Course · Intermediate

Introduction to Deep Learning in TensorFlow

Name: Introduction to Deep Learning in TensorFlow
Rating: 4.8 (359 reviews)

In this course, you'll learn the fundamentals of deep learning, as well as how to build, train, and evaluate models using the TensorFlow framework.

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Intermediate friendly
6 hrs
5 lessons
1 project
Premium

Course overview

Develop deep learning models by training and evaluating neural networks with TensorFlow to solve complex prediction problems.

What's inside

5 lessons · 1 project

01

Deep Learning Fundamentals

Learn the difference between shallow and dense neural networks and how forward propagation can be implemented from scratch.

48 min
02

Introduction to TensorFlow Operations

Create constant and variable tensors, perform mathematical calculations, and convert between tensors and NumPy arrays.

56 min
03

Shallow Neural Network with the Sequential API

Build, train, evaluate, and make predictions on a shallow neural network regression model in TensorFlow built using the Sequential API in Keras.

85 min
04

Multi-layer (dense) Deep Learning Model

Configure and train a multi-layer regression model by adding dense layers to the network, specify the loss metrics, and configure the optimizer using TensorFlow’s Sequential API in Keras.

51 min
05

Deep Learning with the Functional API in Keras

Build deep learning models with complex topologies using the Functional API in Keras by explicitly connecting the various layers of the deep learning model architecture, specifying the loss metrics, and compiling the model.

43 min
06

Guided Project: Predicting Listing Gains in the Indian IPO Market Using TensorFlow Project

For this project, you'll work as a data scientist for an investment firm analyzing the Indian IPO market. You'll build a deep learning model using TensorFlow to predict listing gains, applying skills in data exploration, visualization, preprocessing and modeling.

34 min

Part of these paths

Skill path

Deep Learning in TensorFlow

23 hrs · 4 courses

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