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Data Science: Deep Learning in Python Udemy Free Download

Data Science: Deep Learning in Python Download

The MOST in-depth take a look at neural community principle, and learn how to code one with pure Python and Tensorflow

Data Science Deep Learning in Python
Data Science Deep Learning in Python
What you’ll study
  • Find out how Deep Learning REALLY works (not just a few diagrams and magical black field code)
  • Find out how a neural community is constructed from primary constructing blocks (the neuron)
  • Code a neural community from scratch in Python and NumPy
  • Code a neural community utilizing Google’s TensorFlow
  • Describe several types of neural networks and the several types of issues they’re used for
  • Derive the backpropagation rule from first ideas
  • Create a neural community with an output that has Okay > 2 lessons utilizing softmax
  • Describe the varied phrases associated to neural networks, resembling “activation”, “backpropagation” and “feedforward”
  • Set up TensorFlow
  • Primary math (calculus derivatives, matrix arithmetic, chance)
  • Set up Numpy and Python
  • Don’t fear about putting in TensorFlow, we’ll try this in the lectures.
  • Being aware of the content material of my logistic regression course (cross-entropy price, gradient descent, neurons, XOR, donut) offers you the right context for this course

This course will get you began in constructing your FIRST synthetic neural community utilizing deep studying strategies. Following my earlier course on logistic regression, we take this primary constructing block, and construct full-on non-linear neural networks proper out of the gate utilizing Python and Numpy. All of the supplies for this course are FREE.

We lengthen the earlier binary classification mannequin to a number of lessons utilizing the softmax operate, and we derive the crucial coaching methodology known as “backpropagation” utilizing first ideas. I present you learn how to code backpropagation in Numpy, first “the slow way”, after which “the fast way” utilizing Numpy options.

Subsequent, we implement a neural community utilizing Google’s new TensorFlow library.

You need to take this course in case you are in beginning your journey towards changing into a grasp at deep studying, or in case you are in machine studying and knowledge science in common. We transcend primary fashions like logistic regression and linear regression and I present you one thing that robotically learns options.

This course supplies you with many sensible examples so to actually see how deep studying can be utilized on something. All through the course, we’ll do a course challenge, which is able to present you learn how to predict consumer actions on an internet site given consumer knowledge like whether or not or not that consumer is on a cell gadget, the variety of merchandise they considered, how lengthy they stayed in your web site, whether or not or not they’re a returning customer, and what time of day they visited.

One other challenge on the finish of the course reveals you ways you need to use deep studying for facial features recognition. Think about with the ability to predict somebody’s feelings simply primarily based on an image!

After getting your ft moist with the basics, I present a short overview of a number of the latest developments in neural networks – barely modified architectures and what they’re used for.


Should you already find out about softmax and backpropagation, and also you need to skip over the idea and pace issues up utilizing extra superior strategies together with GPU-optimization, take a look at my follow-up course on this matter, Data Science: Sensible Deep Learning Ideas in Theano and TensorFlow.

I’ve different programs that cowl extra superior matters, resembling Convolutional Neural NetworksRestricted Boltzmann MachinesAutoencoders, and extra! However you need to be very snug with the fabric in this course earlier than shifting on to extra superior topics.

This course focuses on “how to build and understand“, not just “how to use”. Anybody can study to make use of an API in quarter-hour after studying some documentation. It’s not about “remembering facts”, it’s about “seeing for yourself” through experimentation. It should educate you learn how to visualize what’s occurring in the mannequin internally. In order for you extra than only a superficial take a look at machine studying fashions, this course is for you.

“If you can’t implement it, you don’t understand it”

  • Or as the nice physicist Richard Feynman mentioned: “What I cannot create, I do not understand”.
  • My programs are the ONLY programs the place you’ll discover ways to implement machine studying algorithms from scratch
  • Different programs will educate you learn how to plug in your knowledge right into a library, however do you really want assist with 3 strains of code?
  • After doing the identical factor with 10 datasets, you understand you didn’t study 10 issues. You realized 1 factor, and simply repeated the identical 3 strains of code 10 occasions…


Instructed Stipulations:

  • calculus (taking derivatives)
  • matrix arithmetic
  • chance
  • Python coding: if/else, loops, lists, dicts, units
  • Numpy coding: matrix and vector operations, loading a CSV file
  • Be aware of primary linear fashions resembling linear regression and logistic regression



  • Try the lecture “Machine Learning and AI Prerequisite Roadmap” (accessible in the FAQ of any of my programs, together with the free Numpy course)
Who this course is for:
  • College students in machine studying – you’ll get all of the tidbits you’ll want to do nicely in the course of a neural community
  • Professionals who need to use neural networks in their machine studying and knowledge science pipeline. Be capable to apply extra highly effective fashions, and know its drawbacks.
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