiple-input and multiple-output deep learning models using Keras.

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Deep Learning in Python

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Join over .css-w0wpus-TrackShowPage{color:#03ef62;}7,940,000 learners and start Deep Learning in Python today!

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Join over 7,940,000 learners and start Deep Learning in Python today!