Deep Learning

Deep Learning

What you'll learn

Gain a comprehensive understanding of TensorFlow and its capabilities.

NN Topologies: Understand various neural network architectures and their use cases.

Deep Learning Frameworks: Explore various deep learning frameworks and their applications.

TF Learn: Introduction and hands-on practice with TensorFlow's high-level API, TF Learn.

Requirements

Fundamental computer skills and comfort with installing and using software.

Basic understanding of Python programming

Essential math and statistics knowledge: A solid foundation in math and statistics is helpful.

Description

Welcome to our "Deep Learning" course! This journey will take you through the exciting realm of artificial intelligence, where we explore the depths of neural networks and their applications. This course is meticulously designed to equip learners with essential skills and techniques required for effective deep learning.In this course, you will start with an introduction to TensorFlow, one of the most powerful libraries in the deep learning ecosystem. We'll guide you through the essential skills and techniques for building and training neural networks. You’ll learn about different neural network topologies, hardware and software requirements, and the tools available in TensorFlow.You will engage in hands-on exercises that will help you apply your new skills. By working on real-world datasets, you will learn how to preprocess data, build and train models, and evaluate their performance. Whether you’re a student, professional developer, data scientist, aspiring AI engineer, or someone looking to start a career in artificial intelligence, this course will set you on the path to success. Start your journey to mastering deep learning with us and unlock limitless opportunities in AI and technology, become proficient in deep learning, opening up a world of opportunities in the AI and technology sectors!

Overview

Section 1: Deep Learning

Lecture 1 Introduction to Tensorflow

Lecture 2 NN Topologies

Lecture 3 HW and SW for DL

Lecture 4 Tensorflow 2.0 Introduction

Lecture 5 TF Data types

Lecture 6 DL Frames

Lecture 7 Neural Networks Part -1

Lecture 8 Neural Networks Part -2

Lecture 9 TF learn introduction

Lecture 10 TF learn hands on

Lecture 11 MNIST Dataset

Lecture 12 Keras Introduction

Lecture 13 Modelling with keras

Lecture 14 Convolutional neural network

Lecture 15 Recurrent Neutral network

Lecture 16 Autoencoders and boltzmann networks

Lecture 17 Data Augentation

Lecture 18 Hands-on project

Section 2: Quiz

Aspiring AI Engineers,Students and Academics,Professional Developers,Data Scientists,Freelancers and Entrepreneurs,Career Changers,Anyone Interested in Artificial Intelligence