Deep Learning with TensorFlow 2

Master deep learning in Python with TensorFlow 2: Apply neural networks to solve real-world data science challenges

5 hours of content 14152 students
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What you get:

  • 5 hours of content
  • 135 Interactive exercises
  • 34 Downloadable resources
  • World-class instructor
  • Closed captions
  • Q&A support
  • Future course updates
  • Course exam
  • Certificate of achievement

Deep Learning with TensorFlow 2

Start for free

What you get:

  • 5 hours of content
  • 135 Interactive exercises
  • 34 Downloadable resources
  • World-class instructor
  • Closed captions
  • Q&A support
  • Future course updates
  • Course exam
  • Certificate of achievement
Start for free

What you get:

  • 5 hours of content
  • 135 Interactive exercises
  • 34 Downloadable resources
  • World-class instructor
  • Closed captions
  • Q&A support
  • Future course updates
  • Course exam
  • Certificate of achievement

What You Learn

  • Master the essential mathematics for understanding deep learning algorithms
  • Build and customize machine learning algorithms from scratch to enhance your control over model architecture
  • Understand key deep learning concepts such as backpropagation, stochastic gradient descent, and batching to optimize your neural network models
  • Learn how to deal with overfitting through early stopping and improve the generalizability of your models
  • Solve complex real-world challenges in TensorFlow 2
  • Improve your career perspectives by acquiring highly sophisticated technical skills such as deep learning in TensorFlow 2
  • Position your profile to capitalize on the ever-growing number of AI development opportunities in the job market

Top Choice of Leading Companies Worldwide

Industry leaders and professionals globally rely on this top-rated course to enhance their skills.

Course Description

Machine and deep learning are some of those quantitative analysis skills that differentiate the data scientist from the other members of the team. Not to mention that the field of machine learning is the driving force of artificial intelligence. This course will teach you how to leverage deep learning and neural networks for the purposes of data science. The technology we employ is TensorFlow 2.0, which is the state-of-the-art deep learning framework.

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Why machine learning

1.1 Why machine learning

7 min

Introduction to neural networks

2.1 Introduction to neural networks

4 min

Training the model theory

2.3 Training the model theory

3 min

Types of machine learning

2.5 Types of machine learning

4 min

The linear model

2.7 The linear model

3 min

The linear model. Multiple inputs.

2.9 The linear model. Multiple inputs.

2 min

Curriculum

  • 1. Introduction
    1 Lesson 7 Min

    In this introductory part of the course, we will discuss why you will need machine learning when working as a data scientist, what you will see in the following chapters of this training, and what the best way to take the course is.

    Why machine learning
    7 min
  • 2. Neural networks Intro
    12 Lessons 42 Min

    The basic logic behind training an algorithm involves four ingredients: data, model, objective function, and an optimization algorithm. In this part of the course, we describe each of them and build a solid foundation that allows you to understand the idea behind using neural networks. After completing this chapter, you will know what the various types of machine learning are, how to train a machine learning model, and understand terms like objective function, L2-norm loss, cross-entropy loss, one gradient descent, and n-parameter gradient descent.

    Introduction to neural networks
    4 min
    Training the model theory
    3 min
    Types of machine learning
    4 min
    The linear model
    3 min
    The linear model. Multiple inputs.
    2 min
    The linear model. Multiple inputs and multiple outputs
    4 min
    Graphical representation
    2 min
    The objective function
    1 min
    L2-norm loss
    2 min
    Cross-entropy loss
    4 min
    One-parameter gradient descent
    7 min
    N-parameter gradient descent
    6 min
  • 3. Setting up the environment
    7 Lessons 24 Min

    Here, we will show you how to install the Jupyter Notebook (the environment we will use to code in Python) and how to import the relevant libraries. Because this course is based in Python, we will be working with several popular libraries: NumPy, SciPy, scikit-learn and TensorFlow.

    Setting up the environment - Do not skip, please!
    1 min
    Why Python and why Jupyter
    5 min
    Installing Anaconda
    3 min
    Jupyter Dashboard - Part 1
    3 min
    Jupyter Dashboard - Part 2
    6 min
    Installing the TensorFlow package
    5 min
    Installing the packages Read now
    1 min
  • 4. Minimal example
    5 Lessons 20 Min

    It is time to build your first machine learning algorithm. We will show you how to import the relevant libraries, how to generate random input data for the model to train on, how to create the targets the model will aim at, and how to plot the training data. The mechanics of this model exemplify how all regressions you’ve run in different packages (scikit-learn) or software (Excel) work. This is an iterative method aiming to find the best-fitting line.

    Outline
    3 min
    Generating the data (optional)
    5 min
    Initializing the variables
    3 min
    Training the model
    8 min
    Minimal Example - Exercises.txt Read now
    1 min
  • 5. Introduction to TensorFlow 2
    8 Lessons 24 Min

    Having created the simple net, we 'translate' it to TensorFlow. This is our way of taking a simple, well-understood problem to introduce the syntax and logic of TensorFlow.

    TensorFlow Outline
    3 min
    TensorFlow 2 Intro
    3 min
    A note on coding in TensorFlow
    1 min
    Types of file formats in Tensorflow and data handling
    3 min
    Model layout - inputs, outputs, targets, weights, bias, optimizer, and loss
    6 min
    Interpreting the result and extracting the weights and bias
    4 min
    Customizing your model
    3 min
    Introduction to TensorFlow - Exercises Read now
    1 min
  • 6. Deep nets overview
    8 Lessons 25 Min

    From this section on, we will explore deep neural networks. Most real-life dependencies cannot be modelled with a simple linear combination (as we have done so far). And because we want to be better forecasters, we need better models. Most of the time, this means working with a model that is more sophisticated than a liner model. In this section, we will talk about concepts like deep nets, non-linearities, activation functions, softmax activation, and backpropagation. Sounds a bit complex, but we have made it easy for you!

    The layer
    2 min
    What is a deep net
    2 min
    Really understand deep nets
    5 min
    Why do we need non-linearities
    3 min
    Activation functions
    4 min
    Softmax activation
    3 min
    Backpropagation
    3 min
    Backpropagation - intuition
    3 min
  • 7. Backpropagation (optional)
    1 Lesson 1 Min

    Тo get a truly deep understanding of deep neural networks, one will have to look at the mathematics of it. As backpropagation is at the core of the optimization process, we wanted to introduce you to it. This is not a necessary part of the course, as in TensorFlow, sklearn, or any other machine learning package (as opposed to simply NumPy), will have backpropagation methods incorporated.

    Backpropagation mathematics Read now
    1 min
  • 8. Overfitting
    6 Lessons 20 Min

    Some of the most common pitfalls you can have when creating predictive models, and especially in deep learning, is to either underfit or overfit your data. This means to either take less advantage of the machine learning algorithm than you could have due to insufficient training (underfitting), or alternatively create a model that fits the training data too much (overtrain the model) which makes it unsuitable for a different sample (overfitting).

    Underfitting and overfitting. A regression example
    4 min
    Underfitting and overfitting. A classification example
    2 min
    Train vs validation
    3 min
    Train vs validation vs test
    3 min
    N-fold cross validation
    3 min
    Early stopping - motivation and types
    5 min
  • 9. Initialization
    3 Lessons 9 Min

    Initialization is the process in which we set the initial values of weights, and it's an important aspect of building a machine learning model. In this section, you will learn how to initialize the weights of your model and how to apply Xavier initialization.

    Initialization
    3 min
    Types of simple initializations
    3 min
    Xavier's initialization
    3 min
  • 10. Optimizers
    7 Lessons 21 Min

    The gradient descent iterates the whole training set before updating the weights. Every iteration updates the weights in a relatively small way. Here, you will learn common pitfalls related to this method and how to boost them, using stochastic gradient descent, momentum, learning rate schedules, and adaptive learning rates.

    SGD&Batching
    3 min
    Local minima pitfalls
    2 min
    Momentum
    3 min
    Learning rate schedules
    4 min
    Learning rate schedules. A picture
    2 min
    Adaptive learning schedules
    4 min
    Adaptive moment estimation
    3 min
  • 11. Preprocessing
    5 Lessons 15 Min

    A large part of the effort data scientists make when creating a new model is related to preprocessing. This process refers to any manipulation we apply to the dataset before running it and training the model. Learning how to preprocess data is fundamental for anyone who wants to be able to create machine learning models, as no meaningful framework can simply take raw data and provide an answer. In this part of the course, we will show you how to prepare your data for analysis and modeling.

    Preprocessing
    3 min
    Basic preprocessing
    1 min
    Standardization
    5 min
    Dealing with categorical data
    2 min
    One hot vs binary
    4 min
  • 12. Deeper example
    12 Lessons 40 Min

    Once we have learned all the relevant theory, we are ready to jump into deep waters. We explore the 'Hello world' of deep learning - the MNIST dataset, where we classify 60,000 images into 10 classes (the 10 digits: 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10).

    MNIST dataset
    2 min
    How to tackle the MNIST dataset
    3 min
    MNIST - Importing libraries and data
    2 min
    Preprocess the data - create a validation dataset and scale the data
    5 min
    Preprocess the data - scale the test data Read now
    1 min
    Preprocess the data - shuffle and batch
    7 min
    Preprocess the data - shuffle and batch Exercise Read now
    1 min
    Outline the model
    5 min
    Select the loss and the optimizer
    2 min
    Learning
    6 min
    MNIST - Exercises Read now
    2 min
    Testing the model
    4 min
  • 13. Business case
    12 Lessons 43 Min

    Data science without an application is nothing but research. Since we at 365 believe that the skills you acquire should be relevant for your work, we finish the course with a business case, where we implement all the deep learning knowledge you've acquired.

    Exploring the dataset and identifying predictors
    8 min
    Outlining the business case solution
    2 min
    Balancing a dataset
    4 min
    Preprocessing the data
    12 min
    Preprocessing Exercise Read now
    1 min
    Load the preprocessed data
    3 min
    Load the preprocessed data Exercise Read now
    1 min
    Learning and interpreting the result
    4 min
    Setting an early stopping mechanism
    5 min
    Setting an early stopping mechanism Exercise Read now
    1 min
    Testing the business model
    1 min
    Homework Read now
    1 min
  • 14. Conclusion
    6 Lessons 19 Min

    This section is designed to help you continue your specialization and data science journey. In this section, we discuss what is further out there in the machine learning world, how Google’s DeepMind uses machine learning, what are RNNs, and what non-NN approaches are there.

    Summary
    4 min
    What's more out there
    2 min
    An overview of CNNs
    5 min
    How DeepMind uses deep learning Read now
    1 min
    An overview of RNNs
    3 min
    Non-NN approaches
    4 min

Topics

PythonTheorymachine learningProgrammingMathematicsDeep LearningTensorflowNeural Networks

Tools & Technologies

python

Course Requirements

  • You need to complete an introduction to Python before taking this course
  • Basic skills in statistics, probability, and linear algebra are required
  • It is highly recommended to take the Machine Learning in Python course first
  • You will need to install the Anaconda package, which includes Jupyter Notebook

Who Should Take This Course?

Level of difficulty: Advanced

  • Aspiring data scientists, ML engineers, and AI developers
  • Existing data scientists, ML engineers, and AI developers who want to improve their technical skills

Exams and Certification

A 365 Data Science Course Certificate is an excellent addition to your LinkedIn profile—demonstrating your expertise and willingness to go the extra mile to accomplish your goals.

Exams and certification

Meet Your Instructor

Iskren Vankov

Iskren Vankov

Winner of Award

3 Courses

1140 Reviews

21980 Students

A top-of-class graduate from Edinburgh, Caltech, and Oxford, Iskren is a true academic force with over 8 years of industry experience, from Junior Developer to Head of Research and Development. He holds numerous prestigious awards, including the Microsoft Research Award for best Informatics dissertation and the BSc Computer Science and Physics Class Prize for the highest mark in his degree program. Iskren’s research background in Quantum Computation and experience in machine learning have contributed greatly to 365’s Deep Learning and CNNs courses, as well as the upcoming RNNs course.

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