Deep Learning for Beginners in Python: Work On 12+ Projects

Artificial Intelligence and Deep Learning are growing exponentially in today's world. There is multiple application of AI and Deep Learning like Self Driving Cars, Chatbots, Image Recognition, Virtual Assistance, ALEXA, and so on...

With this course, you will understand the complexities of Deep Learning in an easy way, as well as you will have A Complete Understanding of the Googles TensorFlow 2.0 Framework

TensorFlow 2.0 Framework has amazing features that simplify the Model Development, Maintenance, Processes, and Performance

In TensorFlow 2.0 you can start the coding with Zero Installation, whether you’re an expert or a beginner, in this course you will learn an end-to-end implementation of Deep Learning Algorithms

List of the Projects that you will work on,

Part 1: Artificial Neural Networks (ANNs)

Project 1: Multiclass image classification with ANN

Project 2: Binary Data Classification with ANN

Part 2: Convolutional Neural Networks (CNNs)

Project 3: Object Recognition in Images with CNN

Project 4: Binary Image Classification with CNN

Project 5: Digit Recognition with CNN

Project 6: Breast Cancer Detection with CNN

Project 7: Predicting the Bank Customer Satisfaction

Project 8: Credit Card Fraud Detection with CNN

Part 3: Recurrent Neural Networks (RNNs)

Project 9: IMDB Review Classification with RNN - LSTM

Project 10: Multiclass Image Classification with RNN - LSTM

Project 11: Google Stock Price Prediction with RNN and LSTM

Part 4: Transfer Learning

Part 5: Natural Language Processing

Basics of Natural Language Processing

Project 12: Movie Review Classification with NLTK

Part 6: Data Analysis and Data Visualization

Crash Course on Numpy (Data Analysis)

Crash Course on Pandas (Data Analysis)

Crash course on Matplotlib (Data Visualization)

With this course, you will learn,

1) To build the Neural Networks from the scratch

2) You will have a complete understanding of  Artificial Neural Networks, Convolutional Neural Networks, and Recurrent Neural Networks

3) You will learn to build the neural networks with LSTM and GRU

4) Hands-On Transfer Learning

5) Learn Natural Language Processing by doing a text classification project

6) Improve your skills in Data Analysis with Numpy, Pandas, and Data Visualization with Matplotlib

So what are you waiting for, Enroll Now and understand Deep Learning to advance your career and increase your knowledge!

Regards,

Vijay Gadhave