Coursera_Deep_Learning_AI

Become a Machine Learning expert by mastering the fundamentals of deep learning and break into AI.

In this course, I build and train neural network architectures such as Convolutional Neural Networks, Recurrent Neural Networks, LSTMs, Transformers, and learn how to make them better with strategies such as Dropout, BatchNorm, Xavier/He initialization, and more. I master theoretical concepts and their industry applications using Python and TensorFlow and tackle real-world cases such as speech recognition, music synthesis, chatbots, machine translation, natural language processing, and more.

AI is transforming many industries. The Deep Learning Specialization provides me a pathway to take the definitive step in the world of AI by helping me gain the knowledge and skills to level up my career.

After few weeks of training, this is what i got :

• Build and train deep neural networks, implement vectorized neural networks, identify architecture parameters, and apply DL to my applications

• Use best practices to train and develop test sets and analyze bias/variance for building DL applications, use standard NN techniques, apply optimization algorithms, and implement a neural network in TensorFlow

• Use strategies for reducing errors in ML systems, understand complex ML settings, and apply end-to-end, transfer, and multi-task learning

• Build a Convolutional Neural Network, apply it to visual detection and recognition tasks, use neural style transfer to generate art, and apply these algorithms to image, video, and other 2D/3D data

• Build and train Recurrent Neural Networks and its variants (GRUs, LSTMs), apply RNNs to character-level language modeling, work with NLP and Word Embeddings, and use HuggingFace tokenizers and transformers to perform Named Entity Recognition and Question Answering

Skills covered and its notebooks:

1️⃣ Neural Networks and Deep Learning

1. Logistic Regression with a Neural Network mindset
2. Python Basics with Numpy
3. Planar data classification with one hidden layer
4. Building your Deep Neural Network Step by Step
5. Deep Neural Network - Application

2️⃣ Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization

3️⃣ Structuring Machine Learning Projects

4️⃣ Convolutional Neural Networks

5️⃣ Sequence Models

Author :

Jennyfer WAN