Data Science- Intro To Deep Learning With Python In 2024

Data Science: Intro To Deep Learning With Python In 2024, available at Free, has an average rating of 4.05, with 13 lectures, based on 60 reviews, and has 9584 subscribers.

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You will learn about Understand the intuition behind Artificial Neural Networks Build artificial neural networks with Tensorflow Classify images, data using deep learning Apply Convolutional Neural Networks in practice This course is ideal for individuals who are Beginners In Python or Beginners In Deep Learning or Beginners In Machine Learning It is particularly useful for Beginners In Python or Beginners In Deep Learning or Beginners In Machine Learning.

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Summary

Title: Data Science: Intro To Deep Learning With Python In 2024

Price: Free

Average Rating: 4.05

Number of Lectures: 13

Number of Published Lectures: 11

Number of Curriculum Items: 13

Number of Published Curriculum Objects: 11

Original Price: Free

Quality Status: approved

Status: Live

What You Will Learn

Understand the intuition behind Artificial Neural Networks

Build artificial neural networks with Tensorflow

Classify images, data using deep learning

Apply Convolutional Neural Networks in practice

Who Should Attend

Beginners In Python

Beginners In Deep Learning

Beginners In Machine Learning

Target Audiences

Beginners In Python

Beginners In Deep Learning

Beginners In Machine Learning

Neural networks are a family of machine learning algorithms that are generating a lot of excitement. They are a technique that is inspired by how the neurons in our brains function. They are based on a simple idea: given certain parameters, it is possible to combine them in order to predict a certain result. For example, if you know the number of pixels in an image, there are ways of knowing which number is written in the image. The data that enters passes through various “ layers” in which a series of adjusted learning rules are applied by a weighted function. After passing through the last layer, the results are compared with the “correct” results, and the parameters are adjusted.

Although the algorithms and the learning process in general are complex, one the network has learned, it can freeze the various weights and function in a memory or execution mode. Google uses these types of algorithms, for example, for image searches.

There is no single definition for the meaning of Deep Learning. In general, when we talk of Deep Learning, we are referring to a group of Machine Learning algorithms based on neural networks that, as we have seen, are characterized by cascade data processing. The entrance signal passes through the various stages, and in each one, they are subjected to a non-linear transformation. This helps to extract and transform the variable according to the determined parameters (weights or boundaries). There isn’t an established limit for the number of stages that a neural network must contain to be considered Deep Learning. However, it is thought that Deep Learning arose in the 80’s, using a model which had 5 or 6 layers. It was (and is) called the neocognitron and was created by the Japanese researcher Kunihiki Fukushima. Neural networks are very effective in identifying patterns.

An example worth highlighting of the application of Deep Learning is the project carried out by Google and the Universities of Stanford and Massachusetts. It aimed to improve the natural language processing techniques of a type of AI called Recurrent Neural Network Language Model (RNNLM). It’s used for automatic translations and creating subtitles, among other thing. Basically, it builds up phrases word by words, basing each word on the previous one and in this way, it can even write poems.

Module 1

1. Introduction to Deep Learning and TensorFlow

2. Basics of Neural Networks

3. Designing a shallow neural network (Scratch and python) (Project)

4. Deeper neural network using TensorFlow. (Project)

Course Curriculum

Chapter 1: Module -1

Lecture 1: Intro to deep learning

Lecture 2: All you need to know about dataset

Lecture 3: Binary classification

Lecture 4: Forward Propagation

Lecture 5: Activation Functions

Lecture 6: BackPropagation

Lecture 7: Gradient descent

Lecture 8: Building blocks Of DNN

Lecture 9: DNN from scratch

Lecture 10: MNIST Fashion in TF framework

Lecture 11: Download the code files

Instructors

Pianalytix ? 75,000+ Students Worldwide
Projects in Data Science, Machine Learning, Power BI, & More

Rating Distribution

1 stars: 5 votes

2 stars: 3 votes

3 stars: 12 votes

4 stars: 19 votes

5 stars: 21 votes

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