TensorFlow for Deep Learning Bootcamp from Udemy

Just launched with all modern best practices for building neural networks with TensorFlow and becoming a TensorFlow & Deep Learning Expert.

Join a live online community of over 900,000+ students and a course taught by a TensorFlow expert. This course will take you from absolute beginner with TensorFlow, to creating state-of-the-art deep learning neural networks.

TensorFlow experts earn up to $ By taking this course you will be joining the growing Machine Learning industry and becoming a top paid TensorFlow Developer.

Here is a full course breakdown of everything we will teach (yes, it's very comprehensive, but don't be intimidated, as we will teach you everything from scratch. ):

The goal of this course is to teach you all the skills necessary for you to become a top 10% TensorFlow Developer.

This course will be very hands on and project based. You won't just be staring at us teach, but you will actually get to experiment, do exercises, and build machine learning models and projects to mimic real life scenarios. By the end of it all, you will develop skillsets needed to develop modern deep learning solutions that big tech companies encounter.

0 — TensorFlow Fundamentals

Introduction to tensors (creating tensors)
Getting information from tensors (tensor attributes)
Manipulating tensors (tensor operations)
Tensors and NumPy
Using @tf.function (a way to speed up your regular Python functions)
Using GPUs with TensorFlow

1 — Neural Network Regression with TensorFlow

Build TensorFlow sequential models with multiple layers
Prepare data for use with a machine learning model
Learn the different components which make up a deep learning model (loss function, architecture, optimization function)
Learn how to diagnose a regression problem (predicting a number) and build a neural network for it

2 — Neural Network Classification with TensorFlow

Learn how to diagnose a classification problem (predicting whether something is one thing or another)
Build, compile & train machine learning classification models using TensorFlow
Build and train models for binary and multi-class classification
Plot modelling performance metrics against each other
Match input (training data shape) and output shapes (prediction data target)

3 — Computer Vision and Convolutional Neural Networks with TensorFlow

Build convolutional neural networks with Conv2D and pooling layers
Learn how to diagnose different kinds of computer vision problems
Learn to how to build computer vision neural networks
Learn how to use real-world images with your computer vision models

4 — Transfer Learning with TensorFlow Part 1: Feature Extraction

Learn how to use pre-trained models to extract features from your own data
Learn how to use TensorFlow Hub for pre-trained models
Learn how to use TensorBoard to compare the performance of several different models

5 — Transfer Learning with TensorFlow Part 2: Fine-tuning

Learn how to setup and run several machine learning experiments
Learn how to use data augmentation to increase the diversity of your training data
Learn how to fine-tune a pre-trained model to your own custom problem
Learn how to use Callbacks to add functionality to your model during training

6 — Transfer Learning with TensorFlow Part 3: Scaling Up (Food Vision mini)

Learn how to scale up an existing model
Learn to how evaluate your machine learning models by finding the most wrong predictions
Beat the original Food101 paper using only 10% of the data

7 — Milestone Project 1: Food Vision

Combine everything you've learned in the previous 6 notebooks to build Food Vision: a computer vision model able to classify 101 different kinds of foods. Our model well and truly beats the original Food101 paper.

8 — NLP Fundamentals in TensorFlow

Learn to:
- Preprocess natural language text to be used with a neural network
- Create word embeddings (numerical representations of text) with TensorFlow
- Build neural networks capable of binary and multi-class classification using:
  - RNNs (recurrent neural networks)
  - LSTMs (long short-term memory cells)
  - GRUs (gated recurrent units)
  - CNNs
Learn how to evaluate your NLP models

9 — Milestone Project 2: SkimLit

Replicate a the model which powers the PubMed 200k paper to classify different sequences in PubMed medical abstracts (which can help researchers read through medical abstracts faster)

10 — Time Series fundamentals in TensorFlow

Learn how to diagnose a time series problem (building a model to make predictions based on data across time, e.g. predicting the stock price of AAPL tomorrow)
Prepare data for time series neural networks (features and labels)
Understanding and using different time series evaluation methods
- MAE — mean absolute error
Build time series forecasting models with TensorFlow
- RNNs (recurrent neural networks)
- CNNs (convolutional neural networks)

11 — Milestone Project 3: (Surprise)

If you've read this far, you are probably interested in the course. This last project will be good... we promise you, so see you inside the course ;)

TensorFlow is growing in popularity and more and more job openings are appearing for this specialized knowledge. As a matter of fact, TensorFlow is outgrowing other popular ML tools like PyTorch in job market. Google, Airbnb, Uber, DeepMind, Intel, IBM, Twitter, and many others are currently powered by TensorFlow. There is a reason these big tech companies are using this technology and you will find out all about the power that TensorFlow gives developers. We guarantee you this is the most comprehensive online course on TensorFlow. So why wait? Make yourself stand out by becoming a TensorFlow Expert and advance your career.See you inside the course.

What's inside

Learning objectives

Build tensorflow models using computer vision, convolutional neural networks and natural language processing
Complete access to all interactive notebooks and all course slides as downloadable guides
Increase your skills in machine learning, artificial intelligence, and deep learning
Understand how to integrate machine learning into tools and applications
Learn to build all types of machine learning models using the latest tensorflow 2

Build image recognition, text recognition algorithms with deep neural networks and convolutional neural networks
Using real world images to visualize the journey of an image through convolutions to understand how a computer “sees” information, plot loss and accuracy
Applying deep learning for time series forecasting
Gain the skills you need to become a tensorflow developer
Be recognized as a top candidate for recruiters seeking tensorflow developers

Build tensorflow models using computer vision, convolutional neural networks and natural language processing
Complete access to all interactive notebooks and all course slides as downloadable guides
Increase your skills in machine learning, artificial intelligence, and deep learning
Understand how to integrate machine learning into tools and applications
Learn to build all types of machine learning models using the latest tensorflow 2
Build image recognition, text recognition algorithms with deep neural networks and convolutional neural networks
Using real world images to visualize the journey of an image through convolutions to understand how a computer “sees” information, plot loss and accuracy
Applying deep learning for time series forecasting
Gain the skills you need to become a tensorflow developer
Be recognized as a top candidate for recruiters seeking tensorflow developers

Syllabus

Introduction

Course Outline

Join Our Online Classroom!

Exercise: Meet Your Classmates & Instructor

All Course Resources + Asking Questions + Getting Help

ZTM Resources

By the end of this section, you'll be able to recognise where deep learning and neural networks can be used. You'll also be able to use TensorFlow to create and manipulate tensors.

What is deep learning?

Why use deep learning?

What are neural networks?

Python + Machine Learning Monthly

What is deep learning already being used for?

What is and why use TensorFlow?

What is a Tensor?

What we're going to cover throughout the course

How to approach this course

Need A Refresher?

Creating your first tensors with TensorFlow and tf.constant()

Creating tensors with TensorFlow and tf.Variable()

Creating random tensors with TensorFlow

Shuffling the order of tensors

Creating tensors from NumPy arrays

Getting information from your tensors (tensor attributes)

Indexing and expanding tensors

Manipulating tensors with basic operations

Matrix multiplication with tensors part 1

Matrix multiplication with tensors part 2

Matrix multiplication with tensors part 3

Changing the datatype of tensors

Tensor aggregation (finding the min, max, mean & more)

Tensor troubleshooting example (updating tensor datatypes)

Finding the positional minimum and maximum of a tensor (argmin and argmax)

Squeezing a tensor (removing all 1-dimension axes)

One-hot encoding tensors

Trying out more tensor math operations

Exploring TensorFlow and NumPy's compatibility

Making sure our tensor operations run really fast on GPUs

TensorFlow Fundamentals challenge, exercises & extra-curriculum

Monthly Coding Challenges, Free Resources and Guides

LinkedIn Endorsements

By the end of this section you'll be able to build neural networks with TensorFlow to solve regression problems (problems which require you to predict a number, such as the sale price of a house).

Introduction to Neural Network Regression with TensorFlow

Inputs and outputs of a neural network regression model

Anatomy and architecture of a neural network regression model

Creating sample regression data (so we can model it)

Note: Code update for upcoming lecture(s) for TensorFlow 2.7.0+ fix

The major steps in modelling with TensorFlow

Steps in improving a model with TensorFlow part 1

Steps in improving a model with TensorFlow part 2

Steps in improving a model with TensorFlow part 3

Evaluating a TensorFlow model part 1 ("visualise, visualise, visualise")

Evaluating a TensorFlow model part 2 (the three datasets)

Evaluating a TensorFlow model part 3 (getting a model summary)

Evaluating a TensorFlow model part 4 (visualising a model's layers)

Evaluating a TensorFlow model part 5 (visualising a model's predictions)

Evaluating a TensorFlow model part 6 (common regression evaluation metrics)

Evaluating a TensorFlow regression model part 7 (mean absolute error)

Evaluating a TensorFlow regression model part 7 (mean square error)

Setting up TensorFlow modelling experiments part 1 (start with a simple model)

Setting up TensorFlow modelling experiments part 2 (increasing complexity)

Comparing and tracking your TensorFlow modelling experiments

How to save a TensorFlow model

How to load and use a saved TensorFlow model

(Optional) How to save and download files from Google Colab

Putting together what we've learned part 1 (preparing a dataset)

Putting together what we've learned part 2 (building a regression model)

Putting together what we've learned part 3 (improving our regression model)

Preprocessing data with feature scaling part 1 (what is feature scaling?)

Preprocessing data with feature scaling part 2 (normalising our data)

Preprocessing data with feature scaling part 3 (fitting a model on scaled data)

TensorFlow Regression challenge, exercises & extra-curriculum

Learning Guideline

Build a deep learning neural network classification model with TensorFlow (a model which can classify whether something is one thing or another).

Introduction to neural network classification in TensorFlow

Example classification problems (and their inputs and outputs)

Input and output tensors of classification problems

Typical architecture of neural network classification models with TensorFlow

Creating and viewing classification data to model

Checking the input and output shapes of our classification data

Building a not very good classification model with TensorFlow

Trying to improve our not very good classification model

Creating a function to view our model's not so good predictions

Note: Updates for TensorFlow 2.7.0

Make our poor classification model work for a regression dataset

Non-linearity part 1: Straight lines and non-straight lines

Non-linearity part 2: Building our first neural network with non-linearity

Non-linearity part 3: Upgrading our non-linear model with more layers

Non-linearity part 4: Modelling our non-linear data once and for all

Non-linearity part 5: Replicating non-linear activation functions from scratch

Getting great results in less time by tweaking the learning rate

Using the TensorFlow History object to plot a model's loss curves

Using callbacks to find a model's ideal learning rate

Training and evaluating a model with an ideal learning rate

Introducing more classification evaluation methods

Finding the accuracy of our classification model

Creating our first confusion matrix (to see where our model is getting confused)

Making our confusion matrix prettier

Putting things together with multi-class classification part 1: Getting the data

Multi-class classification part 2: Becoming one with the data

Multi-class classification part 3: Building a multi-class classification model

Good to know

Know what's good

, what to watch for

, and possible dealbreakers

Provides hands-on experience building machine learning models and projects, mimicking real-life scenarios encountered by big tech companies, which is valuable for practical application

Starts with TensorFlow fundamentals, including tensors and operations, and progresses to neural network regression, classification, and computer vision, providing a comprehensive learning path

Covers convolutional neural networks for computer vision and recurrent neural networks for natural language processing, which are essential skills in these rapidly growing fields

Includes a section on time series fundamentals, covering data preparation, evaluation methods, and model building with RNNs and CNNs, which is relevant for forecasting and prediction tasks

Emphasizes the use of TensorFlow Hub for pre-trained models and TensorBoard for comparing model performance, which streamlines the development process and enhances model evaluation

Explores data augmentation techniques to increase the diversity of training data and improve model generalization, which is useful when working with limited datasets

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in TensorFlow for Deep Learning Bootcamp with these activities:

Review Linear Algebra Fundamentals

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Reinforce your understanding of linear algebra concepts, which are fundamental to understanding tensor operations and neural network architectures in TensorFlow.

Browse courses on Linear Algebra

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Review matrix operations such as addition, subtraction, and multiplication.
Practice solving systems of linear equations.
Understand vector spaces and linear transformations.

Read 'Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow' by Aurélien Géron

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Gain practical experience with machine learning techniques and learn how to apply them using TensorFlow and Keras.

View Hands-On Machine Learning with Scikit-Learn,... on Amazon

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Read the chapters on neural networks and deep learning.
Work through the code examples and exercises in the book.
Experiment with different machine learning models and techniques.

Read 'Deep Learning' by Goodfellow, Bengio, and Courville

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Gain a deeper understanding of the theoretical underpinnings of deep learning, which will enhance your ability to effectively use TensorFlow.

View Deep Learning on Amazon

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Read the chapters on convolutional neural networks and recurrent neural networks.
Study the sections on optimization algorithms and regularization techniques.
Review the mathematical notation and concepts presented in the book.

Four other activities

Expand to see all activities and additional details

Show all seven activities

Implement Custom Layers and Models

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Solidify your understanding of TensorFlow's Keras API by implementing custom layers and models from scratch.

Show steps

Create a custom layer with trainable weights.
Build a custom model by subclassing the tf.keras.Model class.
Train your custom model on a simple dataset.

Write a Blog Post on TensorFlow Best Practices

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Reinforce your learning by summarizing and explaining TensorFlow best practices in a blog post.

Show steps

Research TensorFlow best practices for model building and training.
Write a clear and concise blog post explaining these best practices.
Include code examples to illustrate the best practices.

Build an Image Classifier with Transfer Learning

Show steps

Apply transfer learning techniques to build an image classifier using a pre-trained model from TensorFlow Hub.

Show steps

Choose a dataset of images to classify.
Select a pre-trained model from TensorFlow Hub.
Fine-tune the pre-trained model on your dataset.
Evaluate the performance of your image classifier.

Create a TensorFlow Model Visualization Dashboard

Show steps

Develop a dashboard to visualize the architecture, training progress, and performance metrics of TensorFlow models.

Show steps

Choose a dashboarding tool (e.g., TensorBoard, Streamlit, or Dash).
Implement visualizations for model architecture, loss curves, and evaluation metrics.
Deploy the dashboard to a web server or cloud platform.

Career center

Learners who complete TensorFlow for Deep Learning Bootcamp will develop knowledge and skills that may be useful to these careers:

Deep Learning Engineer

Deep Learning Engineers specialize in building and deploying neural networks, a key emphasis of this TensorFlow for Deep Learning Bootcamp. A Deep Learning Engineer works with complex models and datasets, requiring a firm grasp of model architecture, training, and evaluation. This course offers a comprehensive introduction to these areas, including practical experience with convolutional neural networks, recurrent neural networks, and transfer learning. It is essential for anyone looking to pursue a career as a Deep Learning Engineer because it offers an immersive learning experience with TensorFlow. The course also covers topics such as time series forecasting and natural language processing, all useful for this role.

See salaries and explore the career path for Deep Learning Engineer

Machine Learning Engineer

A Machine Learning Engineer develops and implements machine learning models, often using tools like TensorFlow. This course is ideal for an aspiring Machine Learning Engineer as it provides hands-on experience building various types of neural networks, including those for regression, classification, and computer vision. The course's focus on practical application, project-based learning, and real world examples with TensorFlow also helps build the skills necessary for this role. The curriculum covers core concepts like tensors, model evaluation, and optimization, which are foundational for this profession. An aspiring Machine Learning Engineer should take this course in particular because it will help them build end-to-end practical skills in TensorFlow.

See salaries and explore the career path for Machine Learning Engineer

Computer Vision Engineer

Computer Vision Engineers develop systems that allow computers to 'see' and interpret images. This role relies heavily on the use of convolutional neural networks, which are specifically covered in this course with TensorFlow. A Computer Vision Engineer works on problems like image recognition and object detection, directly related to the course's practical exercises. This course offers experience developing models using real world images and provides a strong background on how to diagnose different computer vision problems. It also helps an aspiring engineer learn to build models that achieve practical results. Computer Vision Engineers should take this course to gain expertise in using TensorFlow for real world computer vision challenges.

See salaries and explore the career path for Computer Vision Engineer

Artificial Intelligence Developer

An Artificial Intelligence Developer builds intelligent systems using machine learning and deep learning techniques. This course provides a strong foundation in deep learning with TensorFlow, which is a primary tool for many AI applications. An Artificial Intelligence Developer builds models for a wide variety of tasks, such as image recognition, natural language processing, and time series forecasting, all of which are part of the course's curriculum. This course, with its hands-on project based approach, is particularly useful for anyone pursuing a career as an Artificial Intelligence Developer, because it delivers comprehensive deep learning skills for real-world scenarios.

See salaries and explore the career path for Artificial Intelligence Developer

Natural Language Processing Engineer

A Natural Language Processing Engineer works on enabling computers to understand human language. The course includes a section on natural language processing fundamentals using TensorFlow, making it directly relevant for this career path. A Natural Language Processing Engineer develops models for tasks such as text classification and sentiment analysis. This course helps an aspiring engineer by teaching them text preprocessing using word embeddings, as well as building models using recurrent neural networks. Aspiring Natural Language Processing Engineers should take this course, as it will help them learn the practical application of TensorFlow to solve language problems.

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Data Scientist

A Data Scientist uses data to solve problems and create insights, often utilizing machine learning methods. This course in TensorFlow builds helpful skills for a Data Scientist by providing practical knowledge of building neural network models. Data Scientists need to be able to build predictive models for a variety of applications. This course, with its emphasis on using real world images and practical projects, helps Data Scientists gain proficiency with deep learning in TensorFlow. The comprehensive curriculum covering regression, classification, computer vision, natural language processing, and time series helps Data Scientists build a broad skill set. This course is a good choice for a Data Scientist to gain valuable experience with the TensorFlow framework.

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Robotics Engineer

A Robotics Engineer develops and builds robots, often incorporating machine learning for perception and control. This course can be helpful for a Robotics Engineer, as many robotics applications now use deep learning techniques, particularly computer vision and time series analysis. The course provides practical experience with TensorFlow, which can be utilized for building AI models for robotics. The Robotics Engineer can apply the skills they learn to build models for object detection and robot navigation. The course's focus on hands-on projects and building actual models helps the Robotics Engineer gain deep learning experience, which they can then apply to their field.

See salaries and explore the career path for Robotics Engineer

Machine Learning Researcher

A Machine Learning Researcher focuses on developing new machine learning algorithms and techniques. This course could be useful for an aspiring Machine Learning Researcher because it provides a solid grounding in the practical implementation of various deep learning model architectures using TensorFlow. This knowledge helps a researcher understand the intricacies of model building and performance. The course's coverage of transfer learning, data augmentation, and model evaluation methods is especially helpful for anyone doing research in the field. Although a Machine Learning Researcher typically needs a graduate degree, this course may be useful as a practical stepping stone for those interested in research.

See salaries and explore the career path for Machine Learning Researcher

AI Product Manager

An AI Product Manager oversees the lifecycle of AI-driven products, requiring an understanding of the technical aspects of machine learning. This course may be helpful for an AI Product Manager, as they will benefit from a familiarity with TensorFlow, a key technology in the AI field. The course focuses on understanding and building different types of machine learning models, including computer vision and natural language processing models. An AI Product Manager needs to learn about practical AI development, and this course may offer that knowledge. The course's project-based learning can help the product manager better grasp the challenges that AI developers face.

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Data Analyst

A Data Analyst interprets data to identify trends and insights, and while not always directly involved in building machine learning models, it may be helpful for them to have familiarity with the field. This course in TensorFlow introduces complex models and various methods to evaluate data. A Data Analyst may find the course material useful for a deeper understanding of what is possible working with large datasets, which are increasingly common in the field. The course's focus on practical projects and real world examples can increase a Data Analyst's knowledge of data analysis and the limits of data science and deep learning. The course may be useful to a Data Analyst wanting to branch into machine learning.

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Software Developer

A Software Developer writes code for various applications, and while this course focuses specifically on deep learning with TensorFlow, it may be useful to a Software Developer. The course provides a foundation in creating machine learning models, and how to work with tensors, which are useful abstractions for a Software Developer to understand. The Software Developer can use this knowledge to create AI-based software. The course helps Software Developers appreciate how machine learning, artificial intelligence, and neural networks are actually implemented. This course may be useful for a Software Developer that hopes to work in AI-driven software development.

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Research Scientist

A Research Scientist conducts experiments and studies to advance scientific knowledge. Although this course focuses on practical TensorFlow, it may be useful for a Research Scientist that wants to explore deep learning techniques. The course's coverage of various neural network architectures can broaden the Research Scientist's understanding of computational methods. The course provides skills with TensorFlow, which may be relevant in various research projects. The course may be useful for a Research Scientist that is looking to introduce more machine learning into their work, or as an introduction to the field. A Research Scientist typically has an advanced graduate degree.

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Quantitative Analyst

A Quantitative Analyst, or quant, uses mathematical and statistical models to solve problems in finance. This course may be helpful for a quant as it teaches how to build time series forecasting models with TensorFlow. The course explains different methods for evaluating time series data, helping a quant understand how to make predictions with time series data. Although the course is not specific to finance, the skill of time series forecasting is a vital part of how a quant develops models. This course may be useful for a Quant wishing to understand deep learning methods for time series prediction, though they may find more relevant material elsewhere.

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Data Engineer

A Data Engineer designs and builds systems for data storage and processing. This course may be useful for a Data Engineer by providing insight on machine learning workflows that they often have to implement. The course's focus on practical TensorFlow will help a Data Engineer appreciate the details of data preparation and model training. The course may provide knowledge about machine learning pipelines, which are an important component of modern data systems. A Data Engineer might find this course helpful for appreciating a different perspective on the data that they work with, even though its focus is more on modeling.

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Business Intelligence Developer

A Business Intelligence Developer creates systems to analyze business data and provide insights. This course is focused on TensorFlow and deep learning. A Business Intelligence Developer may not find this material immediately applicable in their role because their primary goal is business analysis, not model building. However, knowledge of how machine learning may be used to forecast or identify patterns may be useful in that role. This course may be useful to a Business Intelligence Developer as a general overview of machine learning methods. This may help them better understand how data may be used in various ways.

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TensorFlow for Deep Learning Bootcamp

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