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TradingMay 19, 2026· 11 min read· By MLXIO Insights Team

Master Technical Analysis Software to Crush Stock Trading

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Updated on July 8, 2026

Updated (July 2026): This guide has been refreshed to reflect current technical analysis workflows, platform options, automation capabilities, and practical considerations for stock traders using tools such as MetaTrader 5, TradingView, thinkorswim, and broker-based charting platforms.


The Role of Technical Analysis Software in Trading

Choosing the right technical analysis software can improve how you analyze markets, test ideas, and manage trades. Modern platforms go far beyond basic charting: they combine real-time or delayed market data, technical indicators, drawing tools, alerts, screeners, backtesting, and—in some cases—automated trading.

For stock traders, technical analysis software is most useful for answering questions such as:

  • Is the stock trending or range-bound?
  • Where are likely support and resistance levels?
  • Is momentum strengthening or fading?
  • Has volume confirmed a breakout?
  • How would a strategy have performed historically?

Popular platforms in 2026 include TradingView, MetaTrader 5, thinkorswim by Charles Schwab, Interactive Brokers Trader Workstation, TrendSpider, and broker-native tools from firms such as Fidelity, Webull, and TradeStation.

This guide uses MetaTrader 5 as the main example because it remains widely used for charting, indicators, backtesting, and algorithmic trading. However, stock availability in MetaTrader 5 depends on your broker and region. Some brokers offer exchange-listed equities, while others primarily offer CFDs, forex, futures, or synthetic instruments. If you trade U.S. stocks directly, confirm that your broker supports the exact market access you need.

Key benefits of technical analysis software include:

  • Advanced charting with multiple timeframes and layouts
  • Technical indicators such as moving averages, RSI, MACD, VWAP, and Bollinger Bands
  • Alerts for price, volume, indicator, or trendline conditions
  • Backtesting to evaluate trading ideas before risking capital
  • Automation through scripts, bots, or strategy builders
  • Market scanning to identify stocks matching specific setups

Setting Up Your Technical Analysis Software

Getting started requires more than downloading an app. You need reliable data, a clean workspace, and a setup that fits your trading style.

Downloading and Installing MetaTrader 5

MetaTrader 5 is available on desktop, web, and mobile. To get started:

  1. Download the platform from the official MetaTrader 5 website or your broker’s portal.
  2. Install the application on Windows, macOS, iOS, or Android. Some desktop setups on macOS or Linux may require broker-specific builds or compatibility tools.
  3. Connect a broker account to access tradable instruments, live quotes, and order execution.
  4. Open a demo account first if you are testing indicators, scripts, or automated strategies.

Minimum System Requirements

MetaTrader 5 and most charting platforms are not especially demanding, but performance matters if you run many charts, indicators, or automated strategies.

Recommended baseline:

  • Operating system: Current Windows, macOS, iOS, or Android version
  • RAM: 4GB minimum; 8GB+ recommended for active charting
  • Internet: Stable broadband or mobile data connection
  • Storage: Enough space for historical data and platform updates
  • Display: Multiple monitors can help active traders but are not required

Browser-based platforms such as TradingView require an updated browser and stable connection. Desktop platforms are often better for intensive backtesting or automation.

Initial Configuration

After installation:

  • Connect your data source through your broker or platform subscription.
  • Set your watchlists for indexes, sectors, ETFs, and stocks you follow.
  • Customize chart templates with your preferred candles, colors, indicators, and timeframes.
  • Enable alerts for important price levels and indicator conditions.
  • Test order entry in a demo environment before trading live.
Platform Best For Indicator Support Automation Cost
MetaTrader 5 Charting, backtesting, algo trading Yes Yes, via MQL5 Free platform; broker/data costs may apply
TradingView Web charting, alerts, community scripts Yes Limited broker automation; Pine Script for studies/strategies Free and paid tiers
thinkorswim U.S. stocks/options analysis Yes Scripting via thinkScript Broker account required
Interactive Brokers TWS Multi-asset trading Yes API and algo tools Broker account required

Understanding Key Technical Indicators and How to Apply Them

Technical indicators help organize price, volume, trend, and momentum data. They should not be treated as magic signals; they work best as part of a defined process.

  • Moving Averages (MA/EMA): Identify trend direction and dynamic support or resistance.
  • Relative Strength Index (RSI): Measures momentum and potential overbought or oversold conditions.
  • MACD: Tracks trend momentum and moving average convergence/divergence.
  • Bollinger Bands: Show volatility expansion and contraction.
  • Average True Range (ATR): Measures volatility and can help with stop placement.
  • Volume indicators: Confirm whether price moves are supported by participation.

To apply an indicator in MetaTrader 5:

  1. Open a chart for the stock or instrument you want to analyze.
  2. Go to Insert > Indicators.
  3. Choose the indicator category, such as Trend, Oscillators, or Volumes.
  4. Adjust inputs such as period, calculation method, and visual style.
  5. Save the setup as a template if you use it often.

Custom Indicators

If built-in indicators are not enough, MetaTrader 5 supports custom development through MQL5. TradingView uses Pine Script, while thinkorswim uses thinkScript.

Example moving average logic in MQL-style pseudocode:

double sma(int period, double price[]) {
   double sum = 0;
   for(int i = 0; i < period; i++) {
      sum += price[i];
   }
   return sum / period;
}

Custom tools are powerful, but keep them simple and test them thoroughly. Many traders lose accuracy by adding too many indicators that measure the same thing.


Chart Patterns and Trend Analysis Techniques

Chart patterns remain popular because they help traders visualize supply, demand, and market psychology. Technical analysis software makes these patterns easier to identify, annotate, and monitor.

Common Chart Patterns

  • Head and Shoulders: Potential reversal after an uptrend.
  • Inverse Head and Shoulders: Potential reversal after a downtrend.
  • Double Top/Bottom: Possible failed continuation and reversal.
  • Triangles: Consolidation patterns that may precede breakouts.
  • Flags and Pennants: Short-term continuation patterns after strong moves.
  • Cup and Handle: Commonly watched in growth stock breakouts.

Patterns are subjective. A breakout with rising volume, strong relative strength, and favorable market conditions is generally more meaningful than a pattern in isolation.

Trend Analysis Tools in MetaTrader 5

MetaTrader 5 includes drawing tools for:

  • Trendlines
  • Horizontal support and resistance
  • Channels
  • Fibonacci retracements
  • Text labels and chart notes
  • Multiple timeframe layouts
Feature MetaTrader 5 Support
Trendlines Yes
Channels Yes
Fibonacci tools Yes
Multiple charts Yes
Pattern recognition Manual; add-ons may provide automated detection

Using Alerts and Automated Signals to Time Trades

One of the biggest advantages of technical analysis software is the ability to monitor markets without staring at charts all day.

Setting Alerts

MetaTrader 5 supports alerts for price levels and platform events. Other platforms, especially TradingView and TrendSpider, offer highly flexible alerts based on indicators, drawings, trendlines, and custom scripts.

Useful alert types include:

  • Price alerts when a stock reaches support, resistance, or breakout levels
  • Indicator alerts such as RSI crossing 70 or 30
  • Moving average alerts when price crosses a key average
  • Volume alerts when volume exceeds an average threshold
  • Trendline alerts when price breaks a manually drawn level

Alerts are best used to focus your attention—not to replace judgment.

Automated Signals and Copy Trading

MetaTrader 5 supports signal subscriptions and automated trading through Expert Advisors. This allows traders to copy signals or run algorithmic strategies.

Use caution with copy trading. Review:

  • Historical performance across different market regimes
  • Maximum drawdown
  • Trade frequency
  • Average holding period
  • Whether results are from live trading or backtests
  • Fees, slippage, and execution quality
Automation Type MetaTrader 5 Support
Price alerts Yes
Indicator-based alerts Yes, with scripts/add-ons
Signal subscriptions Yes
Automated trading Yes, via MQL5 Expert Advisors
Strategy backtesting Yes

Backtesting Strategies with Historical Data

Backtesting lets you test a strategy against historical price data before trading it live. It is one of the most valuable features of technical analysis software, but it must be used carefully.

Backtesting in MetaTrader 5

MetaTrader 5 includes a built-in Strategy Tester for Expert Advisors. Traders can:

  • Run strategies on historical data
  • Test different timeframes
  • Review trade-by-trade results
  • Optimize parameters
  • Visualize entries and exits
  • Evaluate drawdown, profit factor, and win rate

Why Backtesting Matters

Backtesting helps you:

  • Avoid relying on untested ideas
  • Estimate risk and drawdown
  • Compare strategy variations
  • Identify whether a setup works better in trending or choppy markets
  • Build confidence before trading live

However, backtesting can be misleading if you overfit parameters, ignore commissions, exclude slippage, or test only favorable periods. Always follow with paper trading or small live position sizing before committing meaningful capital.


Integrating Technical Analysis with Fundamental Insights

Technical analysis focuses on price and volume, but stock traders often benefit from combining charts with fundamental and market context.

Practical Integration

Before taking a trade, consider:

  • Upcoming earnings dates
  • Revenue and earnings trends
  • Sector strength or weakness
  • Analyst revisions and guidance changes
  • Broader index direction
  • Interest rate and macroeconomic conditions
  • News catalysts such as product launches, regulation, or litigation

A bullish chart setup in a weak company before earnings carries different risk than the same setup in a leading stock with strong fundamentals and institutional demand.

Advanced Integration

More advanced traders can combine platform tools with external data:

  • Economic calendars
  • Earnings APIs
  • Broker research feeds
  • SEC filings
  • News sentiment tools
  • Python-based analysis
  • AI-assisted screeners and summaries

AI tools have become more common in trading workflows, especially for summarizing news, scanning watchlists, and generating code snippets. They can save time, but outputs should be verified. Do not rely on AI-generated trading signals without independent testing.


Common Mistakes to Avoid When Using Technical Software

Even the best platform cannot fix a weak process. Avoid these common mistakes:

  1. Using too many indicators
    Multiple indicators often repeat the same information. Keep your chart clean.

  2. Ignoring risk management
    Define position size, stop level, and maximum loss before entering.

  3. Overfitting backtests
    A strategy that looks perfect historically may fail live if it was optimized too aggressively.

  4. Trading without market context
    Breakouts fail more often in weak markets. Reversals can be dangerous in strong trends.

  5. Forgetting commissions, spreads, and slippage
    Small costs can turn a high-frequency strategy from profitable to unprofitable.

  6. Relying blindly on signals or bots
    Automation should be monitored. Markets change, and systems can break.

  7. Failing to update software
    Keep platforms, plugins, and mobile apps current for security and stability.


Case Study: Applying Technical Analysis to a Real Trade

Let’s walk through a simple moving average crossover example.

Scenario

You want to analyze a large-cap stock using a trend-following strategy.

Step-by-Step Process

  1. Open MetaTrader 5 and load the stock or stock CFD chart offered by your broker.
  2. Apply two moving averages:
    • Fast EMA: 10 periods
    • Slow EMA: 50 periods
  3. Check the broader trend using the daily and weekly charts.
  4. Add volume analysis to confirm whether buying activity is increasing.
  5. Set alerts for a bullish or bearish crossover.
  6. Backtest the strategy on historical data, including realistic spreads and costs.
  7. Define risk before trading:
    • Entry level
    • Stop-loss level
    • Position size
    • Profit target or trailing exit
  8. Monitor and refine after each trade.

Example crossover logic:

if (FastMA > SlowMA && PrevFastMA <= PrevSlowMA) {
   Alert("Bullish crossover detected");
}

if (FastMA < SlowMA && PrevFastMA >= PrevSlowMA) {
   Alert("Bearish crossover detected");
}

This strategy is simple and easy to test, but it can generate false signals in sideways markets. Adding trend filters, volume confirmation, or market regime rules may improve reliability.


FAQ

Q1: What is the best technical analysis software for beginners in 2026?
A: TradingView is often the easiest for beginners because it is browser-based and intuitive. MetaTrader 5 is strong for backtesting and automation. Broker platforms such as thinkorswim are also excellent for U.S. stock traders.

Q2: Does MetaTrader 5 support automated trading?
A: Yes. MetaTrader 5 supports automated trading through MQL5 Expert Advisors, custom indicators, and scripts.

Q3: Can I use MetaTrader 5 for stock trading?
A: Yes, if your broker offers stocks or stock CFDs through MetaTrader 5. Availability depends on the broker, country, and market.

Q4: How do I backtest a trading strategy in MetaTrader 5?
A: Use the built-in Strategy Tester to run an Expert Advisor on historical data, review performance metrics, and optimize parameters.

Q5: Is technical analysis software free?
A: Many platforms offer free versions, including MetaTrader 5 and TradingView’s basic tier. Real-time exchange data, premium indicators, advanced alerts, and broker commissions may cost extra.

Q6: Can I create custom indicators?
A: Yes. MetaTrader 5 uses MQL5, TradingView uses Pine Script, and thinkorswim uses thinkScript.


Bottom Line

Technical analysis software can make stock trading more structured, data-driven, and efficient. Platforms such as MetaTrader 5, TradingView, thinkorswim, and Interactive Brokers TWS provide advanced charting, indicators, alerts, backtesting, and automation tools.

The software is only part of the process. The best traders combine clean charts, tested strategies, disciplined risk management, and awareness of market context. Start with a simple setup, validate your ideas with backtesting and paper trading, and avoid overcomplicating your analysis. Used correctly, technical analysis software can help you make faster, clearer, and more consistent trading decisions in 2026 and beyond.

Sources & References

Content sourced and verified on May 19, 2026

  1. 1
    4 Best Technical Analysis Tools For Traders (2026)

    https://www.newtrading.io/technical-analysis-tools-software/

  2. 2
    atlassian/jira-software - Docker Image

    https://hub.docker.com/r/atlassian/jira-software

  3. 3
    GitHub - abusufyanvu/6S191_MIT_DeepLearning: MIT Introduction to Deep Learning (6.S191) Instructors: Alexander Amini and Ava Soleimany Course Information Summary Prerequisites Schedule Lectures Labs, Final Projects, Grading, and Prizes Software labs Gather.Town lab + Office Hour sessions Final project Paper Review Project Proposal Presentation Project Proposal Grading Rubric Past Project Proposal Ideas Awards + Categories Important Links and Emails Course Information Summary MIT's introductory course on deep learning methods with applications to computer vision, natural language processing, biology, and more! Students will gain foundational knowledge of deep learning algorithms and get practical experience in building neural networks in TensorFlow. Course concludes with a project proposal competition with feedback from staff and a panel of industry sponsors. Prerequisites We expect basic knowledge of calculus (e.g., taking derivatives), linear algebra (e.g., matrix multiplication), and probability (e.g., Bayes theorem) -- we'll try to explain everything else along the way! Experience in Python is helpful but not necessary. This class is taught during MIT's IAP term by current MIT PhD researchers. Listeners are welcome! Schedule Monday Jan 18, 2021 Lecture: Introduction to Deep Learning and NNs Lab: Lab 1A Tensorflow and building NNs from scratch Tuesday Jan 19, 2021 Lecture: Deep Sequence Modelling Lab: Lab 1B Music Generation using RNNs Wednesday Jan 20, 2021 Lecture: Deep Computer Vision Lab: Lab 2A Image classification and detection Thursday Jan 21, 2021 Lecture: Deep Generative Modelling Lab: Lab 2B Debiasing facial recognition systems Friday Jan 22, 2021 Lecture: Deep Reinforcement Learning Lab: Lab 3 pixel-to-control planning Monday Jan 25, 2021 Lecture: Limitations and New Frontiers Lab: Lab 3 continued Tuesday Jan 26, 2021 Lecture (part 1): Evidential Deep Learning Lecture (part 2): Bias and Fairness Lab: Work on final assignments Lab competition entries due at 11:59pm ET on Canvas! Lab 1, Lab 2, and Lab 3 Wednesday Jan 27, 2021 Lecture (part 1): Nigel Duffy, Ernst & Young Lecture (part 2): Kate Saenko, Boston University and MIT-IBM Watson AI Lab Lab: Work on final assignments Assignments due: Sign up for Final Project Competition Thursday Jan 28, 2021 Lecture (part 1): Sanja Fidler, U. Toronto, Vector Institute, and NVIDIA Lecture (part 2): Katherine Chou, Google Lab: Work on final assignments Assignments due: 1 page paper review (if applicable) Friday Jan 29, 2021 Lecture: Student project pitch competition Lab: Awards ceremony and prize giveaway Assignments due: Project proposals (if applicable) Lectures Lectures will be held starting at 1:00pm ET from Jan 18 - Jan 29 2021, Monday through Friday, virtually through Zoom. Current MIT students, faculty, postdocs, researchers, staff, etc. will be able to access the lectures during this two week period, synchronously or asynchronously, via the MIT Canvas course webpage (MIT internal only). Lecture recordings will be uploaded to the Canvas as soon as possible; students are not required to attend any lectures synchronously. Please see the Canvas for details on Zoom links. The public edition of the course will only be made available after completion of the MIT course. Labs, Final Projects, Grading, and Prizes Course will be graded during MIT IAP for 6 units under P/D/F grading. Receiving a passing grade requires completion of each software lab project (through honor code, with submission required to enter lab competitions), a final project proposal/presentation or written review of a deep learning paper (submission required), and attendance/lecture viewing (through honor code). Submission of a written report or presentation of a project proposal will ensure a passing grade. MIT students will be eligible for prizes and awards as part of the class competitions. There will be two parts to the competitions: (1) software labs and (2) final projects. More information is provided below. Winners will be announced on the last day of class, with thousands of dollars of prizes being given away! Software labs There are three TensorFlow software lab exercises for the course, designed as iPython notebooks hosted in Google Colab. Software labs can be found on GitHub: https://github.com/aamini/introtodeeplearning. These are self-paced exercises and are designed to help you gain practical experience implementing neural networks in TensorFlow. For registered MIT students, submission of lab materials is not necessary to get credit for the course or to pass the course. At the end of each software lab there will be task-associated materials to submit (along with instructions) for entry into the competitions, open to MIT students and affiliates during the IAP offering. This includes MIT students/affiliates who are taking the class as listeners -- you are eligible! These instructions are provided at the end of each of the labs. Completing these tasks and submitting your materials to Canvas will enter you into a per-lab competition. MIT students and affiliates will be eligible for prizes during the IAP offering; at the end of the course, prize-winners will be awarded with their prizes. All competition submissions are due on January 26 at 11:59pm ET to Canvas. For the software lab competitions, submissions will be judged on the basis of the following criteria: Strength and quality of final results (lab dependent) Soundness of implementation and approach Thoroughness and quality of provided descriptions and figures Gather.Town lab + Office Hour sessions After each day’s lecture, there will be open Office Hours in the class GatherTown, up until 3pm ET. An MIT email is required to log in and join the GatherTown. During these sessions, there will not be a walk through or dictation of the labs; the labs are designed to be self-paced and to be worked on on your own time. The GatherTown sessions will be hosted by course staff and are held so you can: Ask questions on course lectures, labs, logistics, project, or anything else; Work on the labs in the presence of classmates/TAs/instructors; Meet classmates to find groups for the final project; Group work time for the final project; Bring the class community together. Final project To satisfy the final project requirement for this course, students will have two options: (1) write a 1 page paper review (single-spaced) on a recent deep learning paper of your choice or (2) participate and present in the project proposal pitch competition. The 1 page paper review option is straightforward, we propose some papers within this document to help you get started, and you can satisfy a passing grade with this option -- you will not be eligible for the grand prizes. On the other hand, participation in the project proposal pitch competition will equivalently satisfy your course requirements but additionally make you eligible for the grand prizes. See the section below for more details and requirements for each of these options. Paper Review Students may satisfy the final project requirement by reading and reviewing a recent deep learning paper of their choosing. In the written review, students should provide both: 1) a description of the problem, technical approach, and results of the paper; 2) critical analysis and exposition of the limitations of the work and opportunities for future work. Reviews should be submitted on Canvas by Thursday Jan 28, 2021, 11:59:59pm Eastern Time (ET). Just a few paper options to consider... https://papers.nips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf https://papers.nips.cc/paper/2018/file/69386f6bb1dfed68692a24c8686939b9-Paper.pdf https://papers.nips.cc/paper/2020/file/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf https://science.sciencemag.org/content/362/6419/1140 https://papers.nips.cc/paper/2018/file/0e64a7b00c83e3d22ce6b3acf2c582b6-Paper.pdf https://arxiv.org/pdf/1906.11829.pdf https://www.nature.com/articles/s42256-020-00237-3 https://pubmed.ncbi.nlm.nih.gov/32084340/ Project Proposal Presentation Keyword: proposal This is a 2 week course so we do not require results or working implementations! However, to win the top prizes, nice, clear results and implementations will demonstrate feasibility of your proposal which is something we look for! Logistics -- please read! You must sign up to present before 11:59:59pm Eastern Time (ET) on Wednesday Jan 27, 2021 Slides must be in a Google Slide before 11:59:59pm Eastern Time (ET) on Thursday Jan 28, 2021 Project groups can be between 1 and 5 people Listeners welcome To be eligible for a prize you must have at least 1 registered MIT student in your group Each participant will only be allowed to be in one group and present one project pitch Synchronous attendance on 1/29/21 is required to make the project pitch! 3 min presentation on your idea (we will be very strict with the time limits) Prizes! (see below) Sign up to Present here: by 11:59pm ET on Wednesday Jan 27 Once you sign up, make your slide in the following Google Slides; submit by midnight on Thursday Jan 28. Please specify the project group # on your slides!!! Things to Consider This doesn’t have to be a new deep learning method. It can just be an interesting application that you apply some existing deep learning method to. What problem are you solving? Are there use cases/applications? Why do you think deep learning methods might be suited to this task? How have people done it before? Is it a new task? If so, what are similar tasks that people have worked on? In what aspects have they succeeded or failed? What is your method of solving this problem? What type of model + architecture would you use? Why? What is the data for this task? Do you need to make a dataset or is there one publicly available? What are the characteristics of the data? Is it sparse, messy, imbalanced? How would you deal with that? Project Proposal Grading Rubric Project proposals will be evaluated by a panel of judges on the basis of the following three criteria: 1) novelty and impact; 2) technical soundness, feasibility, and organization, including quality of any presented results; 3) clarity and presentation. Each judge will award a score from 1 (lowest) to 5 (highest) for each of the criteria; the average score from each judge across these criteria will then be averaged with that of the other judges to provide the final score. The proposals with the highest final scores will be selected for prizes. Here are the guidelines for the criteria: Novelty and impact: encompasses the potential impact of the project idea, its novelty with respect to existing approaches. Why does the proposed work matter? What problem(s) does it solve? Why are these problems important? Technical soundness, feasibility, and organization: encompasses all technical aspects of the proposal. Do the proposed methodology and architecture make sense? Is the architecture the best suited for the proposed problem? Is deep learning the best approach for the problem? How realistic is it to implement the idea? Was there any implementation of the method? If results and data are presented, we will evaluate the strength of the results/data. Clarity and presentation: encompasses the delivery and quality of the presentation itself. Is the talk well organized? Are the slides aesthetically compelling? Is there a clear, well-delivered narrative? Are the problem and proposed method clearly presented? Past Project Proposal Ideas Recipe Generation with RNNs Can we compress videos with CNN + RNN? Music Generation with RNNs Style Transfer Applied to X GAN’s on a new modality Summarizing text/news articles Combining news articles about similar events Code or spec generation Multimodal speech → handwriting Generate handwriting based on keywords (i.e. cursive, slanted, neat) Predicting stock market trends Show language learners articles or videos at their level Transfer of writing style Chemical Synthesis with Recurrent Neural networks Transfer learning to learn something in a domain for which it’s hard or risky to gather data or do training RNNs to model some type of time series data Computer vision to coach sports players Computer vision system for safety brakes or warnings Use IBM Watson API to get the sentiment of your Facebook newsfeed Deep learning webcam to give wifi-access to friends or improve video chat in some way Domain-specific chatbot to help you perform a specific task Detect whether a signature is fraudulent Awards + Categories Final Project Awards: 1x NVIDIA RTX 3080 4x Google Home Max 3x Display Monitors Software Lab Awards: Bose headphones (Lab 1) Display monitor (Lab 2) Bebop drone (Lab 3) Important Links and Emails Course website: http://introtodeeplearning.com Course staff: [email protected] Piazza forum (MIT only): https://piazza.com/mit/spring2021/6s191 Canvas (MIT only): https://canvas.mit.edu/courses/8291 Software lab repository: https://github.com/aamini/introtodeeplearning Lab/office hour sessions (MIT only): https://gather.town/app/56toTnlBrsKCyFgj/MITDeepLearning

    https://github.com/abusufyanvu/6S191_MIT_DeepLearning

  4. 4
    Installing basic software - Learn web development | MDN

    https://developer.mozilla.org/en-US/docs/Learn_web_development/Getting_started/Environment_setup/Installing_software

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