Deep Learning with TensorFlow 2.0
Deep Learning with TensorFlow 2.0, available at $94.99, has an average rating of 4.72, with 111 lectures, 15 quizzes, based on 2348 reviews, and has 21002 subscribers.
You will learn about Gain a Strong Understanding of TensorFlow – Google’s Cutting-Edge Deep Learning Framework Build Deep Learning Algorithms from Scratch in Python Using NumPy and TensorFlow Set Yourself Apart with Hands-on Deep and Machine Learning Experience Grasp the Mathematics Behind Deep Learning Algorithms Understand Backpropagation, Stochastic Gradient Descent, Batching, Momentum, and Learning Rate Schedules Know the Ins and Outs of Underfitting, Overfitting, Training, Validation, Testing, Early Stopping, and Initialization Competently Carry Out Pre-Processing, Standardization, Normalization, and One-Hot Encoding This course is ideal for individuals who are Aspiring data scientists or People interested in Machine Learning, Deep Learning, Business, and Artificial Intelligence, or Anyone who wants to learn how to code and build machine and deep learning algorithms from scratch It is particularly useful for Aspiring data scientists or People interested in Machine Learning, Deep Learning, Business, and Artificial Intelligence, or Anyone who wants to learn how to code and build machine and deep learning algorithms from scratch.
Enroll now: Deep Learning with TensorFlow 2.0
Summary
Title: Deep Learning with TensorFlow 2.0
Price: $94.99
Average Rating: 4.72
Number of Lectures: 111
Number of Quizzes: 15
Number of Published Lectures: 111
Number of Published Quizzes: 15
Number of Curriculum Items: 126
Number of Published Curriculum Objects: 126
Original Price: $189.99
Quality Status: approved
Status: Live
What You Will Learn
- Gain a Strong Understanding of TensorFlow – Google’s Cutting-Edge Deep Learning Framework
- Build Deep Learning Algorithms from Scratch in Python Using NumPy and TensorFlow
- Set Yourself Apart with Hands-on Deep and Machine Learning Experience
- Grasp the Mathematics Behind Deep Learning Algorithms
- Understand Backpropagation, Stochastic Gradient Descent, Batching, Momentum, and Learning Rate Schedules
- Know the Ins and Outs of Underfitting, Overfitting, Training, Validation, Testing, Early Stopping, and Initialization
- Competently Carry Out Pre-Processing, Standardization, Normalization, and One-Hot Encoding
Who Should Attend
- Aspiring data scientists
- People interested in Machine Learning, Deep Learning, Business, and Artificial Intelligence,
- Anyone who wants to learn how to code and build machine and deep learning algorithms from scratch
Target Audiences
- Aspiring data scientists
- People interested in Machine Learning, Deep Learning, Business, and Artificial Intelligence,
- Anyone who wants to learn how to code and build machine and deep learning algorithms from scratch
Data scientists, machine learning engineers, and AI researchers all have their own skillsets. But what is that one special thing they have in common?
They are all masters of deep learning.
We often hear about AI, or self-driving cars, or the ‘algorithmic magic’ at Google, Facebook, and Amazon. But it is not magic – it is deep learning. And more specifically, it is usually deep neural networks – the one algorithm to rule them all.
Cool, that sounds like a really important skill; how do I become a Master of Deep Learning?
There are two routes you can take:
The unguided route – This route will get you where you want to go, eventually, but expect to get lost a few times. If you are looking at this course you’ve maybe been there.
The 365 route – Consider our route as the guided tour. We will take you to all the places you need, using the paths only the most experienced tour guides know about. We have extra knowledge you won’t get from reading those information boards and we give you this knowledge in fun and easy-to-digest methods to make sure it really sticks.
Clearly, you can talk the talk, but can you walk the walk? – What exactly will I get out of this course that I can’t get anywhere else?
Good question! We know how interesting Deep Learning is and we love it! However, we know that the goal here is career progression, that’s why our course is business focused and gives you real world practice on how to use Deep Learning to optimize business performance.
We don’t just scratch the surface either – It’s not called ‘Skin-Deep’ Learning after all. We fully explain the theory from the mathematics behind the algorithms to the state-of-the-art initialization methods, plus so much more.
Theory is no good without putting it into practice, is it? That’s why we give you plenty of opportunities to put this theory to use. Implement cutting edge optimizations, get hands on with TensorFlow and even build your very own algorithm and put it through training!
Wow, that’s going to look great on your resume!
Speaking of resumes, you also get a certificate upon completion which employers can verify that you have successfully finished a prestigious 365 Careers course – and one of our best at that!
Now, I can see you’re bragging a little, but I admit you have peaked my interest. What else does your course offer that will make my resume shine?
Trust us, after this course you’ll be able to fill your resume with skills and have plenty left over to show off at the interview.
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Of course, you’ll get fully acquainted with Google’ TensorFlow and NumPy, two tools essential for creating and understanding Deep Learning algorithms.
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Explore layers, their building blocks and activations – sigmoid, tanh, ReLu, softmax, etc.
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Understand the backpropagation process, intuitively and mathematically.
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You’ll be able to spot and prevent overfitting – one of the biggest issues in machine and deep learning
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Get to know the state-of-the-art initialization methods. Don’t know what initialization is? We explain that, too
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Learn how to build deep neural networks using real data, implemented by real companies in the real world. TEMPLATES included!
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Also, I don’t know if we’ve mentioned this, but you will have created your very own Deep Learning Algorithm after only 1 hour of the course.
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It’s this hands-on experience that will really make your resume stand out
This all sounds great, but I am a little overwhelmed, I’m afraid I may not have enough experience.
We admit, you will need at least a little understanding of Python programming but nothing to worry about. We start with the basics and take you step by step toward building your very first (or second, or third etc.) Deep Learning algorithm – we program everything in Python and explain each line of code.
We do this early on and it will give you the confidence to carry on to the more complex topics we cover.
All the sophisticated concepts we teach are explained intuitively. Our beautifully animated videos and step by step approach ensures the course is a fun and engaging experience for all levels.
We want everyone to get the most out of our course, and the best way to do that is to keep our students motivated. So, we worked hard to ensure that students with varying skills are challenged without being overwhelmed. Each lecture builds upon the last and practical exercises mean that you can practice what you’ve learned before moving on to the next step.
And of course, we are available to answer any queries you have. In fact, we aim to answer any and all question within 1 business day. We don’t just chuck you in the pool then head to the bar and let you fend for yourself.
Remember, we don’t just want you to enrol – we want you to complete the course and become a Master of Deep Learning.
OK, awesome! I feel much better about my level of experience now, but we haven’t discussed yours! How do I know you can teach me to become a Master of Deep Learning?
That’s an understandable worry, but it’s one we have no problem removing.
We are 365 Careers and we’ve been creating online courses for ages. We have over 1,750,000 students and enjoy high ratings for all our Udemy courses. We are a team of experts who are all, at heart, teachers. We believe knowledge should be shared and not just through boring text books but in engaging and fun ways.
We are well aware how difficult it is to build your knowledge and skills in the data science field, it’s so new and has grown so fast that the education sector has struggled to keep up and offer any substantial methods of teaching these topic areas. We wanted to change things – to rock the boat – so we developed our unique teaching style, one that countless students have enjoyed and thrived with.
And between us, we think this course is one of our favourites, so if this is your first time with us, you’re in for a treat. If it’s not and you’ve taken one of our courses before, then, you’re still in for a treat!
I’ve been hurt before though, how can I be sure you won’t let me down?
Easy, with Udemy’s 30-day money back guarantee. We strive for the best and believe that our courses are the best out there. But you know what, everyone is different, and we understand that. So, we have no problem offering this guarantee, we want students who will complete and get the most out of this course. If you are one of the few who finds this course not what you wanted or expected then, get your money back. No questions, no risk, no problem.
Great, that takes a load of my shoulders. What next?
Click on the ‘Buy now’ button and take that first step toward a satisfying data science career and becoming a Master of Deep Learning.
Course Curriculum
Chapter 1: Welcome! Course introduction
Lecture 1: Meet your instructors and why you should study machine learning?
Lecture 2: What does the course cover?
Lecture 3: Download All Resources and Important FAQ
Chapter 2: Introduction to neural networks
Lecture 1: Introduction to neural networks
Lecture 2: Training the model
Lecture 3: Types of machine learning
Lecture 4: The linear model
Lecture 5: Need Help with Linear Algebra?
Lecture 6: The linear model. Multiple inputs
Lecture 7: The linear model. Multiple inputs and multiple outputs
Lecture 8: Graphical representation
Lecture 9: The objective function
Lecture 10: L2-norm loss
Lecture 11: Cross-entropy loss
Lecture 12: One parameter gradient descent
Lecture 13: N-parameter gradient descent
Chapter 3: Setting up the working environment
Lecture 1: Setting up the environment – An introduction – Do not skip, please!
Lecture 2: Why Python and why Jupyter?
Lecture 3: Installing Anaconda
Lecture 4: The Jupyter dashboard – part 1
Lecture 5: The Jupyter dashboard – part 2
Lecture 6: Jupyter Shortcuts
Lecture 7: Installing TensorFlow 2
Lecture 8: Installing packages – exercise
Lecture 9: Installing packages – solution
Chapter 4: Minimal example – your first machine learning algorithm
Lecture 1: Minimal example – part 1
Lecture 2: Minimal example – part 2
Lecture 3: Minimal example – part 3
Lecture 4: Minimal example – part 4
Lecture 5: Minimal example – Exercises
Chapter 5: TensorFlow – An introduction
Lecture 1: TensorFlow outline
Lecture 2: TensorFlow 2 intro
Lecture 3: A Note on Coding in TensorFlow
Lecture 4: Types of file formats in TensorFlow and data handling
Lecture 5: Model layout – inputs, outputs, targets, weights, biases, optimizer and loss
Lecture 6: Interpreting the result and extracting the weights and bias
Lecture 7: Cutomizing your model
Lecture 8: Minimal example with TensorFlow – Exercises
Chapter 6: Going deeper: Introduction to deep neural networks
Lecture 1: Layers
Lecture 2: What is a deep net?
Lecture 3: Understanding deep nets in depth
Lecture 4: Why do we need non-linearities?
Lecture 5: Activation functions
Lecture 6: Softmax activation
Lecture 7: Backpropagation
Lecture 8: Backpropagation – visual representation
Chapter 7: Backpropagation. A peek into the Mathematics of Optimization
Lecture 1: Backpropagation. A peek into the Mathematics of Optimization
Chapter 8: Overfitting
Lecture 1: Underfitting and overfitting
Lecture 2: Underfitting and overfitting – classification
Lecture 3: Training and validation
Lecture 4: Training, validation, and test
Lecture 5: N-fold cross validation
Lecture 6: Early stopping
Chapter 9: Initialization
Lecture 1: Initialization – Introduction
Lecture 2: Types of simple initializations
Lecture 3: Xavier initialization
Chapter 10: Gradient descent and learning rates
Lecture 1: Stochastic gradient descent
Lecture 2: Gradient descent pitfalls
Lecture 3: Momentum
Lecture 4: Learning rate schedules
Lecture 5: Learning rate schedules. A picture
Lecture 6: Adaptive learning rate schedules
Lecture 7: Adaptive moment estimation
Chapter 11: Preprocessing
Lecture 1: Preprocessing introduction
Lecture 2: Basic preprocessing
Lecture 3: Standardization
Lecture 4: Dealing with categorical data
Lecture 5: One-hot and binary encoding
Chapter 12: The MNIST example
Lecture 1: The dataset
Lecture 2: How to tackle the MNIST
Lecture 3: Importing the relevant packages and load the data
Lecture 4: Preprocess the data – create a validation dataset and scale the data
Lecture 5: Preprocess the data – scale the test data
Instructors
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365 Careers
Creating opportunities for Data Science and Finance students -
365 Careers Team
Creating opportunities for Business & Finance students
Rating Distribution
- 1 stars: 26 votes
- 2 stars: 32 votes
- 3 stars: 205 votes
- 4 stars: 795 votes
- 5 stars: 1290 votes
Frequently Asked Questions
How long do I have access to the course materials?
You can view and review the lecture materials indefinitely, like an on-demand channel.
Can I take my courses with me wherever I go?
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!
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