Deep Learning Odyssey Track - Online Learning with Certificate

Deep Learning Odyssey

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Start with the core principles of how neural networks work and progress to building and training your own models using popular frameworks like TensorF…

124

Chapters

Learning hours

177

Assignments

AI Assistant

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What you will learn

Clean and prepare data for machine learning

Understand neural network concepts

Explore PyTorch for neural network training

Transform raw data into usable features

Build neural networks with scikit-learn

Apply CNNs for image classification tasks

Engineer features to improve model performance

Implement neural networks using TensorFlow

Use RNNs for sequence and time series data

Learning track content

Master the foundational steps for cleaning and preparing raw data for analysis and machine learning.

Understanding Data Quality

Handling Missing Values

Dealing with Duplicates and Outliers

Challenge: Data Cleaning

Transform raw data into usable features for machine learning models.

Scaling and Normalization

Encoding Categorical Variables

Feature Transformation and Extraction

Challenge: Transformation Grid

Engineer and select features to improve model performance and interpretability.

Feature Selection Basics

Creating Interaction Features

Automating Preprocessing with Pipelines

Challenge: Preprocessing Pipeline

First, we will discuss what a neural network is and how it works. And also consider the scope of its application.

What is a Neural Network?

Applications of Deep Learning in the Real World

Neural Networks or Traditional Models

Neural Network Structure

What is a Neuron?

Activation Functions

Forward and Backward Propagation

Next, we will try to build our own neural network and see how efficiently it copes with learning. We will also consider a ready-made solution from the scikit-learn library.

Single Neuron Implementation

Challenge: Creating a Neuron

Perceptron Layers

Challenge: Creating a Perceptron

Forward Propagation

Loss Function

Backward Propagation

Backpropagation Implementation

Model Training

Challenge: Training the Perceptron

Model Evaluation

Challenge: Evaluating the Perceptron

Neural Network with scikit-learn

Finally, we will give you some additional useful information on how to understand which model to use and what types of neural networks there are. To complete the course, you will be tested on your acquired knowledge.

Other types of Neural Networks

Hyperparameter Tuning

Challenge: Automatic Hyperparameter Tuning

Quiz: Neural Networks Essentials

Summary

You will gain a foundational understanding of TensorFlow's primary components - Tensors. You'll delve into the nature and applications of tensors, familiarize yourself with tensor properties, and acquire knowledge in essential mathematical operations.

Welcome to TensorFlow

Introduction to Tensors

Tensor Properties

Applications of Tensors

You'll learn how TensorFlow operates and the ways to improve its performance. By the end of this module, you'll be well-equipped to implement basic neural networks or other tensor calculations, using only the TensorFlow library without any extras.

Creating a Neural Network Layer

Gradient Tape

Graph Execution

Neural Network Implementation

Summary

Explore the fundamentals of PyTorch, focusing on tensors—the core data structure used for computations. You will learn about tensor creation, random initialization, mathematical operations, and shape manipulation.

What is PyTorch?

Introduction to Tensors

Tensor Creation Functions

Challenge: Initializing Tensors

Creating Random Tensors

Challenge: Initializing Model Weights and Biases

Mathematical Operations with Tensors

Challenge: Performing Mathematical Operations

Shapes and Dimensions in PyTorch

Challenge: Reshaping Tensors

Explore key concepts for training models in PyTorch, including computing gradients and performing multi-step backpropagation. Master linear regression as a foundational machine learning model and introduces handling datasets efficiently.

Gradients in PyTorch

Multi-Step Backpropagation

Linear Regression

Challenge: Implementing Linear Regression

Working with Datasets

Challenge: Preparing the Iris Dataset

Discover how to build, train, and evaluate neural networks using PyTorch. You will learn how to define a simple feedforward neural network, optimize its parameters through training, and assess its performance.

Creation of a Simple Neural Network

Training of the Model

Evaluation of the Model

Challenge: Classifying Flowers

Computer vision enables machines to interpret and analyze visual data, mimicking human perception. This section covers the basics of image representation, color models, and mathematical foundations essential for understanding how computers process images. You'll explore real-world applications, from autonomous vehicles to medical imaging, and learn how Computer vision integrates with AI and machine learning.

What Is Computer Vision?

Fundamentals of Image Processing

Linear Algebra for Image Manipulation

OpenCV is a powerful library for image manipulation and computer vision tasks. This section covers essential techniques like image filtering, transformations, edge detection, and segmentation. You'll learn how to perform blurring, thresholding, contour detection, and feature extraction to enhance and analyze images efficiently.

Basic Transformations

Fourier Transform

Low-pass and High-pass Filters

Noise Reduction and Smoothing

Histogram Equalization

Super-Resolution Techniques

Edge Detection

Corner and Blob Detection

CNNs process visual data using convolution, pooling, and activation layers to extract features for tasks like image classification and object detection. Key components include padding, convolution for feature extraction, pooling for complexity reduction, and activation for non-linearity. Popular architectures like AlexNet, VGG, and ResNet power AI in healthcare, autonomy, and security.

Introduction to Convolutional Neural Networks

Overview of Popular CNN Models

Challenge: Building a CNN

Object detection is a fundamental task in computer vision that involves identifying and localizing objects within an image. Unlike image classification, which assigns a single label to an entire image, object detection not only classifies objects but also determines their positions using bounding boxes. This section covers key techniques and algorithms used in object detection, ranging from traditional methods to deep learning-based approaches like YOLO and U-Net.

Object Localization

Object Detection

Bounding Box Predictions

Intersection Over Union (IoU) and Evaluation Metrics

Non-Max Suppression (NMS)

Anchor Boxes

YOLO Model Overview

Challenge: Object Detection with Custom Model and YOLO

Computer vision has significantly advanced over the years, shifting from basic image processing methods to complex deep learning techniques. This section delves into the latest innovations in computer vision, focusing on transfer learning, facial recognition, and image generation. We will explore the benefits of pre-trained models on performance, the principles of facial recognition technology, and the way AI creates images through deep learning.

Transfer Learning in Computer Vision

Overview of Face Recognition

Overview of Image Generation

Covers the limitations of traditional neural networks for sequential data and introduces the fundamentals of Recurrent Neural Networks. Explains RNN architecture, types, and step-by-step implementation through basic examples and a coding challenge.

Shortcomings of ANNs

How RNN Works?

Types of RNNs

Implementing a Basic RNN

Challenge: Implementing the Basic RNN

Explores common training challenges such as vanishing and exploding gradients. Introduces advanced RNN variants including LSTM and GRU, highlighting their internal mechanisms and use cases, with practical implementation examples for each.

Vanishing and Exploding Gradients

Sigmoid and Tanh Activations

Long Short-Term Memory (LSTM) Networks

Example of LSTM

Gated Recurrent Units (GRU)

When to Use LSTM and GRU

Focuses on processing and forecasting time series data using RNN-based models. Includes data loading, preprocessing techniques, model training, and performance evaluation, with emphasis on comparing LSTM and GRU architectures.

Introduction to Time Series Forecasting

Loading the Data

Preprocessing Time Series Data

Training and Evaluating an RNN

LSTM vs GRU for Time Series

Challenge: Time Series Forecasting with LSTM

Demonstrates the application of RNNs to text classification tasks. Covers core NLP concepts, text encoding methods, data preparation steps, and construction of an LSTM-based model for sentiment prediction.

NLP Basics

Text Encoding

Loading and Preprocessing the Data

Sentiment Analysis Model

Challenge: Building an LSTM for Sentiment Analysis

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Web Development with C#

SQL from Zero to Hero

С++ Essentials

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Why People Choose Codefinity

Trusted by Employees of Leading Companies and Students from Top Universities

Full catalog access

Try Codefinity with confidence – 30-day money-back guarantee included

Instantly access 500+ courses

Trusted by Employees of Leading Companies
and Students from Top Universities