Deep Learning Prerequisites: The Numpy Stack in Python (V2+)Download

Probably, you have heard of deep learning and don’t really know what it actually entails, cheers, we got you covered. We have provided here some deep learning and data science prerequisites to help you better understand deep learning.

Here I will provide some prerequisites, full concepts of the Numpy, Scipy, Pandas, and Matplotlib stack, also some preparatory classes to help you fully understand deep learning, machine learning, and artificial intelligence.

You might be puzzled if you are just starting out in the field of data science or have had some experience some time ago with neural networks.

However, I know that at first I was puzzled, as were several of my colleagues and friends, but I assure you, these specific and diverse perspectives of deep learning will shed a lot of light on what deep learning is all about.

Interestingly, In this post, you’ll discover exactly what deep learning is, listening to a variety of experts and leaders in the field.

This course is designed to remove that obstacle – to show you how to do things in the Numpy stack that are frequently needed in deep learning and data science.

Table Of Contents

Deep Learning Prerequisites Or Requirements

Here are some of the deep learning prerequisite or requirements.

  • Full understanding of linear algebra and the Gaussian distribution.
  • Familiarize yourself with Python coding
  • You should already know “why” things like scalar product, matrix inversion, and Gaussian probability distributions are useful and what they can be used for
  • matrix arithmetic
  • probability
  • Python coding: if/else, loops, lists, dicts, sets

What is Deep Learning?

Deep learning is a branch of machine learning that deals with algorithms inspired by the structure and function of the brain, called artificial neural networks.

Deep Learning Vs Machine Learning

Machine Learning which is an approach to achieve Artificial Intelligence, is the method of using algorithms to learn and interpret data, and then make perceptions or forecasts about something in the world.

So, instead of manually coding software routines with a specific set of instructions to perform a specific task, the machine is “trained” using large amounts of data and algorithms that give it the ability to learn how to perform the task.

Machine learning came directly from the minds of the early AI crowd, and decision tree learning and inductive logic programming have been included in algorithmic approaches over the years.

Also, other algorithm approaches include clustering, enhanced learning, and Bayesian networks.

However, as we all know, none has achieved the ultimate target of general AI, and with early machine learning approaches, even narrow AI was virtually out of reach.

Interestingly, one of the very best application areas for machine learning for many years was computer vision, though it still required a great deal of hand-coding to get the job done.

On the other hand, Deep Learning which is another algorithmic approach for the early machine learning crowd, artificial neural networks, has mostly appeared and disappeared over the decades.

Neural networks are inspired by our understanding of our brain’s biology, all those interconnections between neurons.

But, unlike a biological brain in which any neuron can connect to any other neuron within a certain physical distance, these artificial neural networks have different covers, connections, and data propagation directions.

For example, take an image and cut it into a bunch of blocks that are fed into the first layer of the neural network.

In the first layer of individual neurons, it passes the data to a second layer. The second layer of neurons does its job, and so on, until the final layer and the final result are produced.

Each neuron assigns a weight to its input: how correct or incorrect it is in relation to the task being performed.

The final product is determined by the total of these weights, which can be clearly explained by the example of the stop sign.

The image attributes of a stop sign are cut out and “examined” by neurons: its octagonal shape, its red fire color, its distinctive letters, the size of the traffic sign, and its movement or lack thereof.

The task of the neural network is to determine whether it is a stop signal or not. It produces a “probability vector”, in fact, a very polite assumption, based on weighting.

In the above example, the system can be 86% sure that the image is a stop sign, 7% sure that it is a speed limit sign, and 5% sure that it is a kite stuck in a tree, and so on, and the network architecture tells the neural network whether it is correct or not.

Recently, Neural networks were almost rejected by the AI research community. They have existed since the early days of AI and have produced very little in terms of “intelligence”.

However, the problem was that even the most basic neural networks were very computationally intensive, it just wasn’t a practical approach.

contrarily, a small heretical research group led by Geoffrey Hinton at the University of Toronto went ahead, eventually paralleling the algorithms for supercomputers to run and test the concept, but it wasn’t until GPUs were implemented in the effort that the promise came to reality.

In coclusion, today, image recognition by trained machines through deep learning in some environments is better than that of humans, and this ranges from cats to identifying indicators of blood cancer and tumors in MRI scans. AlphaGo, from Google, learned the game and trained for the Go game (adjusted his neural network) playing against himself several times.

Deep Learning Tutorials

Deep Learning With Python

Deep Learning with Python introduces the field of deep learning using the Python language and the powerful Keras library. Written by the creator of Keras and artificial intelligence researcher at Google, François Chollet, this book develops your understanding through intuitive explanations and practical examples.

Find more about the deep learning with python ebook with the button below.

Deep Learning Algorithms

While deep learning algorithms have self-learning representations, they rely on RNAs that reflect how the brain calculates information.

During the training process, the deep learning algorithms use unknown elements in the input distribution to extract characteristics, group objects, and discover useful data patterns.

However, like self-learning training machines, this occurs on several levels, using the algorithms to build the models.

Deep learning algorithms use several models. Although no network is considered perfect, some algorithms are better suited to perform specific tasks. To choose the right ones, it is good to get a solid understanding of all the primary algorithms.

Here are some Deep Learning Algorithms

  • Convolutional Neural Networks (CNNs)
  • Long Short Term Memory Networks (LSTMs)
  • Recurrent Neural Networks (RNNs)
  • Generative Adversarial Networks (GANs)
  • Radial Basis Function Networks (RBFNs)
  • Multilayer Perceptrons (MLPs)
  • Self Organizing Maps (SOMs)
  • Deep Belief Networks (DBNs)
  • Restricted Boltzmann Machines( RBMs)
  • Autoencoders

Deep learning algorithms work with almost any type of data and require large amounts of information and computing power to solve difficult problems.

Benefits of this deep learning course

  • Understand supervised machine learning (classification and regression) with real-world examples using Scikit-Learn
  • Understand and code using the Numpy stack
  • Use Numpy, Scipy, Matplotlib, and Pandas to implement numerical algorithms
  • Understand the pros and cons of various machine learning models, including deep learning, decision trees, random forest, linear regression, momentum, and more.

Who this Course is For

  • Students and professionals with little experience in Numpy who plan to learn deep learning and machine learning later
  • Professionals who have experienced machine learning and data science, but have trouble translating ideas into code.

Get the paid version with certificate from Udemy with the image below[Click on the image below]

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