**Overview**

**Learn How to Develop Neural Networks From Scratch and Develop and Deploy Models.**

There is a technological revolution happening, changing all aspects of our daily lives. AI (Artificial Intelligence) has penetrated deep into our activities, interactions, professions, comforts, and experiences.

Deep learning is a machine learning technique that teaches computers to do what comes naturally to humans: learn by example. Deep learning is a key technology behind driverless cars, enabling them to recognize a stop sign or to distinguish a pedestrian from a lamppost. It is the key to voice control in consumer devices like phones, tablets, TVs, and hands-free speakers. Deep learning is getting lots of attention lately and for good reason. It’s achieving results that were not possible before.

In deep learning, a computer model learns to perform classification tasks directly from images, text, or sound. Deep learning models can achieve state-of-the-art accuracy, sometimes exceeding human-level performance. Models are trained by using a large set of labeled data and neural network architectures that contain many layers. This immersive deep learning course teaches you how to use this data to create outcomes to use in your development.

**In This Artificial Intelligence and Deep Leaning Course You Will: **

- Understand the basics of deep learning
- Use Tuning Models
- Create convolutional neural networks
- Use Recurrent neural networks
- Develop and deploy models
- Use python / Jupyter notebooks
- Know the best practices to follow

**Course Outline**

**Part 1: Introduction to Deep Learning**

- What is a neural network?
- Supervised Learning with Neural Networks
- Why is Deep Learning taking off?

**Part 2: Neural Networks Basics**

- Binary Classification
- Logistic Regression
- Logistic Regression Cost Function
- Gradient Descent
- Derivatives
- More Derivative Examples
- Computation graph
- Derivatives with a Computation Graph
- Logistic Regression Gradient Descent
- Gradient Descent on m Examples
- Vectorization

**Part 3: Shallow Neural Networks**

- Neural Networks Overview
- Neural Network Representation
- Computing a Neural Network’s Output
- Vectorizing across multiple examples
- Explanation for Vectorized Implementation
- Activation functions
- Why do you need non-linear activation functions?
- Derivatives of activation functions
- Gradient descent for Neural Networks

**Part 4: Deep Neural Networks**

- Deep L-layer neural network
- Forward Propagation in a Deep Network
- Getting your matrix dimensions right
- Why deep representations?
- Building blocks of deep neural networks
- Forward and Backward Propagation
- Parameters vs Hyperparameters

**Part 5: Practical Aspects of Deep Learning**

- Train / Dev / Test sets
- Bias / Variance
- Basic Recipe for Machine Learning
- Regularization
- Why regularization reduces overfitting?
- Dropout Regularization
- Understanding Dropout
- Other regularization methods
- Normalizing inputs
- Vanishing / Exploding gradients
- Weight Initialization for Deep Networks
- Numerical approximation of gradients
- Gradient checking
- Gradient Checking Implementation Notes

**Part 6: Optimization Algorithms**

- Mini-batch gradient descent
- Understanding mini-batch gradient descent
- Exponentially weighted averages
- Understanding exponentially weighted averages
- Bias correction in exponentially weighted averages
- Gradient descent with momentum
- RMSprop
- Adam optimization algorithm
- Learning rate decay
- The problem of local optima

**Part 7: Hyperparameter Tuning, Batch Normalization, and Programming Frameworks**

- Tuning process
- Using an appropriate scale to pick hyperparameters
- Hyperparameters tuning in practice: Pandas vs. Caviar
- Normalizing activations in a network
- Fitting Batch Norm into a neural network
- Why does Batch Norm work?
- Batch Norm at test time
- Softmax Regression
- Training a softmax classifier
- Deep learning frameworks
- TensorFlow

**Part 8: Foundations of Convolutional Neural Networks**

- Computer Vision
- Edge Detection Example
- More Edge Detection
- Padding
- Strided Convolutions
- Convolutions Over Volume
- One Layer of a Convolutional Network
- Simple Convolutional Network Example
- Pooling Layers
- CNN Example
- Why Convolutions?
- Deep convolutional models: case studies
- Classic Networks
- ResNets
- Why ResNets Work
- Networks in Networks and 1×1 Convolutions
- Inception Network Motivation
- Inception Network
- Using Open-Source Implementation
- Transfer Learning
- Data Augmentation
- State of Computer Vision

**Part 9: Recurrent Neural Networks**

- Why sequence models
- Notation
- Recurrent Neural Network Model
- Backpropagation through time
- Different types of RNNs
- Language model and sequence generation
- Sampling novel sequences
- Vanishing gradients with RNNs
- Gated Recurrent Unit (GRU)
- Long Short Term Memory (LSTM)
- Bidirectional RNN
- Deep RNNs

**Part 10: Natural Language Processing & Word Embeddings**

- Word Representation
- Using word embeddings
- Properties of word embeddings
- Embedding matrix
- Learning word embeddings
- Word2Vec
- Negative Sampling
- GloVe word vectors
- Sentiment Classification
- Debiasing word embeddings
- Sequence models & Attention mechanism
- Basic Models
- Picking the most likely sentence
- Beam Search
- Refinements to Beam Search
- Error analysis in beam search
- Bleu Score (optional)
- Attention Model Intuition
- Attention Model
- Speech recognition
- Trigger Word Detection

**Part 11: ML Strategy**

- Why ML Strategy
- Orthogonalization
- Single number evaluation metric
- Satisficing and Optimizing metric
- Train/dev/test distributions
- Size of the dev and test sets
- When to change dev/test sets and metrics
- Why human-level performance?
- Avoidable bias
- Understanding human-level performance
- Surpassing human-level performance
- Improving your model performance
- Carrying out error analysis
- Cleaning up incorrectly labeled data
- Build your first system quickly, then iterate
- Training and testing on different distributions
- Bias and Variance with mismatched data distributions
- Addressing data mismatch
- Transfer learning
- Multi-task learning
- What is end-to-end deep learning?
- Whether to use end-to-end deep learning