Genre: eLearning | MP4 | Video:AVC 1280 x 720 | Audio: AAC 48.0 KHz
Language: English | Size: 5.12 GB | Duration: 11h 7m

The world has been revolving much around the terms "Machine Learning" and "Deep Learning" recently. With or without our knowledge every day we are using these technologies. Ranging from google suggestions, translations, ads, movie recommendations, friend suggestions, sales and customer experience so on and so forth. There are tons of other applications too. No wonder why "Deep Learning" and "Machine Learning along with Data Science" are the most sought after talent in the technology world now a days.

But the problem is that, when you think about learning these technologies, a misconception that lots of maths, statistics, complex algorithms and formulas needs to be studied prior to that. Its just like someone tries to make you believe that, you should learn the working of an Internal Combustion engine before you learn how to drive a car. The fact is that, to drive a car, we just only need to know how to use the user friendly control pedals extending from engine like clutch, brake, accelerator, steering wheel etc. And with a bit of experience, you can easily drive a car.

The basic know how about the internal working of the engine is of course an added advantage while driving a car, but its not mandatory. Just like that, in our deep learning course, we have a perfect balance between learning the basic concepts along the implementation of the built in Deep Learning Classes and functions from the Keras Library using the Python Programming Language. These classes, functions and APIs are just like the control pedals from the car engine, which we can use easily to build an efficient deep learning model.

Lets now see how this course is organized and an overview about the list of topics included.

We will be starting with few theory sessions in which we will see an overview about the Deep Learning and neural networks. The difference between deep learning and machine learning, the history of neural networks, the basic work-flow of deep learning, biological and artificial neurons and applications of neural networks.

In the next session, we will try to answer the most popular , yet confusing question weather we have to choose Deep Learning or machine learning for an upcoming project involving Artificial intelligence. We will compare the scenarios and factors which help us to decide in between machine learning or deep learning.

And then we will prepare the computer and install the python environment for doing our deep learning coding. We will install the anaconda platform, which a most popular python platform and also install the necessary dependencies to proceed with the course.

Once we have our computer ready, we will learn the basics of python language which could help if you are new to python and get familiar with the basic syntax of python which will help with the projects in our course. We will cover the details about python assignments, flow control, functions, data structures etc.

Later we will install the libraries for our projects like Theano, Tensorflow and Keras which are the best and most popular deep learning libraries. We will try a sample program with each libraries to make sure its working fine and also learn how to switch between them.

Then we will have another theory session in which we will learn the concept of Multi-Layer perceptrons, which is the basic element of the deep learning neural network and then the terminology and the Major steps associated with Training a Neural Network. We will discuss those steps in details in this session.

After all these exhaustive basics and concepts, we will now move on to creating real-world deep learning models.

At first we will download and use the Pima Indians Onset of Diabetes Dataset, with the training data of Pima Indians and whether they had an onset of diabetes within five years. We will build a classification model with this and later will train the model and evaluate the accuracy of the model. We will also try Manual and automatic data splitting and k-Fold Cross Validation with this model

The next dataset we are going to use is the Iris Flowers Classification Dataset, which contains the classification of iris flowers into 3 species based on their petal and sepal dimensions. This is a multi class dataset and we will build a multi-classification model with this and will train the model and try to evaluate the accuracy.

The next dataset is the Sonar Returns Dataset, which contains the data about the strength of sonar signals returns and classification weather it was reflected by a rock or any metal like mines under the sea bed. we will build the base model and will evaluate the accuracy. Also we will try to Improve Performance of model With Data Preparation technique like standardization and also by changing the topology of the neural network. By making it deeper or shallow.
[/i]Homepage
https://www.skillshare.com/classes/Learn-Deep-Learning-Skill-with-Python-and-Keras-for-Dummies-The-Complete-Beginners-Guide/1144911345

[i]Screenshots