TensorFlow in Practice
If you are a software developer who wants to build scalable AI-powered algorithms, you need to understand how to use the tools to build them. This Specialization will teach you best practices for using TensorFlow, a popular open-source framework for machine learning. In these courses you will:
- Learn how to build machine learning models in TensorFlow
- Build image recognition algorithms with deep neural networks and convolutional neural networks
- Understand how to deploy your models on mobile and the web
- Go beyond image recognition into object detection, text recognition, and more
- Expand the basic APIs for custom learning/training
The Machine Learning course and Deep Learning Specialization teach the most important and foundational principles of Machine Learning and Deep Learning. This new deeplearning.ai TensorFlow Specialization teaches you how to use TensorFlow to implement those principles so that you can start building and applying scalable models to real-world problems. To develop a deeper understanding of how neural networks work, we recommend that you take the Deep Learning Specialization.
Course 1
Introduction to TensorFlow for AI, ML and DL
Course 2
Convolutional Neural Networks in TensorFlow
Course 3
Natural Language Processing in TensorFlow
Course 4
Sequences, Time Series, and Prediction
Course 1: Introduction to TensorFlow for AI, ML and DL
This first course introduces you to TensorFlow, a popular machine learning framework. You will learn how to build a basic neural network for computer vision and use convolutions to improve your neural network.
Week 1: A New Programming Paradigm
- Introduction: A conversation with Andrew Ng
- A primer in machine learning
- The “Hello World” of neural networks
- Working through “Hello World” in TensorFlow and Python
Week 2: Introduction to Computer Vision
- A conversation with Andrew Ng
- An introduction to computer vision
- Writing code to load training data
- Coding a computer vision neural network
- Walk through a notebook for computer vision
- Using callbacks to control training
- Walk through a notebook with callbacks
Week 3: Enhancing Vision with Convolutional Neural Networks
- A conversation with Andrew Ng
- What are convolutions and pooling?
- Implementing convolutional layers
- Implementing pooling layers
- Improving the fashion classifier with convolutions
- Walking through convolutions
Week 4: Using Real-World Images
- A conversation with Andrew Ng
- Understanding ImageGenerator
- Defining a ConvNet to use complex images
- Training the ConvNet with fit_generator
- Walking through developing a ConvNet
- Walking through training the ConvNet with fit_generator
- Adding automatic validation to test accuracy
- Exploring the impact of compressing images
- Outro: Conversation with Andrew
Course 2: Convolutional Neural Networks in TensorFlow
This second course teaches you advanced techniques to improve the computer vision model you built in Course 1. You will explore how to work with real-world images in different shapes and sizes, visualize the journey of an image through convolutions to understand how a computer “sees” information, plot loss and accuracy, and explore strategies to prevent overfitting, including augmentation and dropouts. Finally, Course 2 will introduce you to transfer learning and how learned features can be extracted from models.
Week 1: Exploring a Larger Dataset
- Introduction: A conversation with Andrew Ng
- Training with the cats vs. dogs dataset
- Working through the notebook
- Fixing through cropping
- Looking at accuracy and loss
Week 2: Augmentation, a Technique to Avoid Overfitting
- A conversation with Andrew Ng
- Introducing augmentation
- Coding augmentation with ImageDataGenerator
- Demonstrating overfitting in cats vs. dogs dataset
- Adding augmentation to cats vs. dogs dataset
- Exploring augmentation with horses vs. humans dataset
Week 3: Transfer Learning
- A conversation with Andrew Ng
- Understanding transfer learning: the concepts
- Coding your own model with transferred features
- Exploring dropouts
- Exploring transfer learning with inception
Week 4: Multi-class Classifications
- A conversation with Andrew Ng
- Moving from binary to multi-class classification
- Exploring multi-class classification with the rock paper scissors dataset
- Training a classifier with the rock paper scissors dataset
- Testing the rock paper scissors classifier
Course 3: Natural Language Processing in TensorFlow
In this third course, you’ll learn how to apply neural networks to solve natural language processing problems using TensorFlow. You’ll learn how to process and represent text through tokenization so that it’s recognizable by a neural network. You’ll be introduced to new types of neural networks, including RNNs, GRUs and LSTMs, and how you can train them to understand the meaning of text. Finally, you’ll learn how to train LSTMs on existing text to create original poetry and more!
Week 1: Sentiment in Text
- Introduction: A conversation with Andrew Ng
- Word-based encodings
- Using APIs
- Text to sequence
- Sarcasm, really?
- Working with the Tokenizer
Week 2: Word Embeddings
- A conversation with Andrew Ng
- The IMDB dataset
- Looking into the details
- How can we use vectors?
- More into the details
- Remember the sarcasm dataset?
- Building a classifier for the sarcasm dataset
- Let’s talk about the loss function
- Pre-tokenized datasets
- Diving into the code
Week 3: Sequence Models
- A conversation with Andrew Ng
- LSTMs
- Implementing LSTMs in code
- A word from Laurence
- Accuracy and Loss
- Using a convolutional network
- Going back to the IMDB dataset
- Tips from Laurence
Week 4: Sequence Models and Literature
- A conversation with Andrew Ng
- Training the data
- Finding what the next word should be
- Predicting a word
- Poetry!
- Laurence the poet
Course 4: Sequences, Time Series, and Prediction
In this fourth course, you will learn how to solve time series and forecasting problems in TensorFlow. You’ll first implement best practices to prepare data for time series learning. You’ll also explore how RNNs and ConvNets can be used for predictions. Finally, you’ll apply everything you’ve learned throughout the Specialization to build a sunspot prediction model using real-world data!
Week 1: Sequences and Prediction
- Introduction: a conversation with Andrew Ng
- Time series examples
- Machine learning applied to time series
- Common patterns in time series
- Introduction to time series
- Train, validation, and test sets
- Metrics for evaluating performance
- Moving average and differencing
- Trailing versus centered windows
- Forecasting
Week 2: Deep Neural Networks for Time Series
- A conversation with Andrew Ng
- Preparing features and labels
- Feeding a windowed dataset into a neural network
- Single layer neural network
- Machine learning on time windows
- Prediction
- More on single-layer network
- Deep neural network training, tuning, and prediction
Week 3: Recurrent Neural Networks for Time Series
- A conversation with Andrew Ng
- Shape of the inputs to the RNN
- Outputting a sequence
- Lambda layers
- Adjusting the learning rate dynamically
- RNNs
- LSTMs
- Coding LSTMs
- More on LSTMs
Week 4: Real-world Time Series Data
- A conversation with Andrew Ng
- Convolutions
- Bi-directional LSTMs
- Real data – sunspots
- Train and tune the model
- Prediction
- Sunspots
- Combining our tools for analysis
Frequently Asked Questions
Who is the deeplearning.ai TensorFlow Specialization for?
The deeplearning.ai TensorFlow Specialization is for anyone who has basic experience coding in Python and wants to learn how to best use TensorFlow to start building AI applications.
What will I learn in the Specialization?
There are four courses in the Specialization. Each course builds on top of the skills you learned in the previous one.
- Course 1: Build a basic neural network for computer vision within TensorFlow, as well as how to use convolutions to improve your neural network.
- Course 2: Learn advanced techniques like augmentation, dropout, and transfer learning to improve your computer vision model.
- Course 3: Apply neural networks to solve natural language processing problems using TensorFlow.
- Course 4: Solve time series and forecasting problems in TensorFlow.
Are there any prerequisites?
You should be comfortable coding in Python and understand high school-level math. Prior machine learning or deep learning knowledge is helpful but not required.
How do I take the Specialization?
You can enroll in the Specialization on Coursera’s platform. You will watch videos and complete assignments on Coursera as well.
How long is the Specialization?
It typically takes 4 weeks, 4-5 hours per week to complete each course. There are four courses in the Specialization.
How much does the Specialization cost?
The Specialization costs $49/month. You can also purchase each course for $49.
How do I audit the Specialization?
You can audit the TensorFlow Specialization for free by going to the homepage of the course, clicking “Enroll,” and clicking “audit” at the bottom of the window. Note that you will not receive a certificate at the end of the course if you choose to audit.
Can I apply for financial aid?
Yes, Coursera provides financial aid to learners who cannot afford the fee. You can apply for it by going to the Coursera course page and clicking on the Financial Aid link beneath the “Enroll” button on the left.
Will I receive a certificate at the end of the Specialization?
You will receive a certificate at the end of each course if you pay for the courses and complete the programming assignments. There is a limit of 180 days of certificate eligibility, after which you would have to re-purchase the course. If you audit the course for free, you will not receive a certificate.
If you complete all four courses in the Specialization and are subscribed to the Specialization, you will also receive a certificate showing that you completed the entire Specialization.
Is this a stand-alone course or a Specialization?
This is a deeplearning.ai Specialization made up of four courses.
About the Instructors
Laurence Moroney is a Developer Advocate at Google working on Artificial Intelligence with TensorFlow. As the author of more programming books than he can count, he’s excited to be working with deeplearning.ai and Coursera in producing video training.
When not working with technology, he’s a member of the Science Fiction Writers of America, having authored several science fiction novels, a produced screenplay and comic books, including the prequel to the movie ‘Equilibrium’ starring Christian Bale. Laurence is based in Washington State, where he drinks way too much coffee.
Andrew Ng is a global leader in AI and co-founder of Coursera. Dr. Ng is also the CEO and founder of deeplearning.ai and founder of Landing AI. He is an Adjunct Professor in the Computer Science Department at Stanford University.
He was until recently Chief Scientist at Baidu, where he was responsible for driving the company’s global AI strategy and infrastructure. He was also the founding lead of the Google Brain team. Dr. Ng has authored or co-authored over 100 research papers in machine learning, robotics and related fields. He holds degrees from Carnegie Mellon University, MIT and the University of California, Berkeley.