Deep learning is part of a broader family of machine learning methods based on artificial neural networks. Machine learning is the study of algorithms and mathematical models that computer systems use to progressively improve their performance on a specific task. Machine learning algorithms build a mathematical model of sample data, known as “training data”, in order to make predictions or decisions without being explicitly programmed to perform the task.The types of machine learning algorithms differ in their approach, the type of data they input and output, and the type of task or problem that they are intended to solve.Supervised learning algorithms build a mathematical model of a set of data that contains both the inputs and the desired outputs.Unsupervised learning algorithms take a set of data that contains only inputs, and find structure in the data, like grouping or clustering of data points. The algorithms therefore learn from test data that has not been labeled, classified or categorized.
Ninety percent of all the world’s data has been created in the last two years. Business around the world generate approximately 450 billion transactions a day. The amount of data collected by all organizations is approximately 2.5 exabytes a day. That number doubles every month.That means,there is always exponential need for Machine learning engineers to build, implement, and maintain machine learning systems,algorithms in technology products with focus on machine learning system reliability, performance, and scalability.
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- #1.Practical Deep Learning with PyTorch
- #2.Practical Deep Learning with Keras and Python
- #3.Natural Language Processing(NLP) with Deep Learning in Keras
- #4.Deep Learning A-Z: Hands-On Artificial Neural Networks
- #5.Deep Learning Prerequisites: Linear Regression in Python
- #6.Deep Learning Prerequisites: Logistic Regression in Python
- #7.Modern Deep Learning in Python
- #8.Natural Language Processing with Deep Learning in Python
- #10.Deep Learning and Computer Vision A-Z: OpenCV, SSD & GANs
- #12.Zero to Deep Learning with Python and Keras
- #13.Deep Learning, Neuronale Netze & AI: Der Komplettkurs
- #14.Complete Guide to TensorFlow for Deep Learning with Python
- #16.Machine Learning, Data Science and Deep Learning with Python
- #17.Master Computer Vision OpenCV4 in Python with Deep Learning
- #18.The Complete Self-Driving Car Course – Applied Deep Learning
- #19.Deep Learning verstehen: Entwickle Neuronale Netze in Python
- #20.Deep Learning, Neuronale Netze und TensorFlow in Python
- #21.PyTorch for Deep Learning and Computer Vision
- #22.Deep Learning e Reti Neurali con Python: il Corso Pratico
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Accelerate your deep learning with PyTorch covering all the fundamentals of deep learning with a python-first framework.
- You need to know basic python such as lists, dictionaries, loops, functions and classes
- You need to know basic differentiation
- You need to know basic algebra
Ratings : 4.4 (1,013 ratings)
Learn to apply machine learning to your problems. Follow a complete pipeline including pre-processing and training.
- You should be able to use Python (if, while, lists. Everything else will be covered in the course)
- NO prior knowledge of machine learning is assumed
Ratings : 4.4 (246 ratings)
Word2Vec, Glove, FastText, Universal Sentence Encoder, GRU, LSTM, Conv-1D, Seq2Seq, Machine Translation and much more!
- Machine Learning, NLP basics, Linear Algebra, Python, Tensor Flow, Keras
Ratings : 4.2 (24 ratings)
Learn to create Deep Learning Algorithms in Python from two Machine Learning & Data Science experts. Templates included.
- High school mathematics level
- Basic Python programming knowledge
Ratings : 4.5 (21,089 ratings)
Data science: Learn linear regression from scratch and build your own working program in Python for data analysis.
- How to take a derivative using calculus
- Basic Python programming
- For the advanced section of the course, you will need to know probability
- For the advanced section of the course, you will need to know the Gaussian distribution
Ratings : 4.6 (3,058 ratings)
Data science techniques for professionals and students – learn the theory behind logistic regression and code in Python
- You should know how to take a derivative
- You should know some basic Python coding
- Install numpy and matplotlib
Ratings :4.6 (2,207 ratings)
Build with modern libraries like Tensorflow, Theano, Keras, PyTorch, CNTK, MXNet. Train faster with GPU on AWS.
- Be comfortable with Python, Numpy, and Matplotlib. Install Theano and TensorFlow.
- If you do not yet know about gradient descent, backprop, and softmax, take my earlier course, deep learning in Python, and then return to this course.
Ratings : 4.6 (1,629 ratings)
Complete guide on deriving and implementing word2vec, GloVe, word embeddings, and sentiment analysis with recursive nets.
- Install Numpy, Matplotlib, Sci-Kit Learn, Theano, and TensorFlow (should be extremely easy by now)
- Understand backpropagation and gradient descent, be able to derive and code the equations on your own
- Code a recurrent neural network from basic primitives in Theano (or Tensorflow), especially the scan function
- Code a feedforward neural network in Theano (or Tensorflow)
- Helpful to have experience with tree algorithms
Ratings : 4.6 (3,109 ratings)
The most in-depth course on recommendation systems with deep learning, machine learning, data science, and AI techniques
- For earlier sections, just know some basic arithmetic
- For advanced sections, know calculus, linear algebra, and probability for a deeper understanding
- Be proficient in Python and the Numpy stack (see my free course)
- For the deep learning section, know the basics of using Keras
Ratings : 4.7 (468 ratings)
Become a Wizard of all the latest Computer Vision tools that exist out there. Detect anything and create powerful apps.
- Only High School Maths
- Basic Python programming knowledge
Ratings : 4.4 (2,981 ratings)
Apprenez crer des algorithmes de Deep Learning en Python par des experts en Machine Learning & Data science.
- Seulement un niveau mathématique de niveau lycée
Ratings : 4.4 (1,147 ratings)
Understand and build Deep Learning models for images, text and more using Python and Keras
- Knowledge of Python, familiarity with control flow (if/else, for loops) and pythonic constructs (functions, classes, iterables, generators)
- Use of bash shell (or equivalent command prompt) and basic commands to copy and move files
- Basic knowledge of linear algebra (what is a vector, what is a matrix, how to calculate dot product)
- Use of ssh to connect to a cloud computer
Ratings : 4.3 (1,868 ratings)
Deep Learning & Artificial Intelligence in Python: Bitcoin-Preise voraussagen, AI entwickeln – in Keras & Tensorflow
- You should have already programmed a bit something
- You should have come along well in math at school earlier (even if you’ve forgotten everything by now)
Ratings : 4.6 (502 ratings)
Learn how to use Google’s Deep Learning Framework – TensorFlow with Python! Solve problems with cutting edge techniques!
- Some knowledge of programming (preferably Python)
- Some basic knowledge of math (mean, standard deviation, etc..)
Ratings : 4.5 (10,659 ratings)
Learn the latest techniques in computer vision with Python, OpenCV, and Deep Learning!
- Must have clear understanding of Python Basics
- Windows 10 or MacOS or Ubuntu
- Must have Install Permissions on Computer
- WebCam if you want to learn the video streaming content
Ratings : 4.5 (1,032 ratings)
Complete hands-on machine learning tutorial with data science, Tensorflow, artificial intelligence, and neural networks
- You’ll need a desktop computer (Windows, Mac, or Linux) capable of running Enthought Canopy 1.6.2 or newer. The course will walk you through installing the necessary free software.
- Some prior coding or scripting experience is required.
- At least high school level math skills will be required.
- This course walks through getting set up on a Microsoft Windows based desktop PC. While the code in this course will run on other operating systems, we cannot provide OS-specific support for them
Ratings : 4.5 (14,328 ratings)
Learn OpenCV4, Dlib, Keras, TensorFlow & Caffe while completing over 21 projects such as classifiers, detectors & more!
- Little to no programming knowledge is needed, but basic programing knowledge will help
- Windows 10 or Ubuntu or a MacOS system
- A webcam to implement some of the mini projects
Ratings : 4.2 (2,487 ratings)
Learn to use Deep Learning, Computer Vision and Machine Learning techniques to Build an Autonomous Car with Python
- A working computer
- No experience required!
Ratings : 4.6 (1,044 ratings)
Schritt fr Schritt entwickelst du dein eigenes neuronales Netz bis hin zur Bilderkennung. Komplett am Beispiel!
- You should have already programmed something
- You should have come along well in math at school (even if you’ve forgotten everything by now)
Ratings : 4.5 (381 ratings)
Beherrsche das Deep Learning fr eigene Neuronale Netzwerke. Nutze Python, TensorFlow und Keras um Probleme zu lsen.
- Basic programming skills are helpful.
Ratings : 4.5 (635 ratings)
Build Highly Sophisticated Deep Learning and Computer Vision Applications with PyTorch
- No experience is required
Ratings : 4.6 (250 ratings)
Apprendi i segreti del Deep Learning e impara a creare le tue Reti Neurali Artificiali con Python, Keras e Tensorflow.
- Basi di matematica da scuola superiore
- Conoscere un qualsiasi linguaggio di programmazione può aiutare, ma non è indispensabile, è presente una sezione su Python per principianti assoluti
Ratings : 4.5 (72 ratings)
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