Udemy - Deep Learning with TensorFlow 2.0 [2020]

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Deep Learning with TensorFlow 2.0 [TutsNode.com] - Deep Learning with TensorFlow 2.0 14 Appendix_ Linear Algebra Fundamentals
  • 104 Why is Linear Algebra Useful_.mp4 (144.3 MB)
  • 094 What is a Matrix_.en.srt (4.5 KB)
  • 094 What is a Matrix_.mp4 (33.6 MB)
  • 095 Scalars and Vectors.en.srt (3.9 KB)
  • 095 Scalars and Vectors.mp4 (33.8 MB)
  • 096 Linear Algebra and Geometry.en.srt (4.3 KB)
  • 096 Linear Algebra and Geometry.mp4 (49.8 MB)
  • 097 Scalars, Vectors and Matrices in Python.en.srt (6.4 KB)
  • 097 Scalars, Vectors and Matrices in Python.mp4 (26.7 MB)
  • 098 Tensors.en.srt (3.7 KB)
  • 098 Tensors.mp4 (22.5 MB)
  • 099 Addition and Subtraction of Matrices.en.srt (4.2 KB)
  • 099 Addition and Subtraction of Matrices.mp4 (32.6 MB)
  • 100 Errors when Adding Matrices.en.srt (2.7 KB)
  • 100 Errors when Adding Matrices.mp4 (11.2 MB)
  • 101 Transpose of a Matrix.en.srt (5.6 KB)
  • 101 Transpose of a Matrix.mp4 (38.1 MB)
  • 102 Dot Product of Vectors.en.srt (4.4 KB)
  • 102 Dot Product of Vectors.mp4 (24.0 MB)
  • 103 Dot Product of Matrices.en.srt (9.9 KB)
  • 103 Dot Product of Matrices.mp4 (49.4 MB)
  • 104 Why is Linear Algebra Useful_.en.srt (12.2 KB)
  • external-assets-links.txt (1.1 KB)
01 Welcome! Course introduction
  • 001 Meet your instructors and why you should study machine learning_.en.srt (10.5 KB)
  • 001 Meet your instructors and why you should study machine learning_.mp4 (105.8 MB)
  • 002 What does the course cover_.en.srt (6.4 KB)
  • 002 What does the course cover_.mp4 (16.4 MB)
  • 003 Download All Resources and Important FAQ.html (1.8 KB)
02 Introduction to neural networks
  • 004 Course-Notes-Section-2.pdf (927.7 KB)
  • 004 Introduction to neural networks.en.srt (6.2 KB)
  • 004 Introduction to neural networks.mp4 (13.6 MB)
  • 005 Course-Notes-Section-2.pdf (927.7 KB)
  • 005 Training the model.en.srt (4.5 KB)
  • 005 Training the model.mp4 (8.8 MB)
  • 006 Course-Notes-Section-2.pdf (927.7 KB)
  • 006 Types of machine learning.en.srt (5.5 KB)
  • 006 Types of machine learning.mp4 (12.2 MB)
  • 007 Course-Notes-Section-2.pdf (927.7 KB)
  • 007 The linear model.en.srt (4.1 KB)
  • 007 The linear model.mp4 (9.1 MB)
  • 008 Need Help with Linear Algebra_.html (1.7 KB)
  • 009 Course-Notes-Section-2.pdf (927.7 KB)
  • 009 The linear model. Multiple inputs.en.srt (3.2 KB)
  • 009 The linear model. Multiple inputs.mp4 (7.5 MB)
  • 010 Course-Notes-Section-2.pdf (927.7 KB)
  • 010 The linear model. Multiple inputs and multiple outputs.en.srt (5.7 KB)
  • 010 The linear model. Multiple inputs and multiple outputs.mp4 (38.3 MB)
  • 011 Course-Notes-Section-2.pdf (927.7 KB)
  • 011 Graphical representation.en.srt (2.8 KB)
  • 011 Graphical representation.mp4 (6.3 MB)
  • 012 Course-Notes-Section-2.pdf (927.7 KB)
  • 012 The objective function.en.srt (2.1 KB)
  • 012 The objective function.mp4 (5.7 MB)
  • 013 Course-Notes-Section-2.pdf (927.7 KB)
  • 013 L2-norm loss.en.srt (2.9 KB)
  • 013 L2-norm loss.mp4 (7.3 MB)
  • 014 Course-Notes-Section-2.pdf (927.7 KB)
  • 014 Cross-entropy loss.en.srt (5.5 KB)
  • 014 Cross-entropy loss.mp4 (11.3 MB)
  • 015 Course-Notes-Section-2.pdf (927.7 KB)
  • 015 GD-function-example.xlsx (42.3 KB)
  • 015 One parameter gradient descent.en.srt (8.8 KB)
  • 015 One parameter gradient descent.mp4 (17.8 MB)
  • 016 Course-Notes-Section-2.pdf (927.7 KB)
  • 016 N-parameter gradient descent.en.srt (7.8 KB)
  • 016 N-parameter gradient descent.mp4 (39.4 MB)
03 Setting up the working environment
  • 017 Setting up the environment - An introduction - Do not skip, please!.en.srt (1.4 KB)
  • 017 Setting up the environment - An introduction - Do not skip, please!.mp4 (6.0 MB)
  • 018 Why Python and why Jupyter_.en.srt (6.5 KB)
  • 018 Why Python and why Jupyter_.mp4 (32.1 MB)
  • 019 Installing Anaconda.en.srt (4.8 KB)
  • 019 Installing Anaconda.mp4 (28.4 MB)
  • 020 The Jupyter dashboard - part 1.en.srt (3.3 KB)
  • 020 The Jupyter dashboard - part 1.mp4 (8.7 MB)
  • 021 The Jupyter dashboard - part 2.en.srt (7.0 KB)
  • 021 The Jupyter dashboard - part 2.mp4 (18.8 MB)
  • 022 Jupyter Shortcuts.html (1.2 KB)
  • 022 Shortcuts-for-Jupyter.pdf (619.2 KB)
  • 023 Installing TensorFlow 2.en.srt (6.6 KB)
  • 023 Installing TensorFlow 2.mp4 (38.7 MB)
  • 024 Installing packages - exercise.html (1.1 KB)
  • 025 Installing packages - solution.html (1.1 KB)
04 Minimal example - your first machine learning algorithm
  • 026 Minimal example - part 1.en.srt (4.7 KB)
  • 026 Minimal example - part 1.mp4 (6.5 MB)
  • 027 Minimal example - part 2.en.srt (7.1 KB)
  • 027 Minimal example - part 2.mp4 (10.7 MB)
  • 028 Minimal example - part 3.en.srt (4.6 KB)
  • 028 Minimal example - part 3.mp4 (9.8 MB)
  • 029 Minimal example - part 4.en.srt (11.3 KB)
  • 029 Minimal example - part 4.mp4 (20.8 MB)
  • 030 Minimal example - Exercises.html (2.5 KB)
  • external-assets-links.txt (1.7 KB)
05 TensorFlow - An introduction
  • 031 TensorFlow outline.en.srt (5.4 KB)
  • 031 TensorFlow outline.mp4 (33.5 MB)
  • 032 TensorFlow 2 intro.en.srt (3.8 KB)
  • 032 TensorFlow 2 intro.mp4 (22.0 MB)
  • 033 A Note on Coding in TensorFlow.en.srt (1.4 KB)
  • 033 A Note on Coding in TensorFlow.mp4 (6.8 MB)
  • 034 Types of file formats in TensorFlow and data handling.en.srt (3.6 KB)
  • 034 Types of file formats in TensorFlow and data handling.mp4 (16.4 MB)
  • 035 Model layout - inputs, outputs, targets, weights, biases, optimizer and loss.en.srt (8.1 KB)
  • 035 Model layout - inputs, outputs, targets, weights, biases, optimizer and loss.mp4 (34.7 MB)
  • 036 Interpreting the result and extracting the weights and bias.en.srt (6.4 KB)
  • 036 Inte

Description


Description

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.

Of course, you’ll get fully acquainted with Google’ TensorFlow and NumPy, two tools essential for creating and understanding Deep Learning algorithms.
Explore layers, their building blocks and activations – sigmoid, tanh, ReLu, softmax, etc.
Understand the backpropagation process, intuitively and mathematically.
You’ll be able to spot and prevent overfitting – one of the biggest issues in machine and deep learning
Get to know the state-of-the-art initialization methods. Don’t know what initialization is? We explain that, too
Learn how to build deep neural networks using real data, implemented by real companies in the real world. TEMPLATES included!
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.
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 220,000 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.
Who this course is for:

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

Requirements

Some basic Python programming skills
You’ll need to install Anaconda. We will show you how to do it in one of the first lectures of the course.
All software and data used in the course are free.

Last Updated 11/2020



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Udemy - Deep Learning with TensorFlow 2.0 [2020]


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1.9 GB
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Udemy - Deep Learning with TensorFlow 2.0 [2020]


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